Dr. Michael Mascagni

Curriculum Vitae

| Biographical Information | Research and Creative Activity | Teaching and Training | Service |


Biographical Information

Name: Michael V. A. Mascagni

Birth Date: on request

Birthplace: Bologna, Italy with given name Michele V. A. Mascagni

Citizenship: United States of America, Repubblica Italiana

Postal Contact Information:

Department of Computer Science School of Computational Science
Florida State University Florida State University
253 Love Building 400 Dirac Science Library
Tallahassee, FL 32306-4530  USA Tallahassee, FL 32306-4120  USA






Electronic Contact Information:

Office Telephone: +1.850.644.3290
Facsimile: +1.850.644.0098
E-mail: mascagni@fsu.edu or mascagni@math.ethz.ch
Homepage:  http://www.cs.fsu.edu/~mascagni



 

 

Academic Degrees:

Ph.D., Mathematics, October, 1987
Courant Institute of Mathematical Sciences, New York University, New York, NY; Dissertation Title: Negative Feedback in Neural Networks; Prof. Charles Peskin, Major Professor
M.S., Mathematics, October 1984
Courant Institute of Mathematical Sciences, New York University
B.S., Mathematics, with Highest Distinction, December 1981
University of Iowa, Iowa City, IA; Minor in Biological Sciences (formerly Zoology)
B.S.E., Biomedical Engineering, with Highest Distinction, May 1981
University of Iowa, Iowa City, IA

Awards:

2008
Fulbright Senior Specialist Roster, Council for International Exchange of Scholars, Washington, DC
2001
Developing Scholar (Associate Professor Research) Award, Florida State University, Tallahassee, FL
1988-1989
National Academy of Sciences/National Research Council Postdoctoral Fellowship used at Mathematical Research Branch, N.I.D.D.K., National Institutes of Health, Bethesda, MD

Academic Positions:

Summer 2007
Visiting Professor: Université de Toulon et du Var, Institut des Sciences de l'Ingénieur de Toulon et du Var, Modélisation Numérique et Couplages, Toulon, France; Prof. Sylvain Maire, Sponsor
Wintersemester 2005-06 - Sommersemeter 2006
Gastprofessor, Seminar für Angewandte Mathematik, Departement Mathematik, Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Zürich, Switzerland; Prof. Dr. Rolf Jeltsch, Seminar Head, Prof. Dr. Wesley Petersen, Academic Host
2002-Present
Professor, Department of Computer Science, Florida State University, Tallahassee, FL; Dr. David Whalley, Chair
Sommersemeter 2002
Gastprofessor, Institut für Scientific Computing, Universität Salzburg, Salzburg, Austria; Prof. Dr. Peter Zinterhof, Chair
2001-Present
Professor, Department of Chemical and Biomedical Engineering, Florida State University, Tallahassee, FL; Dr. Michael H. Peters, Chair (Courtesy)
1999-Present
Professor, Department of Mathematics, Florida State University, Tallahassee, FL; Dr. Phillip Bowers, Chair (Courtesy)
1999-Present
Faculty Affiliate, School of Computational Science, Florida State University, Tallahassee, FL; Dr. Max Gunzburger, Director
1999-2002
Associate Professor, Department of Computer Science, Florida State University, Tallahassee, FL; Dr. Ted Baker, Chair
1997-1999
Director, University of Southern Mississippi/Center of Higher Learning Naval Oceanographic Office/Programming Environment and Training Research Program, Stennis Space Center, MS; Dr. Peter Ranelli, Technical Director, Center of Higher Learning
1997-1999
Coordinator, Ph.D. Program in Scientific Computing and Associate Professor of Mathematics: University of Southern Mississippi, Hattiesburg, MS; Dr. Grayson Rayborn, Director, School of Mathematical Sciences
1997-1999
Associate Professor, Department of Mathematics, University of Southern Mississippi, Hattiesburg, MS; Dr. Wallace Pye, Chairman
May 1996
Visiting Professor: Dipartimento di Metodi e Modelli Matematici per le Scienze Applicate (DMMMSA), Università degli Studi di Padova, Padova, Italy; Prof. Renato Spigler, Sponsor
1994-1995
Adjunct Professor: Georgetown University Department of Computer Science, Washington, D.C.; Dr. Timothy Law Snyder, Chair
1987
Adjunct Professor: Dept. of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY
1981-1983
Graduate Fellow in Biophysics; The Rockefeller University, New York, NY; Dr. Robert Shapley, Advisor

Other Professional Positions:

1989-1996
Research Staff Member: Center for Computing Sciences (formerly Supercomputing Research Center), Institute for Defense Analyses, Bowie, MD; Dr. Francis Sullivan, Director
1987-1996
NIH-NRC Research Associate/Guest Worker: Mathematical Research Branch, NIDDK, National Institutes of Health, Bethesda, MD; Dr. John Rinzel, Advisor
1996
Visiting Researcher: National Institute of Standards and Technology, Gaithersburg, MD; Dr. Judy Devaney, Sponsor
Summer 1995
Visiting Scientist: Research Institute for Advanced Computer Science (RIACS), NASA Ames Research Center, Moffett Field, CA; Dr. Robert Schreiber, Sponsor
Summer 1984
Courant Institute/IBM Summer Student: Department of Mathematical Sciences, IBM T. J. Watson Research Center, Yorktown Heights, NY; Dr. Willard Miranker, Advisor
Summer 1983
Summer Student in Numerical Weather Prediction: NASA Goddard Space Flight Center, Greenbelt, MD; Dr. Eugenia Kalnay and Mr. Dean Duffy, Advisors
 
Professional Society Memberships:
 
Association of Computing Machinery (ACM)
International Association for Mathematics and Computers in Simulation (IMACS)
Society of Industrial and Applied Mathematics (SIAM)
Society of Industrial and Applied Mathematics, Supercomputing (SC) Activity Group
Society of Industrial and Applied Mathematics, Computational Science and Engineering (CSE) Activity Group
The Speedup Society, The Swiss Forum for Grid and High-Performance Computing

Honor Society Memberships:

Phi Beta Kappa (National Liberal Arts Honor Society), Iowa Alpha Chapter
Tau Beta Pi (National Engineering Honor Society), Iowa Beta Chapter

Other Honors:

2005-Present
          Member, Board of Directors, International Association for Mathematics and Computers in Simulation (IMACS)
2005-Present
          Member, Technical Committee on Monte Carlo Methods, International Association for Mathematics and Computers in Simulation (IMACS)
2004-Present
          Marquis Who's Who, Who's Who in Computational Science and Engineering
1999-Present

          Society of Industrial and Applied Mathematics (SIAM) Visiting Lecturer
1986-1987
         
New York University, College of Arts and Science, New York, NY, Dean's Dissertation Fellowship
1983-1984
         
New York University, Courant Institute of Mathematical Sciences, New York,  NY, Computational Fluid Dynamics Fellowship


Research and Creative Activity

Research Interests:

Refereed Chapters in Edited Volumes:

  1. Y. Li and M. Mascagni (2006), "An Overview of Grid-Based Monte Carlo Computing," Grid Technologies, Emerging from Distributed Architectures to Virtual Organizations, WIT Press, ISBN: 978-1-84564-055-2, M. P. Bekakos, G. A. Gravvanis and H. R. Arabnia, editors, pp. 391-421.  This paper provides an overview of computational infrastructure for parallel, distributed, and Grid-based Monte Carlo computations.  The starting point is the Scalable Parallel Random Number Generators (SPRNG) Library, and its uses for parallel and distributed Monte Carlo, and the discussion continues with a description of our Grid-Computing Infrastructure for Monte Carlo Applications (GCIMCA), and an extension of this point-of-view to workflows.  The paper then continues with consideration of quasi-Monte Carlo and the differences that arise in computing in this manner on the Grid with quasirandom numbers.  The work concludes with a summary and many open problems.
  2. C.-O. Hwang, J. A. Given, and M. Mascagni (2004), "First- and Last-Passage Algorithms for Diffusion Monte Carlo," New Vistas in Statistical Physics: Applications in Econophysics, Bioinformatics, and Pattern Recognition, L. T. Wille, editor, Springer Verlag: Berlin/New York, pp.  47-65.  This invited review paper summarizes first- and last-passage methods developed by our research group for solving problems in electrostatics, material science, and biochemistry.
  3. C.-O. Hwang, M. Mascagni and N. A. Simonov (2003), Monte Carlo Methods for the Linearized Poisson-Boltzmann Equation, Advances in Numerical Analysis, Nova Science Publishers, Inc., Hauppauge, NY, 20 pages.  This paper reviews several methods for the solution of the linear Poisson-Boltzmann equation via Monte Carlo methods.  In addition, the effectiveness of the various methods are illustrated on several examples.  Finally, one of the methods is applied to a complex application where the solution is used in a biochemical setting.  The Poisson-Boltzmann equation is becoming more important in applications where biomolecules are studied in solution.
  4. M. Mascagni (2003), "Random Number Generation," in CRC Standard Mathematical Tables and Formulae 31st Edition, D. Zwillinger, editor, Chapman and Hall/CRC, Boca Raton, pp. 644-649.  This invited chapter gives a review of the use of pseudorandom numbers to produce uniform real and integer variables and how to transform them into nonuniform distribution.  The volume where this chapter appears is a widely used reference for Mathematics and computational technique.
  5. M. Mascagni (2003), "Deterministic Monte Carlo Methods and Parallelism," Sourcebook on Parallel Computing, J. Dongarra, I. Foster, F. Fox, W. Gropp, K. Kennedy, L. Torcson, and A. White, editors, Morgan Kaufman Publishers, San Francisco, pp. 249-258.  This invited review of parallel quasi-Monte Carlo methods provides an overview of the subject and some new results for single eigenvalue computations.  This work is part of the summary document to be produced by the NSF funded Center for Research in Parallel Computing.
  6. A. Srinivasan, D. M. Ceperley, and M. Mascagni (1999), "Random Number Generators for Parallel Applications," in Monte Carlo Methods in Chemical Physics, D. M. Ferguson, J. I. Siepmann, and D. G. Truhlar, editors, Advances in Chemical Physics Series, Volume 105, John Wiley and Sons, New York, pp. 13-36.  This invited review presents an overview of parallel random number generation and the SPRNG library for the Monte Carlo community working in Physical Chemistry and Molecular Physics.
  7. M. Mascagni (1999), "Serial and Parallel Random Number Generation," in Quantum Monte Carlo in Physics and Chemistry, P. Nightingale and C. Umrigar, editors, Springer-Verlag: New York, Berlin, pp. 277-288.  This invited review presents an overview of parallel random number generation and the SPRNG library for the Quantum Monte Carlo community.  This paper was presented at the NATO Advanced Study Institute on Quantum Monte Carlo Methods in Physics and Chemistry.
  8. M. Mascagni (1997), "Some Methods of Parallel Pseudorandom Number Generation," in Algorithms for Parallel Processing, R. Schreiber, M. Heath and A. Ranade editors, Springer Verlag: New York, Berlin, pp. 277-288.  This invited review presents the discrete mathematics and number theory behind the use of parameterized pseudorandom number generators in parallel.  This paper was presented at the Institute for Mathematics and Its Applications during a special year in High Performance Computing Workshop on Algorithms for Parallel Processing.
  9. M. Mascagni and A. Sherman (1996), "Numerical Methods for Neuronal Modeling," in Methods of Neuronal Modeling: From Ions to Networks, Second Edition, C. Koch and I. Segev editors, MIT Press: Cambridge, Massachusetts, pp. 569-606.  This invited review is a second edition update of the review done in 1989 that is listed below.
  10. M. Mascagni (1996), "Parallel Wiener Integral Methods for Elliptic Boundary Value Problems: A Tale of Two Architectures," in Applications on Advanced Architecture Computers.  This invited chapter looks at SIMD and MIMD implementations of random walk based Monte Carlo algorithms for the solution of elliptic boundary value problems.
  11. M. Mascagni (1996), "Random Number Generation," in CRC Standard Mathematical Tables and Formulae 30th Edition, D. Zwillinger, editor, pp. 593-598.  This invited chapter gives a review of the use of pseudorandom numbers to produce uniform real and integer variables and how to transform them into nonuniform distribution.  The volume where this chapter appears is a widely used reference for Mathematics and Computational technique.
  12. M. Mascagni (1989), "Numerical Methods for Neuronal Modeling," in Methods of Neuronal Modeling: From to Networks to Ions, C. Koch and I. Segev editors, MIT Press: Cambridge, pp. 439-484.  This invited chapter reviews numerical methods for the solution of problems that arise in the quantitative simulation of the nervous system.  It presents finite-difference methods for the solution of ordinary and partial differential equations that arise, as well as methods for solving neural network type systems.  This chapter was based on material the author developed for the Methods in Computational Neuroscience course taught at the Marine Biological Laboratory for four summers.

Refereed International Journal Papers:

  1. C.-O. Hwang, M. Mascagni and T. Won (2008), "Monte Carlo Methods for Computing the Capacitance of the Unit Cube: A Review,"  Mathematics and Computers in Simulation, 11 pages, in the press.  This paper reviews Monte Carlo methods for computing the capacitance of the unit cube to high accuracy.  Based on this, the walk-on-planes (WOB) and walk-on-the-boundary (WOB) methods are analyzed for their computational efficiency.  WOB is found to be superior and is subsequently used to provide a more accurate, and confirmatory, numerical result.
  2. N. Simonov, M. Mascagni and M. O. Fenley (2007), "Monte Carlo Based Linear Poisson-Boltzmann Approach Makes Accurate Salt-Dependent Solvation Free Energy Predictions Possible," Journal of Chemical Physics, 127: 18505.  This paper uses Monte Carlo techniques developed by the authors to make computations of the solvation free energy over a wide range of salt concentrations.  The problems solved involve the implicit solvent model, Poisson-Boltzmann equation, and the results obtained agree with other computational results as well as experimental results.  In addition, these computations explicitly benefit from another advantage of using Monte Carlo in these computations, the ability to use a single simulation to compute the energies at all of the different salt concentrations.
  3. Y. Li and M. Mascagni (2005), "Grid-based Quasi-Monte Carlo Applications," Monte Carlo Methods and Applications, 11: 39-55.  This paper presents preliminary results on extending the Grid-based Monte Carlo services to quasi-Monte Carlo.  Experiments using scrambled quasirandom numbers are also presented.
  4. H. Chi, M. Mascagni, and T. Warnock (2005), "On the Scrambled Halton Sequence," Mathematics and Computers in Simulation, 70(1): 9-21.  This paper analyzes the two-dimensional correlations in the Halton sequence, and based on this analysis presents a new way to find an optimal scrambling (derandomization) of the Halton sequence.  The efficacy of this new scrambling is numerically demonstrated to be far superior to the unscrambled Halton sequence on a very difficult high-dimensional integral.  This paper is joint with Tony Warnock, a Halton student.
  5. M. Mascagni and N. A. Simonov (2004), "Monte Carlo Methods for Calculating Some Physical Properties of Large Molecules," SIAM Journal on Scientific Computing, 26(1): 339-357.  This paper carefully presents a Monte Carlo algorithm for computing the solution of an internal Poisson and external linearized Poisson-Boltzmann problem for molecular geometries.  An analysis of the Monte Carlo estimators is given, as well as a detailed computational complexity analysis.  Finally, a simple problem involving two spherical molecules is solved with the methods described in the paper.
  6. M. Mascagni and H. Chi (2004), "Parallel Linear Congruential Generators with Sophie-Germain Moduli," Parallel Computing, 30: 1217-1231.  This paper considers the use of Sophie-Germain primes, primes of the form m=2p+1 where p is also prime, for use in parameterized linear congruential generators.  It is shown that this choice minimizes initialization time, maximizes the number of streams for a given prime modulus, and provides fast generation when particular Sophie-Germain moduli are used.
  7. A. Karaivanova, M. Mascagni and N. Simonov (2004), "Parallel Quasirandom Walks on the Boundary," Monte Carlo Methods and Applications, 10: 311-320.  This paper studies the us of quasirandom numbers in the solution problems using the "random walk on the boundary" Monte Carlo algorithm.  The analysis and numerical results show that a small but significant improvement in convergence rate is seen over traditional Monte Carlo on this algorithm.
  8. M. Mascagni and H. Chi (2004), "On the Scrambled Halton Sequence," Monte Carlo Methods and Applications, 10: 435-442.  This paper analyzes the two-dimensional correlations in the Halton sequence, and based on this analysis presents a new way to find an optimal scrambling (derandomization) of the Halton sequence.  The efficacy of this new scrambling is numerically demonstrated to be far superior to the unscrambled Halton sequence on a very difficult high-dimensional integral.
  9. Y. Li, M. Mascagni, R. van Engelen and Q. Cai (2004), "A Grid Workflow-Based Monte Carlo Simulation Environment," Neural Parallel and Scientific Computations, 12: 439-454.  This paper takes our previous work on grid services for Monte Carlo and views these services in a workflow setting.
  10. N. A. Simonov and M. Mascagni (2004), "Random Walk Algorithms for Estimating Effective Properties of Digitized Porous Media," Monte Carlo Methods and Applications, 10: 599-608.  This paper describes a Monte Carlo method for permeability calculations in complex digitized porous structures.  The results of computational experiments for some random models of porous media confirm the log-normality hypothesis for the permeability distribution.
  11. A. Rasulov, A. Karaivanova and M. Mascagni (2004), "Quasirandom in Branching Random Walks," Monte Carlo Methods and Applications, 10: 551-558.  This paper studies the effects of using quasirandom numbers in the generation of branching walks used to solved certain nonlinear boundary-value problems.  A slight improvement in convergence rate is seen.
  12. M. Mascagni and N. A. Simonov (2004), "The Random Walk on the Boundary Method for Calculating Capacitance," Journal of Computational Physics, 195(2): 465-473. This paper describes the random walk on the boundary Monte Carlo method, and applies it to the calculation of the capacitance of the unit cube. This calculation is the most accurate known.
  13. C.-O. Hwang and M. Mascagni (2004), "Electrical Capacitance of the Unit Cube," Journal of Applied Physics, 95(7): 3798-3802.  This paper presents a new computation of the capacitance of the unit cube using a first-passage variant based on walks on planes.  The computed results are consistent with our previous computations, and has a slightly smaller set of error bars.
  14. M. Mascagni and A. Srinivasan (2004), "Parameterizing Parallel Multiplicative Lagged-Fibonacci Generators," Parallel Computing, 30: 899-916.  This paper shows how to parameterize full-period multiplicative lagged-Fibonacci generators via the seed, and then how to use this to produce a parallel version of the generator.  This generator is now used in the SPRNG library.
  15. C.-O. Hwang and M. Mascagni (2003), "Analysis and Comparison of Green's Function First-Passage Algorithms with "Walk on Spheres" Algorithms," Mathematics and Computers in Simulation, 63: 605-613.  This paper shows that the Green's function first-passage (GFFP) algorithm is always more efficient that the "walk on spheres" algorithm for solving elliptic PDEs.  In addition, the complexity of GFFP is analyzed.
  16. M. Mascagni and C.-O. Hwang (2003), "e-Shell Error Analysis of Walk on Spheres Algorithms," Mathematics and Computers in Simulation, 63: 605-613.  This paper provides analytic and empirical evidence that the error associated the the e-shell used in Walk on Spheres algorithms is linear in e.  This result motivates the preferential usage of the Green's function first-passage method over Walk on Spheres when both are applicable.
  17. C.-O. Hwang, M. Mascagni and J. A. Given (2003), "A Feynman-Kac Path-Integral Implementation for Poisson's Equation Using an h-conditioned Green's Function," Mathematics and Computers in Simulation, 62: 347-355.  This paper presents a new random walk method for solving the Poisson equation using the Feynman-Kac formula using only a small number of points in a Brownian trajectory.
  18. Y. Li and M. Mascagni (2003), "Analysis of Large-scale Grid-based Monte Carlo Applications,"  International Journal of High Performance Computing Applications (IJHPCA), 17(4): 369-382.  This paper provides an overview of the M-out-of-N technique for Grid-based Monte Carlo.  Also, methods for producing trustworthy Monte Carlo computations are presented.
  19. A. Srinivasan, M. Mascagni, and D. Ceperley  (2003), "Testing Parallel Random Number Generators,"  Parallel Computing, 29: 69-94.  This paper provides a mathematical framework for testing parallel random number generators and also motivates the construction of the SPRNG test suite.  In addition, results from extensive parallel testing of multiplicative lagged-Fibonacci generators, candidates for SPRNG, are presented.
  20. J. A. Given, C.-O. Hwang and M. Mascagni (2002), "First- and last-passage Monte Carlo algorithms for the charge density distribution on a conducting surface," Physical Review E, 66, 056704, 8 pages.  This paper presents two new Monte Carlo algorithms based on the concept of "last-passage" diffusion.  These methods are compared with each other and with the best first-passage algorithm for computing the charge density on a circular disk held at unit potential.
  21. C.-O. Hwang, J. A. Given and M. Mascagni (2001), "The Simulation-Tabulation Method for Classical Diffusion Monte Carlo," Journal of Computational Physics, 174: 925-946.  This paper shows how simulated Green's functions, simulation-tabulation, can be used to augment our Green's function first-passage Monte Carlo method.  The utility of simulation-tabulation is verified by solving problems from materials science and biochemistry.
  22. M. Mascagni, A. Karaivanova and Y. Li (2001), "A Quasi-Monte Carlo Method for Elliptic Partial Differential Equations," Monte Carlo Methods and Applications, 7: 283-294.  This paper presents new bounds on errors associated with the use of quasirandom numbers in Markov chain-based methods for the solution of elliptic partial differential equations.
  23. C.-O. Hwang, M. Mascagni and J. A. Given (2001), "Rapid Diffusion Monte Carlo Algorithms for Fluid Dynamic Permeability," Monte Carlo Methods and Applications, 7: 213-222.  This paper uses our Green's function first-passage Monte Carlo method to compute the permeability of a wide class of porous media models considerably extending our previous results. 
  24. C.-O. Hwang and  M. Mascagni (2001), "Efficient Modified Walk on Spheres Algorithm for the Linearized Poisson-Boltzmann Equation," Applied Physics Letters, 76: 787-789.  This paper presents an improved method for using the Feynman-Kac formula as the basis for a Monte Carlo algorithm to solve the linearized Poisson-Boltzmann equation.  This is accomplished with a new probability that is used to terminate random walks in the linearized Poisson-Boltzmann case.
  25. M. Mascagni and A. Karaivanova (2000), "Matrix Computations Using Quasirandom Sequences,"  Springer Verlag Lecture Notes in Computer Science, 1988: 552-559.  This paper presents new methods and error bounds for using quasi-Monte Carlo methods for computing eigenvalues of large, sparse matrices.
  26. M. Mascagni and A. Srinivasan (2000), "Algorithm 806: SPRNG: A Scalable Library for Pseudorandom Number Generation," ACM Transactions on Mathematical Software, 26: 436-461.  This paper describes the SPRNG library and gives an overview of the mathematical foundation for the random number generators in SPRNG, the computational techniques used in parallelization, the randomness testing suite in SPRNG, and shows how the library can be used to provide reliable and reproducible parallel Monte Carlo computations.  SPRNG is the first library of its kind.
  27. C.-O. Hwang, J. A. Given and M. Mascagni (2000), "On the Rapid Calculation of Permeability for Porous Media Using Brownian Motion Paths," Physics of Fluids, 12: 1699-1709.  This paper derives our Green's function first-passage Monte Carlo method and applies it to the computation of the fluid permeability of porous media made up of overlapping and nonoverlapping monosized spheres.  This new method is the fastest method known for doing these kinds of calculations.
  28. M. Mascagni (1998), "Parallel Linear Congruential Generators with Prime Moduli," Parallel Computing, 24: 923-936.  This paper derives a method for parameterizing primitive roots modulo a prime and uses this as the basis for providing parallel linear congruential random numbers.  In addition, an efficient algorithm for finding the ith integer relatively prime to given, factored, integer is presented.
  29. M. Mascagni, M. L. Robinson, D. V. Pryor and S. A. Cuccaro (1995), "Parallel Pseudorandom Number Generation Using Additive Lagged-Fibonacci Recursions", Springer Verlag Lecture Notes in Statistics, 106: 263-277.  This paper proves bounds on exponential sum bounds used to estimate the cross-correlation between different random number streams produced using our parallelization of additive lagged-Fibonacci generators.
  30. M. Mascagni, S. A. Cuccaro, D. V. Pryor and M. L. Robinson (1995), "A Fast, High Quality, and Reproducible Parallel Lagged-Fibonacci Pseudorandom Number Generator", Journal of Computational Physics, 119: 211-219.  This paper presents a novel parameterization of additive lagged-Fibonacci generators based on seeding.  This approach is used as the basis of providing a parallel version of this generator that requires no interprocessor communication while assuring that different processors get distinct random number streams.
  31. A. Sherman and M. Mascagni (1994), "A Gradient Random Walk Method for Two-Dimensional Reaction-Diffusion Equations'', SIAM Journal on Scientific Computing, 15: 1280-1293.  This paper presents and analyzes a Monte Carlo method for solving two-dimensional reaction-diffusion equations.  The method is related to the random vortex method for the two-dimensional incompressible Navier-Stokes equations, and the paper also presents numerical evidence of it's effectiveness.
  32. M. Mascagni (1991), "A Parallelizing Algorithm for Computing Solutions to Arbitrarily Branched Neuron Models," Journal of Neuroscience Methods, 36: 105-114.  This paper presents a parallel algorithm for solving coupled, branching, one-dimensional nonlinear reaction-diffusion equations based on finite-difference methods.  These kinds of equations arise in the realistic modeling of the nervous system.
  33. M. Mascagni (1991), "High-Dimensional Numerical Integration and Massively Parallel Computing," Contemporary Mathematics, 115: 53-73.  This paper presents parallel data-parallel methods for doing deterministic and Monte Carlo high-dimensional numerical integration using parallel prefix methods.  In addition, data-parallel techniques for Monte Carlo solution of partial differential equations based on random walks is presented along with numerical examples performed on the CM-2 massively parallel computer.
  34. M. Mascagni (1990), "The Backward Euler Method for Numerical Solution of the Hodgkin-Huxley Equations of Nerve Conduction," SIAM Journal on Numerical Analysis, 27: 941-962.  This method analyzed the convergence of the backward Euler method for the finite-difference solution of the Neumann initial-boundary value problem for the Hodgkin-Huxley equations of nerve conduction.  Convergence is proved with the help of derived a priori bounds for solutions to the nonlinear difference equations.
  35. M. Mascagni (1990), "In Initial-Boundary Value Problem of Physiological Importance for Equations of Nerve Conduction," Communications on Pure and Applied Mathematics, 42: 213-227.  The paper proves well-posedness in the sense of Hadamard for the Neumann initial-boundary value problem for the Hodgkin-Huxley equations of nerve conduction.  In addition, a priori bounds on the solution of this nonlinear system of partial differential equations.
  36. M. Mascagni (1989), "Animation's Role in Mathematically Modeling the Nervous System," Iris Universe, Winter 1989: 6-18.  This paper presents computational results obtained in the numerical modeling of a ring of Hodgkin-Huxley neurons with passive dendritic segments.  In particular, a presentation level visualization of the results is presented as well as a discussion of new visualization tools that allow rapid qualitative analysis of the large data sets produced in realistic neural modeling.
  37. M. Mascagni and W. L. Miranker (1985), "Arithmetically Improved Algorithmic Performance," Computing, 35: 153-175.  This paper presents theoretical and numerical evidence that numerical algorithms sensitive to numerical accuracy can be significantly improved by using augmented floating-point arithmetic to exactly compute inner products.  This augmented arithmetic was implemented in hardware in IBM 370 series mainframe with the ACRITH product.
  38. W. L. Miranker, M. Mascagni, and S. Rump (1985), "Case Studies for Augmented Floating-Point Arithmetic," Lecture Notes in Computer Science, 235: 86-118.  This paper provides numerical examples from poorly posed problems arising from finite-difference solutions of ordinary and partial differential equations, and numerical linear algebra to  motivate the use of augmented floating-point arithmetic to exactly compute inner products.

Invited International Publications:

  1. M. Mascagni (1999), "Parallel Pseudorandom Number Generation," SIAM News, August, pp. 1,8-10.  This article provides a general presentation of the mathematical and computational underpinnings of parallel random number generation.  In particular, the problem of parallel reproducibility and the solution of parameterized random number generations id discussed.
  2. M. Mascagni (1998), "High-Performance Monte Carlo Tools," IEEE Computational Science and Engineering, 5(2): 97-98.  This article summarizes the results of a workshop on High-Performance Monte Carlo Tools.
  3. M. Mascagni (1990), "Parallel Wiener Integral Methods for Elliptic Boundary Value Problems: A Tale of Two Architectures," SIAM News, July, pp. 27-33.  This article looks at SIMD and MIMD implementations of random walk based Monte Carlo algorithms for the solution of elliptic boundary value problems.  It was reprinted as item 6 among the refereed book chapters, above.

Refereed International Conference Papers:

  1. M. Mascagni and J. Ren (2008), "New Development in the Scalable Parallel Random Number Generator (SPRNG) Library," The Institute of Statistical Mathematics Cooperative Research Report, 210: 120-125.  The Scalable Parallel Random Number Generators (SPRNG) Library is a widely used software package for random number generation in high-performance computing settings.  In this paper, we provide an overview of SPRNG and especially discuss its recent developments. First, we give a very short review of random number generators and their applications to Monte Carlo computations. Then, we discuss some methods of parallel random number generation, and give the rationale for SPRNG. We next discuss about the past versions of SPRNG and the most recent version, version 4.0. Finally, webriefly discuss the impact of SPRNG and speculate on possible future work to SPRNG.
  2. M. Mascagni (2008), "Random Number Generation : A Practitioner's Overview," The Institute of Statistical Mathematics Cooperative Research Report, 210: 97-119.  This gives a comprehensive overview of pseudorandom number generation, parallel pseudorandom number generation, and quasirandom number generation.  The presentation is motivated by an applications-based point-of-view.
  3. Y. Li, M. Mascagni and A. Gorin (2007), "Decentralized Replica Exchange Parallel Tempering: An Efficient Implementation of Parallel Tempering Using MPI and SPRNG," Lecture Notes in Computer Science, 4707: 507-519.  This was a paper given at the international conference entitled Computational Science and Its Applications (ICCSA 2007) in Kuala Lumpur, Malaysia in August, 2007.  This paper discusses parallel Tempering (PT), also known as Replica Exchange, which is a powerful Markov Chain Monte Carlo sampling approach which aims at reducing the relaxation time in simulations of physical systems. In this paper, we present a novel implementation of PT, so-called decentralized replica exchange PT, using MPI and the Scalable Parallel Random Number Generators (SPRNG) libraries. By adjusting the replica exchange operations in the original PT algorithm, and taking advantage of the characteristics of pseudorandom number generators, this implementation minimizes the overhead caused by interprocessor communication in replica exchange in PT. This enables one to efficiently apply PT to large-scale massively parallel systems. The efficiency of this implementation has been demonstrated in the context of various benchmark energy functions, such as the high-dimensional Rosenbrock function, and a rugged funnel-like function.
  4. H. Chi and M. Mascagni (2007), "Efficient Generation of Parallel Quasirandom Sequences via Scrambling," Lecture Notes in Computer Science, 4487: 723-730.  This was a paper given at the international conference entitled International Conference on Computational Science 2007 (ICCS 2007), held May 2007 in Beijing, People's Republic of China.  This paper proposes an alternative approach for generating parallel quasirandom sequences. We take a single quasirandom sequence and provide different random digit scramblings of the given sequence. The exact meaning of the digit scrambling we use depends on the mathematical details of the quasirandom number sequence's method of generation. For the Faure sequence we scramble by modifying the generator matrices in the definition. The obtained sequences are very interesting as the scrambled versions used in individual processes are of higher quality than the original Faure sequence. Thus, we not only obtain the expected near-perfect speedup of the naturally parallel Monte Carlo methods, but the errors in the parallel computation is even smaller than if the computation were done with the same quantity of quasirandom numbers using the original, unscrambled, Faure sequence.
  5. N. A. Simonov and M. Mascagni (2005), "The Method of Random Walk on Sphere for Solving Boundary-Value problems for Molecular Electrostatics, Proceedings of the 17th IMACS World Congress, 5 pages published on compact disc, July, 2005.  This paper presents preliminary results for a new method for evaluating internal boundary conditions that arise in molecular electrostatics computations.  The methods were developed to work in concert with existing Monte Carlo methods for solving the entire PDE system, and are a significant improvement on a finite-difference based method previously developed.  Not only is performance enhanced by an order of magnitude, but a bias from the finite-difference based method is eliminated. 
  6. Y. Li and M. Mascagni (2005), "A Bio-inspired Job Scheduling Algorithm for Monte Carlo Applications on a Computational Grid," Proceedings of the 17th IMACS World Congress, 7 pages published on compact disc, July, 2005.  In this paper we present a bio-inspired job scheduling mechanism that enables the adaptation of large-scale, naturally parallel and compute-intensive Monte Carlo tasks to clustered computational farms, such as large-scale computational grids, with heterogeneous and dynamic performance.  The kernel of this scheduling mechanism is a swarm intelligent algorithm, which is inspired from the ants’ behavior in a social insect colony.
  7. C. Fleming, M. Mascagni and N. A. Simonov (2005), "An Efficient Monte Carlo Approach for Solving Linear Problems in Biomolecular Electrostatics," Proceedings of the Fifth International Conference on Computational Science (ICCS 2005), V. S. Sunderam, G. D. van Albada, P. M. A. Sloot, and  J. J. Dongarra (eds.), Lecture Notes in Computer Science, 3516: 760-765 (Part 3). (May 2005, Atlanta, GA)  This paper presents preliminary results for a new method for evaluating internal boundary conditions that arise in molecular electrostatics computations.  The methods were developed to work in concert with existing Monte Carlo methods for solving the entire PDE system, and are a significant improvement on a finite-difference based method previously developed.  Not only is performance enhanced by an order of magnitude, but a bias from the finite-difference based method is eliminated.
  8. H. Chi, P. Beerli, D. W. Evans and M. Mascagni (2005), "On the Scrambled Soboĺ Sequence," Proceedings of the Fifth International Conference on Computational Science (ICCS 2005), V. S. Sunderam, G. D. van Albada, P. M. A. Sloot, and  J. J. Dongarra (eds.), Lecture Notes in Computer Science, 3516: 775-782 (Part 3). (May 2005, Atlanta, GA  This paper presents an optimal linear scrambling of the Soboĺ sequence with techniques similar to those previously developed by Chi and Mascagni for the Faure and Halton sequences.  This sequences is shown to be of good quality in comparison to others based on the evaluation of a high-dimensional geometrical Asian option.
  9. N. A. Simonov and M. Mascagni (2004), "Random Walk Algorithms for Estimating Electrostatic Properties of Large Molecules," Proceedings of The International Conference on Computational Mathematics (ICCM-2004), Novosibirsk, Russia, G. A. Mikhailov, V. P. Il'in, and Y. M. Laevsky, eds., ICM&G Publisher, Novosibirsk, Russia, Part I: 352-358.  This paper describes a new Monte Carlo algorithm for solving the coupled Poisson/Poisson-Boltzmann system related to the electrostatics of large molecules in a continuum model of solvent.
  10. M. Mascagni, A. Karaivanova, C.-O. Hwang (2004), "Quasi-Monte Carlo Methods for Elliptic  Boundary Value Problems," Proceedings of Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC) 2002, H. Niederreiter (ed.), Springer Verlag: Berlin, pp. 345-356.  This paper gives a brief overview of quasi-Monte Carlo methods for solving elliptic boundary value problems using walk-on-spheres variants.
  11. Y. Li and M. Mascagni (2004), "E-Science Workflow on the Grid," Proceedings of the International Association for the Development of the Information Society (IADIS) International Conference: e-Society 2004, P. Isaías, P. Komers, M. McPherson (eds.),  pp. 1041-1046.  This paper describes how one can use workflow techniques to implement e-science-based grid computations.  Specifically, it describes how one maps agent operations from workflow onto grid services using XML as the communications intermediary.
  12. Y. Li and M. Mascagni (2004), "E-Science on the Grid: Toward a Dynamic E-Science Automation with XML and Workflow Techniques," accepted to the Proceedings of the 8th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2004), Orlando, Florida, 7 pages.  This paper describes how one can use workflow techniques to implement e-science-based grid computations.  Specifically, it describes how one maps agent operations from workflow onto grid services using XML as the communications intermediary.
  13. M. Mascagni and H. Chi (2004), "Optimal Quasi-Monte Carlo Valuation of Derivative Securities," Computational Finance and Its Applications, M. Costantino and C. A. Brebbia (eds.), WIT Press, pp. 177-185.  This paper finds an optimal scrambling of the Faure sequence within the i-binomial family.  Then, this derandomized generalized Faure (GFaure) sequence is used to evaluate a high-dimensional derivative security, an European call option on the geometric mean of several assets.  The numerical results show improvement over the plain Faure sequence.
  14. M. Mascagni and Y. Li (2004), "Computational Infrastructure for Parallel, Distributed, and Grid-based Monte Carlo Computations," Proceedings of the Fourth International Conference on Large-Scale Scientific Computations (LSSC'03), Sozopol, Bulgaria, I. Lirkov, S. Margenov, J. Wasniewski, P. Yalamov eds., Lecture Notes in Computer Sciences, 2907: 39-52.  This paper provides an overview of computational infrastructure for parallel, distributed, and Grid-based Monte Carlo computations.  The starting point is the Scalable Parallel Random Number Generators (SPRNG) Library, and its uses for parallel and distributed Monte Carlo, and the discussion ends with a description of our Grid-Computing Infrastructure for Monte Carlo Applications (GCIMCA).
  15. A. Karaivanova, M. Mascagni and N. Simonov (2004), "Solving Boundary Value Problems Using Quasirandom Walks on the Boundary," Proceedings of the Fourth International Conference on Large-Scale Scientific Computations (LSSC'03), Sozopol, Bulgaria, I. Lirkov, S. Margenov, J. Wasniewski, P. Yalamov eds., Lecture Notes in Computer Sciences, 2907: 162-169.  This paper studies the us of quasirandom numbers in the solution problems using the "random walk on the boundary" Monte Carlo algorithm.  The analysis and numerical results show that a small but significant improvement in convergence rate is seen over traditional Monte Carlo on this algorithm.
  16. A. Karaivanova and M. Mascagni (2003), "Quasi-Monte Carlo Methods for Some Problems in Linear Algebra," Proceedings of the 7th Joint Conference on Information Sciences (JCIS 2003), pp. 1754-1757.  This paper presents Monte Carlo and quasi-Monte Carlo methods for the solution of various problems in numerical linear algebra.  The paper begins with an analysis of matrix-vector products, then solutions via Neumann series, and finally the eigenvalue problems including stochastic versions of the power method and the resolvent method.
  17. M. Mascagni and A. Karaivanova (2002), "A Monte Carlo Approach for Finding More Than One Eigenpair," Proceedings of Fifth International Conference on Numerical Methods and Applications, I. Dimov, L. Lirkov, S. Margenov, and Z. Zlatev (eds.),  Lecture Notes in Computer Science, 2542: 123-131.  This paper extends previous results on Monte Carlo methods for spectral linear algebra calculations.
  18. Y. Li, M. Mascagni and R. van Engelen (2003), "GCIMCA: A Globus and SPRNG Implementation of a Grid-Computing Infrastructure for Monte Carlo Applications," accepted to the The 2003 International Conference on Parallel and Distributed Processing Techniques and Applications, (PDPTA'03), Las Vegas, Nevada,  5 pages.  Taking advantage of the grid facilities of the Globus toolkit and the large-scale random number streams generated by the SPRNG library, this paper discusses the implementation of GCIMCA, the Grid-Computing Infrastructure for Monte Carlo Applications, to provide services for high-performance and trustworthy grid-based Monte Carlo computations.
  19. M. Mascagni and N. A. Simonov (2003), "Monte Carlo Methods for Calculating the Electrostatic Energy of a Molecule," Proceedings of the 2003 International Conference on Computational Science (ICCS 2003), P. M. A. Sloot, D. Abramson, A. V. Bogdanov, J. J. Dongarra, A. Y. Zomaya, and Y. E. Gorbachev (eds.), Lecture Notes in Computer Science, 2330: 598-608 (Part 2). (June 2003, Melbourne, Australia and Saint Petersburg, Russia)  This paper presents a new Monte Carlo algorithm for computing an electrostatic form of the internal energy of a large protein molecule.  The algorithm is also analyzed.
  20. Y. Li and M. Mascagni (2003), "Improving Performance via Computational Replication on a Large-Scale Computational Grid," Proceedings of the IEEE/ACM International Symposium on Cluster Computing and the Grid (IEEE/ACM CCGRID2003), pp. 442-448.  This paper describes and analyze the computational replication method to improve performance of a generic application on a computational grid.  The computational replication method is extended to an N-out-of-M schedule technique to improve the wall clock time of Grid-based Monte Carlo computations.
  21. Y. Li, M. Mascagni and M. H. Peters (2003), "Grid-based Nonequilibrium Multiple-Time Scale Molecular Dynamics/Brownian Dynamics Simulations of Ligand-Receptor Interactions in Structured Protein Systems," Proceedings of the First International Workshop on Biomedical Computations on the Grid (BioGrid'03) in Proceedings of the IEEE/ACM International Symposium on Cluster Computing and the Grid (IEEE/ACM CCGRID2003), pp. 568-573.  This paper describes the application of our Grid-based Monte Carlo technology to problems in protein biophysics.
  22. M. Mascagni and N. A. Simonov (2002), "Random Walk Algorithms on The Boundary Methods for Computing Reaction Rate and Capacitance," Proceedings of the The International Conference on Computational Mathematics, G. A. Mikhailov, V. P. Il'in, Y. M. Laevsky (eds.), ICM & MG Publishers, Novosibirsk, Russia, pp. 238-242.  This paper presents "walk on the boundary" methods for solving some boundary-value problems formulated as integral equations.  Specifically, it deals with computing the capacitance of a convex object and diffusion-limited reaction rates.
  23. Y. Li and M. Mascagni (2002), "Grid-based Monte Carlo Application," Proceedings of Grid Computing-GRID 2002, Manish Parashar (ed.), Lecture Notes in Computer Science, 2536: 13-24.  This paper examines the suitability of Monte Carlo applications for the grid.  In addition, the M-out-of-N strategy is examined to speed Grid Monte Carlo computations in a faulty environment and in using the random number generator to provide the ability to validate a volunteered Monte Carlo computation.
  24. M. Mascagni and A. Karaivanova (2002), "A Parallel Quasi-Monte Carlo Method for Solving Systems of Linear Equations,"  Proceedings of the 2002 International Conference on Computational Science, Peter M. A. Sloot, C. J. Kenneth Tan, Jack J. Dongarra, Alfons G. Hoekstra (eds.), Lecture Notes in Computer Science, 2330: 598-608 (Part 2).  (April 2002, Amsterdam, Netherlands)  This paper presents and analyzes a quasi-Monte Carlo approach to solving systems of linear systems.  In addition, the parallel efficiency of this method is shown to be extremely good and consistent with the ordinary Monte Carlo approach to this problem.
  25. A. Srinivasan and M. Mascagni (2002), "Monte Carlo Techniques for Estimating the Fiedler Vector in Graph Applications," Proceedings of the 2002 International Conference on Computational Science (ICCS 2002), Peter M.A. Sloot, C. J. Kenneth Tan, Jack J. Dongarra, Alfons G. Hoekstra (eds.), Lecture Notes in Computer Science, 2330: 635-645 (Part 2).   (April 2002, Amsterdam, Netherlands)  This paper shows how to use Monte Carlo techniques, based on Markov chains and the probabilistic computations of matrix-vector products, to estimate the Fiedler vector.  This problem has significance in graph partitioning problems related to domain decomposition.
  26. M. Mascagni and A. Karaivanova (2001), "A Parallel Quasi-Monte Carlo Method for Computing Extremal Eigenvalues," Proceedings of Monte Carlo and Quasi-Monte Carlo Methods 2000, K.-T. Fang, H. F. J. Hickernell, and H. Niederreiter, eds., Springer-Verlag: Berlin: pp. 369-380.  (December 2000, Honk Kong, China)  This paper provides an error bound for the use of quasi-Monte Carlo methods for computing extremal eigenvalues of sparse matrices via methods related to the power method.  In addition, it is shown that the parallel efficiency expected of Monte Carlo methods extends to these Markov chain-based quasi-Monte Carlo methods.
  27. J. A. Given, C.-O. Hwang and M. Mascagni (2001), "Continuous Path Brownian Trajectories for Diffusion Monte Carlo Via First- and Last-Passage Distributions," Proceedings of the Third International Conference on Large-Scale Scientific Computations, 12 pages, in press. (June 2001, Sozopol, Bulgaria)  This paper presents an overview of the application of the Green's function first-passage and simulation tabulation methods to problems arising in porous media, composite materials, and biochemistry.
  28. C.-O. Hwang, J. A. Given, and M. Mascagni (2001), "A Feynman-Kac Path-Integral Implementation for Poisson's Equation," in the Proceedings of the 2001 International Conference on Computational Science, part I, pp. 1282-1288. (May 2001, San Francisco, CA)  This paper presents a new method to evaluate path integrals arising from the Feynman-Kac solution of the Poisson equation when only first-passage information is known about the path trajectories.  This has applications for the use of the Green's function first-passage method for Poisson's equation.
  29. M. Mascagni (2000), "Theory and Software for Parallel Random Number Generation," Proceedings of The Fourth International Conference on Supercomputing in Nuclear Applications (SNA 2000), CD-ROM: 14 pages. (September 2000, Tokyo, Japan). This paper presents an overview of parallel random number generation aimed at the Nuclear Engineering community.  Mathematical background and the use of SPRNG is presented.
  30. M. Zhou and M. Mascagni (2000), "The Cycle Server: A Web Platform for Running Parallel Monte Carlo Applications on a Heterogeneous Condor Pool of Workstations," Proceedings of the 2000 International Conference on Parallel Processing Workshops on Scalable Web Services, pp. 111-118. (August 2000, Toronto, Canada)  This paper presents a distributed computing tool that permits users to submit and retrieve parallel Monte Carlo jobs to a Condor cluster.  Most importantly, this tool provides a distributed compilation service that, given application source, produces executables for many different operating system/architecture combinations.
  31. M. Mascagni and S. Rahimi (2000), "Parallel Inversive Congruential Generators:  Software and Field-Programmable Gate Array Implementations," in Proceedings of the International Conference on Monte Carlo Simulation, G. I. Schuëller and P. D. Spanos, eds., pp. 35-40. (June 2000, Monte Carlo, Monaco)  This paper presents a hardware design for modular integer inversion and implements and benchmarks the design on a field-programmable gate array device.  This problem is motivated by the desire to accelerate the generation of inversive congruential pseudorandom numbers.
  32. A. Karaivanova and M. Mascagni (2000), "Are Quasirandom Numbers Good for Anything Besides Integration?"  Proceedings of Advances in Reactor Physics and Mathematics and Computation into the Next Millennium (PHYSOR2000),  CD-ROM: 15 pages. (May 2000, Pittsburgh, PA)  This paper presents quasi-Monte Carlo methods for Markov-chain based problems arising from numerical linear algebra.  It contrasts these applications of quasirandom numbers to the more classical application of numerical integration.
  33. M. Mascagni (1999), "SPRNG: A Scalable Library for Pseudorandom Number Generation,"  in Proceedings of the Ninth SIAM Conference on Parallel Processing for Scientific Computing, CD-ROM: 10 pages.  (March 1999, San Antonio, TX)  This paper presents an overview of parallel pseudorandom number generation via parameterization and discuss particulars of the SPRNG library.
  34. M. Hydari, D. M. Ceperley, A. Srinivasan, and M. Mascagni (1999), "A Fast High-Quality Pseudo Random Number Library for Java," in Proceedings of the Ninth SIAM Conference on Parallel Processing for Scientific Computing, CD-ROM: 17 pages. (March 1999, San Antonio, TX)  This paper presents a Java extension to the SPRNG library.
  35. M. Mascagni (1999), "SPRNG: A Scalable Library for Pseudorandom Number Generation," Recent Advances in Numerical Methods and Applications II,  O. Iliev, B. Sendov, M. Kaschiev, S. Margenov, P. Vassilevski, editors, World Scientific, pp. 284-295. (August 1998, Sofia, Bulgaria)  This paper presents an overview of parallel pseudorandom number generation via parameterization and discuss particulars of the SPRNG library.
  36. J.-L. Larriba-Pey, M. Mascagni, A. Jorba and J. J. Navarro (1995), "An Analysis of the Parallel Computation of Arbitrarily Branched Cable Neuron Models'', in Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing, pp. 373-378. (March 1995, San Francisco, CA)  This paper provides an analysis of parallel finite-difference methods for solving nerve equations based on new results for parallel tridiagonal linear system solvers.
  37. S. A. Cuccaro, M. Mascagni and D. V. Pryor (1995) "Techniques for Testing the Quality of Parallel Pseudorandom Number Generators'', Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing, pp. 279-284. (March 1995, San Francisco, CA)  This paper presents a mathematical framework for the testing of parallel random number generators based on the parallel modification of serials tests and on the use of exponential sum tests.
  38. D. V. Pryor, S. A. Cuccaro, M. Mascagni and M. L. Robinson (1994) "Implementation and Usage of a Portable and Reproducible Parallel Pseudorandom Number Generator'', Proceedings of Supercomputing '94, pp. 311-319. (November 1994, Washington, D.C.)  This paper discusses the parallel computational aspects that permit the dynamic spawning of distinct parallel random number generators without the need for interprocessor communication.  The method utilizes parameterized generators mapped to the binary tree and the manipulations that are simplified with this mapping.
  39. M. Mascagni, S. A. Cuccaro, D. V. Pryor and M. L. Robinson (1993) "Recent Developments in Parallel Pseudorandom Number Generation'', Proceedings of the Sixth SIAM Conference on Parallel Processing for Scientific Computing, pp. 524-529. (March 1993, Norfolk, VA)  This paper presents results on the parameterization of additive lagged-Fibonacci generators for use in parallel.

International Conference Proceedings Edited:

  1. D. H. Bailey, P. E. Bjørstad, J. R. Gilbert, M. V. Mascagni, R. S. Schreiber, H. D. Simon, V. J. Torczon and L. T. Watson, editors (1995) Proceedings of the Seventh SIAM Conference on Parallel Processing for Scientific Computing, SIAM, Philadelphia.

National Conference Papers:

  1. M. Zhou and M. Mascagni (1999), "Parallel Monte Carlo in a Distributed Environment: SPRNG and CONDOR," in Proceedings of the First Southern Symposium on Computing, CD-ROM: 5 pages. (December, 1998, Hattiesburg, MS)  This paper briefly reviews a distributed computing tool that permits users to submit and retrieve parallel Monte Carlo jobs to a Condor cluster.  Most importantly, this tool provides a distributed compilation service that, given application source, produces executables for many different operating system/architecture combinations.
  2. C.-O. Hwang, J. A. Given and M. Mascagni (1999), "A New Fluid Permeability Estimation,"  in Proceedings of the First Southern Symposium on Computing, CD-ROM: 7 pages. (December, 1998, Hattiesburg, MS)  This paper briefly presents Green's function first-passage Monte Carlo method to compute the permeability of porous media models and provides preliminary numerical results.  

Preprints:

  1. Y. Li, M. Mascagni and A. Gorin (2008), "A Decentralized Parallel Implementation for Parallel Tempering Algorithm," submitted to Parallel Computing in March, 2008.  This paper discusses parallel Tempering (PT), also known as Replica Exchange, which is a powerful Markov Chain Monte Carlo sampling approach which aims at reducing the relaxation time in simulations of physical systems. In this paper, we present a novel implementation of PT, so-called decentralized replica exchange PT, using MPI and the Scalable Parallel Random Number Generators (SPRNG) libraries. By adjusting the replica exchange operations in the original PT algorithm, and taking advantage of the characteristics of pseudorandom number generators, this implementation minimizes the overhead caused by interprocessor communication in replica exchange in PT. This enables one to efficiently apply PT to large-scale massively parallel systems. The efficiency of this implementation has been demonstrated in the context of various benchmark energy functions.
  2. Y. Li, W. Mirugi and M. Mascagni (2005), "Test the Rule 30 Cellular Automata Random Number Generator," submitted for publication in Mathematics and Computers in Simulation, 8 pages.  This paper looks at the "Rule 30" cellular automata as a random number generator.  This cellular automata was first proposed as a random number generator by Wolfram, and is still used in Mathematica.  Empirical tests showed the generator similar in quality to other common generators, but overall it is unsuitable as the generation time is 1000 times slower.
  3. A. Rasulov, G. Raimova and M. Mascagni (2005), "Quasirandom Sequences in Branching Random Walks," submitted for publication in Mathematics and Computers in Simulation, 9 pages.  This paper presents strong numerical evidence that using quasirandom number in the generation of uniform directions, as part of a Markov chain-based algorithm for solving partial differential equations is very effective.
  4. N. Simonov and M. Mascagni (2005), "Random Walk Algorithms for Solving Some Boundary-Value Problems in Biomolecular Electrostatics," submitted for publication in Mathematics and Computers in Simulation, 14 pages.  This paper presents new results based on using a tangent plane approximation to remove a negative term that arose in an integral equation-based method for enforcing certain boundary conditions.  These boundary conditions are part of an electrostatics system involving molecular geometry and the Poisson and Poisson-Boltzmann equations.
  5. H. Chi, R. Jones, and M. Mascagni (2005), "Generating Parameterized Parallel Random Number Streams via LCGs with Differing Moduli," submitted for publication in Mathematics and Computers in Simulation, 12 pages.  This paper presents a new parameterization of linear congruential generators (LGCs) of the kind already used in the SPRNG library.  Using spectral test methods based on combined LCGs, we create a new criterion to assess the parallel quality of LCGs which have different moduli.  The approach is explained, justified theoretically, and a small numerical example is carried out.
  6. A. Rasulov, G. Raimova and M. Mascagni (2005), "Monte Carlo Solution of Some Nonlinear Parabolic Initial-Value Problems," submitted for publication in Mathematics and Computers in Simulation, 9 pages.  This paper presents a Markov chain-based algorithm for solving the pure initial-value problem for a class of nonlinear parabolic equations.  The nonlinearity is dealt with with a branching Markov chain, and numerical results are presented as further evidence of efficacy.
  7. Y. Li and M. Mascagni (2005), "Optimizing Dynamic Grid-based Resources for Large-Scale Monte Carlo Applications," submitted for publication in Mathematics and Computers in Simulation, 14 pages.  In this paper we present a novel, bio-inspired method for optimizing the organization of dynamic computational resources on a Grid for carrying out large-scale Monte Carlo applications. The kernel of the scheduling mechanism is a swarm intelligence algorithm.  We tested the algorithm on a simulated computational Grid and compared it with static scheduling algorithms. Our results showed good performance, adaptability, and robustness on a dynamic computational Grid with respect to its competitors. 
  8. B. Bouta, A. Srinivasan and M. Mascagni (2005), "Exploring Monte Carlo Linear Solver Splittings: A Load-Balancing Example," submitted for publication in Mathematics and Computers in Simulation, 18 pages.  This paper presents new Monte Carlo methods for solving linear systems are studied within the context of the load-balancing problem.  In our take on his problem, the graph Laplacian matrix provides the linear system.  We then study this system with three stat, ionary iterative methods that are used as the basis for providing Monte Carlo methods.  This work represents new results based on using more advantageous splittings to improve the performance of Monte Carlo methods in Linear Algebra.
  9. H. Chi and M. Mascagni (2003), "Scrambled Quasirandom Sequences and Their Application," submitted for publication in SIAM Review, 41 pages.  This paper is a review of the state-of-the-art in methods of scrambling quasirandom numbers.  In addition, applications of quasirandom sequences are discussed including automatic error estimation for quasi-Monte Carlo and parallel quasirandom number generation.  Also, the topics of randomized quasirandom numbers and the derandomization of quasirandom numbers is reviewed.
  10. E. I. Atanassov and M. Mascagni (2003), "Efficient Generation of Low-discrepancy Sequences," submitted to Journal of Complexity, 18 pages.  This paper presents algorithms and source code examples for the efficient generation of scrambled Halton and Sobol' quasirandom numbers on modern microprocessor architectures.
  11. C.-O. Hwang, M. Mascagni and J. A. Given (2001), "A Feynman-Kac Formula Implementation for the Linearized Poisson-Boltzmann Equation," submitted for publication in Mathematics and Computers in Simulation, 10 pages.  This paper presents a new random walk method for solving the linear Poisson-Boltzmann equation and proves mathematically (not implementationally) the same as a previously published method of the authors.

Reports:

  1. M. H. Zhou, M. Mascagni, and A. Y. Qiao (1998), "Explicit Finite Difference Schemes for the Advection Equation," Conservation Law Preprint 1998-024.  This report presents a new explicit finite-difference method for solving the advection equation.
  2. M. Mascagni (1997), "Polynomial versus Matrix Methods for Leap-Ahead in Shift Register Type Pseudorandom Number Generators," Institute for Mathematics and its Applications (IMA) Reprint 1469.  This paper shows that fast leap-ahead methods applicable to shift-register pseudorandom number generators can be extended to additive lagged-Fibonacci generators.
  3. M. Mascagni (1995), "A Deterministic Particle Method for One-Dimensional Reaction-Diffusion Equations'', Research Institute for Advanced Computer Science (RIACS) Technical Report: 95.23, Institute for Defense Analyses Center for Computing Sciences (IDA/CCS) Technical Report: CCS-TR-95-144.  This paper derives a one-dimensional particle method for the solution of nonlinear reaction-diffusion equations.  This method is a level-set analog of Monte Carlo methods previously studied by the author.  Numerical evidence is presented on the efficacy of the method, and error analysis and proof is provided.
  4. M. Mascagni and S. A. Cuccaro (1992), "A Comparison of Modular Multiplication Across Parallel Supercomputing Architectures," Institute for Defense Analyses Supercomputing Research Center Technical Report: SRC-TR-92-116.  This paper compares the speed of integer modular multiplication modulo a Mersenne prime across supercomputing and special purpose computing systems.  This paper was classified after initial publication, and is no longer publicly available.

Abstracts:

  1. J. Tabak, M. Mascagni and R. Bertram (2007), "Spontaneous Episodic Activity: Why Episode Duration is Correlated with the Length of the Preceding but not Following Interval," Society for Neuroscience Abstracts, 33: 925.7.  This abstract presents results on using a simple stochastic model to replace a homogeneous integrate-and-fire network of excitatory neurons.  The results are based on correlation between of episode duration with the previous but not the following inter-episode interval.  The leads to a diagnostic for synaptic depression versus cellular adaptation.
  2. M. Mascagni (1987), "Computer Simulation of Negative Feedback in Neurons," Society for Neuroscience Abstracts, 13: 375.4.  This abstract presents results on the use of a Hodgkin-Huxley axon/dendrite model to study the effect of negative feedback on repetitive firing behavior of neurons.  It is empirically shown that negative feedback increases the input sensitivity of the repetitive firing response.

Software:

  1. M. Mascagni, A. Srinivasan, D. M. Ceperley, and F. Saied (1995), "Scalable Parallel Random Number Generators (SPRNG) Library."  This package has become the standard for parallel and distributed random number generation and was originally developed under DARPA Contract Number DABT63-95-C-0123 for ITO: Scalable Systems and Software, entitled A Scalable Pseudorandom Number Generation Library for Parallel Monte Carlo Computations at the University of Illinois at Champaign Urbana's National Center for Supercomputing Applications, the Institute for Defense Analyses' Center for Computing Sciences, and the University of Southern Mississippi's Doctoral Program in Scientific Computing.  This software continues to be supported by FSU and the U.S. Department of Energy, and is now distributed at the website: http://sprng.fsu.edu.

Invited Colloquia, Lectures, Proseminars and Seminars:

  1. The Institute of Informatics, National Research Grid Initiative (NAEGRI), Center for Grid Research and Development, Tokyo, Japan: Institute Seminar, 2008
  2. Weierstraß Institut für Angewandte Analysis und Stochastik (WIAS), Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany: Stochastic Algorithms Seminar, 2007
  3. Université de Toulon et du Var, Institut des Sciences de l'Ingénieur de Toulon et du Var, Modélisation Numérique et Couplages, Toulon, France: Mathematics Colloquium, 2007
  4. Université de Toulon et du Var, Institut des Sciences de l'Ingénieur de Toulon et du Var, Modélisation Numérique et Couplages, Toulon, France: Student Seminar, 2007
  5. Florida State University, Department of Mathematics, Tallahassee, FL: Mathematical Biology Seminar, 2007
  6. Florida State University, Department of Computer Science, Tallahassee, FL: Introduction to Research Seminar, 2007
  7. Florida State University, Department of Computer Science, Tallahassee, FL: Faculty Seminar, 2007
  8. Columbia University, The Fu Foundation School of Engineering and Applied Science, Department of Applied Physics and Mathematics, New York, NY: Applied Mathematics Seminar, 2007
  9. The University at Stony Brook, Department of Applied Mathematics and Statistics (AMS), Stony Brook, NY: AMS Seminar, 2007
  10. University of Florida, Department of Mathematics, Gainesville, FL: Applied Mathematics Colloquium, 2007
  11. North Carolina State University, Department of Mathematics, Raleigh, NC: Applied Mathematics Seminar, 2007
  12. North Carolina State University, Department of Computer Science, Raleigh, NC: Computer Science Seminar, 2007
  13. University of Florida, Department of Computer Information Sciences and Engineering (CISE), Gainesville, FL: Computer Science Colloquium, 2007
  14. Florida State University, School of Computational Science, Tallahassee, FL: Graduate Student Seminar, 2007
  15. Florida State University, Department of Computer Science, Tallahassee, FL: Introduction to Research Seminar, 2006
  16. Tulane University, Department of Mathematics, New Orleans, LA: Applied Mathematics Seminar, 2006
  17. Louisiana State University, Department of Mathematics, Baton Rouge, LA: Applied Analysis Seminar, 2006
  18. Louisiana State University, Center for Computation and Technology, Baton Rouge, LA: CCT Colloquium, 2006
  19. Florida Agricultural and Mechanical University, Department of Computer and Information Sciences, Tallahassee, FL: Departmental Colloquium, 2006
  20. Florida State University, Department of Computer Science, Tallahassee, FL: Faculty Research Presentation Series, 2006
  21. Naval Research Laboratory, Center for Computational Science, Washington, DC: Seminar, 2006
  22. Naval Research Laboratory, Electronic Support Measures, Washington, DC: Particles Research Group Seminar, 2006
  23. National Institute of Standards and Technology, Mathematics and Computational Science Division, Gaithersburg, MD: Seminar, 2006
  24. National Institutes of Health, Laboratory of Biological Modeling, National Institute for Diabetes, Digestive, and Kidney Diseases, Bethesda, MD: Seminar, 2006
  25. Technishe Universität Wien, Institut für Mikroelekronik, Vienna, Austria: Electrical Engineering Guest Lecture, 2006
  26. Katholieke Universiteit Leuven (Catholic University of Leuven), Department of Computer Science, Faculty of Engineering, Leuven, Belgium: Numerical Analysis Seminar, 2006
  27. Université Libre de Bruxelles (Free University of Brussels) , Service de Métrologie Nucléaire, Brussels, Belgium: Nuclear Engineering Colloquium, 2006
  28. Florida State University-Florida A&M University, Department of Chemical and Biomedical Engineering, Tallahassee, FL: Graduate Seminar, 2006
  29. Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Federal Institute of Technology, School of Life Sciences, Brain Mind Institute, Laboratory of Neural Microcircuitry, Lausanne, Switzerland: Laboratory Seminar, 2006
  30. Office of Naval Research, Global, London, United Kingdom: Office of Naval Research, Army Research Office, Air Force Office Scientific Research Seminar, 2006
  31. Herriot-Watt University, Department of Mathematics, Edinburgh, Scotland, United Kingdom: Industrial and Applied Mathematics Seminar, 2006
  32. Strathclyde University, Department of Mathematics, Glasgow, Scotland, United Kingdom: Numerical Analysis Colloquium, 2006
  33. Universität Ulm, Ulmer Zentrum für Wissenschafliches Rechnen (Ulm Center for Scientific Computing), Ulm, Germany: Forschungsseminar Wissenschaftliches Rechnen (Scientific Computing Research Seminar), 2006
  34. International Business Machines, Computational Chemistry and Materials Science Department, Zürich Research Laboratory, Rüschlikon, Switzerland: Deep Computing Institute Seminar, 2006
  35. Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Computational/Collaborational Laboratory in Computational Science and Engineering (CoLab), Zürich, Switzerland: CoLab Seminar, 2006
  36. Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Departement Mathematik, Seminar für Angewandte Mathematik (SAM), Zürich, Switzerland: SAM Colloquium, 2006
  37. Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Federal Institute of Technology, Chaire d'Analyse et Simulation Numériques, Institut d'Analyse et Calcul Scientifique, Lausanne, Switzerland: Colloque d'Analyse Numériques, 2006
  38. Conseil Européen pour la Recherche Nucléaire (CERN), Physics Department, Software Division and Information Technology Division, Geneva, Switzerland: CERN Computing Seminar, 2006
  39. Institut Supérieur d'Informatique, de Modélisation et de leurs Applications (ISIMA), Advanced Institute for Computer Science, Modeling and Applications, Université Blaise Pascal, Clermont-Ferrand, France: Modeling and Simulation Seminar, 2006
  40. The Georgia Institute of Technology, College of Computing, Division of Computational Science and Engineering, Atlanta, GA: Computational Science and Engineering Colloquium, 2006
  41. Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Department Informatik, Computer Science Department, Zürich, Switzerland: Theoretical Computer Science Seminar, 2006
  42. Geowatt AG, Swiss Expert Geothermal Group, Zürich, Switzerland: Company Seminar, 2006
  43. Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Rechnergestützte Wissenschaften, Computational Science and Engineering, Zürich, Switzerland: Case Studies Seminar in Computational Science and Engineering (Fallstudien), 2005
  44. Humboldt Universität, Department of Mathematics, Berlin, Germany: Numerical Analysis Seminar, 2005
  45. Weierstraß Institut für Angewandte Analysis und Stochastik (WIAS), Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany: Stochastic Algorithms Seminar, 2005
  46. National Institute of Standards and Technology, Mathematics and Computational Science Division, Gaithersburg, MD: Seminar, 2005
  47. National Institutes of Health, Laboratory of Biological Modeling, National Institute for Diabetes, Digestive, and Kidney Diseases, Bethesda, MD: Seminar, 2005
  48. Computational Science Center, Brookhaven National Laboratory, Brookhaven, NY: Computational Science Center Seminar, 2005
  49. The University at Stony Brook, Department of Applied Mathematics and Statistics (AMS), Stony Brook, NY: AMS Seminar, 2005
  50. University of Cyprus, Department of Computer Science, Nicosia, Cyprus: Computer Science Colloquium, 2005
  51. University of Cyprus, Department of Mathematics, Nicosia, Cyprus: Mathematics Colloquium, 2005
  52. University of Pittsburgh, Department of Mathematics, Pittsburgh, PA: Biological Mathematics Colloquium, 2004
  53. University of Miami, Department of Computer Science, Miami, FL: Applied Mathematics Colloquium, 2004
  54. Institut Supérieur d'Informatique, de Modélisation et de leurs Applications (ISIMA), Advanced Institute for Computer Science, Modeling and Applications, Clermont-Ferrand, France: Computer Science Seminar, 2004
  55. Université de Savoie, LAMA, Le Bourget-du-Lac, France: Applied Mathematics Colloquium, 2004
  56. Florida State University, Department of Computer Science, Tallahassee, FL: Introduction to Research Seminar, 2004
  57. Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia: Institute of Computational Mathematics and Mathematical Geophysics (Computing Center), Department of Statistical Modeling in Physics, Seminar on Monte Carlo Methods, 2004
  58. Florida State University-Florida A&M University, Department of Mechanical Engineering, Tallahassee, FL: Graduate Seminar, 2004
  59. Florida State University, Department of Mathematics, Tallahassee, FL: Mathematics Colloquium, 2004
  60. Arizona State University, Department of Mathematics, Tempe, AZ: Computational and Applied Mathematics Proseminar, 2003
  61. University of Arizona, Department of Mathematics, Tucson, AZ: Analysis and Its Applications Seminar, 2003
  62. National Institutes of Health, Laboratory of Biological Modeling, National Institute for Diabetes, Digestive, and Kidney Diseases, Bethesda, MD: Seminar, 2003
  63. National Institute of Standards and Technology, Mathematics and Computational Science Division, Gaithersburg, MD: Seminar, 2003
  64. Florida State University, Department of Computer Science, Tallahassee, FL: Introduction to Research Seminar, 2003
  65. Florida State University-Florida A&M University, Department of Chemical Engineering, Tallahassee, FL: Graduate Seminar, 2003
  66. Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Computational/Collaborational Laboratory in Computational Science and Engineering (CoLab), Zürich, Switzerland: CoLab Colloquium, 2003
  67. Seoul National University, Program in Computational Science and Technology, Seoul, Korea: Computer Science and Technology Colloquium, 2003
  68. Kunsan National University, Department of Mechanical Engineering, Kunsan, South Korea: Mechanical Engineering Colloquium, 2003
  69. Inha University, Department of Physics, Incheon, South Korea: Physics Colloquium, 2003
  70. Seoul National University, Department of Computer Science and Engineering, Seoul, Korea: Computer Science Colloquium, 2003
  71. Keio University, Faculty of Science and Technology, Department of Mathematics, Yokohama, Japan: Mathematics Colloquium, 2003
  72. Washington University in St. Louis, Center for Computational Biology, School of Medicine: Special Seminar, 2003
  73. University of California, San Diego, Computer Science and Engineering Department, San Diego, CA: Computer Science Seminar, 2003
  74. University of California, Los Angeles, Computer Science Department, Los Angeles, CA: Computer Science Seminar, 2003
  75. Oak Ridge National Laboratory, Computer Science and Mathematics Division, Oak Ridge, TN: Computer Science and Mathematics Seminar, 2003
  76. Universität Kaiserslautern, Fachbereich Informatik, Kaiserslautern, Germany: Computer Science Colloquium, 2002
  77. Johann Wolfgang Goethe Universität Frankfurt am Main, Department of Mathematics, Frankfurt am Main, Germany: Departmental Colloquium, 2002
  78. Universität Heidelberg, Intgerdisziplinäres Institut für wissenschaftliches Rechen (ITWR), Heidelberg, Germany: Scientific Computing Colloquium, 2002
  79. Florida State University, Department of Computer Science, Tallahassee, FL: Introduction to Research Seminar, 2002
  80. National Institutes of Health, Mathematical Research Branch, National Institute for Diabetes, Digestive, and Kidney Diseases, Bethesda, MD: Seminar, 2002
  81. Florida State University, School of Computational Science and Information Technology, Tallahassee, FL: Physical Sciences Colloquium, 2002
  82. FH Salzburg: Fachhochschulgesellschaft mbH, Salzburg University of Applied Sciences & Technology, School of Telecommunication Engineering, Salzburg, Austria: Colloquium, 2002
  83. Universität Salzburg, Institut für Scientific Computing, Salzburg, Austria: Scientific Computing Colloquium, 2002
  84. Università degli Studi di Bologna, Bologna, Italy: Seminario di Matematica, 2002
  85. Università degli Studi di Ferrara, Ferrara, Italy: Seminario di Analisi Numerica, 2002
  86. Università degli Studi di Ferrara, Ferrara, Italy: Seminario di Matematica, 2002
  87. Università degli Studi di Catania, Catania, Italy: Seminario di Analisi Numerica, 2002
  88. Università degli Studi di Catania, Catania, Italy: Seminario di Informatica, 2002
  89. Universität Salzburg, Institut für Mathematik, Salzburg, Austria: Mathematics Colloquium, 2002
  90. Technishe Universität Wien, Institut für Mikroelekronik, Vienna, Austria: Electrical Engineering Guest Lecture, 2002
  91. Université Libre de Bruxelles (Free University of Brussels), Service de Métrologie Nucléaire, Brussels, Belgium: Nuclear Engineering Colloquium, 2002
  92. Florida Atlantic University, Department of Physics, Boca Raton, FL: Physics Colloquium, 2002
  93. Florida State University, Department of Statistics, Tallahassee, FL: Statistics Colloquium, 2002
  94. Florida State University, Department of Computer Science, Tallahassee, FL: Computer Science Colloquium, 2002
  95. New York University, Courant Institute of Mathematical Sciences, New York, NY: Applied Mathematics Seminar, 2001
  96. National Institutes of Health, National Cancer Institute, Frederick Cancer Research and Development Center, Laboratory of Experimental and Computational Biology, Frederick, MD: Seminar, 2001
  97. National Institutes of Health, Mathematical Research Branch, National Institute for Diabetes, Digestive, and Kidney Diseases, Bethesda, MD: Seminar, 2001
  98. National Institute of Standards and Technology, Mathematics and Computational Science Division, Gaithersburg, MD: Seminar, 2001
  99. University of South Carolina, Department of Computer Science and Engineering, Columbia, SC: Computer Science and Engineering Colloquium, 2001
  100. University of South Carolina, Department of Computer Science and Engineering, Columbia, SC: Invited Lecture, Computational Science, 2001
  101. Emory University, Department of Mathematics and Computer Science, Atlanta, GA: Mathematics and Computer Science Colloquium, 2001
  102. Emory University, Department of Mathematics and Computer Science, Atlanta, GA: Computational Mathematics Seminar, 2001
  103. Florida State University, Department of Chemical Engineering, Tallahassee, FL: Chemical Engineering Colloquium, 2001
  104. Lawrence Livermore National Laboratory, Livermore, CA: Center for Applied Scientific Computing (CASC) Colloquium, 2001
  105. Lawrence Livermore National Laboratory, Livermore, CA: Monte Carlo Seminar (A-Division), 2001
  106. Lawrence Livermore National Laboratory, Livermore, CA: Internships in Terascale Simulation Technology (ITST) Lecture, 2001
  107. Bulgarian Academy of Sciences, Sofia, Bulgaria: Central Laboratory for Parallel Processing Colloquium, 2001
  108. Weierstraß Institut für Angewandte Analysis und Stochastik (WIAS), Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany: Stochastic Algorithms Seminar, 2001
  109. Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Zürich, Switzerland: Vector and Parallel Computing Colloquium, 2001
  110. Eidgenössische Technische Hochschule (ETH), Swiss Federal Institute of Technology, Zürich, Switzerland: Applied and Numerical Mathematics Colloquium, 2001
  111. Università degli Studi di Roma Una "La Sapienza", Rome, Italy: Seminario di Analisi Numerica, 2001
  112. Institut National de Recherche en Informatique et Automatique (INRIA), Sophia-Antipolis, France: Omega Project Seminar, 2001
  113. National Institute of Standards and Technology, Computational and Applied Mathematics, Gaithersburg, MD:  Seminar, 2001
  114. Oak Ridge National Laboratory, Computer Science and Mathematics Division, Oak Ridge, TN: Computer Science and Mathematics Seminar, 2001
  115. Florida State University, Center for Materials Research and Technology (MARTECH), Tallahassee, FL: Martech Seminar, 2001
  116. Florida State University, Department of Computer Science, Tallahassee, FL: Computer Science Colloquium, 2001
  117. Florida State University, Department of Mathematics, Tallahassee, FL: Mathematics Colloquium, 2001
  118. Florida State University, Department of Mathematics, Tallahassee, FL: Introduction to Research Seminar, 2001
  119. Florida State University, Department of Mathematics, Tallahassee, FL: Symbolic Computing Seminar, 2001
  120. Florida State University, Department of Mathematics, Tallahassee, FL: FSU Mathematics Society Seminar, 2001
  121. Florida State University, Department of Mathematics, Tallahassee, FL: Complex Analysis Seminar, 2001
  122. Florida State University, Department of Mathematics, Tallahassee, FL: Mathematics Colloquium, 2000
  123. Florida State University, Department of Mathematics, Tallahassee, FL: Cryptography Seminar, 2000
  124. University of California, Santa Barbara, Department of Computer Science, Santa Barbara, CA: Computer Science Colloquium, 2000
  125. University of Tokyo, Financial Engineering Department, Tokyo, Japan: Financial Engineering Seminar, 2000
  126. Universität Salzburg, Department of Computer Science, Salzburg, Austria: Computer Science Seminar, 2000
  127. Florida A&M University-Florida State University, Department of Electrical Engineering, Tallahassee, FL: Graduate Seminar, 2000
  128. Stetson University, Department of Mathematics, Deland, FL: Mathematics Colloquium, 1999
  129. Florida State University, Department of Mathematics, Tallahassee, FL: Mathematics Colloquium, 1999
  130. Florida State University, Department of Statistics, Tallahassee, FL: Statistics Colloquium, 1999
  131. Universität Salzburg, Institut für Mathematik, Salzburg, Austria: Mathematics Colloquium,1999
  132. Universität Salzburg, Institut für Mathematik, Salzburg, Austria: pLab Group Seminar, 1999
  133. Bettis Laboratory, West Mifflin, PA: Joint Bettis, KAPL, and Naval Reactors Seminar, 1999
  134. Bettis Laboratory, West Mifflin, PA: Reactor Methods and Programming Seminar, 1999
  135. Lawrence Berkeley National Laboratory, NERSC, Berkeley, CA: Scientific Computing Seminar, 1999
  136. University of Tennessee, Knoxville, TN: Innovative Computing Laboratory (Computer Science) Seminar, 1999
  137. Oak Ridge National Laboratory, Oak Ridge, TN: Center for Computational Science Seminar, 1999
  138. University of Michigan, Ann Arbor, MI: Nuclear Engineering and Radiological Sciences Colloquium, 1999
  139. University of Michigan, Ann Arbor, MI: Nuclear Engineering and Radiological Sciences Monte Carlo Seminar, 1999
  140. University of Texas, Austin, TX: Texas Institute for Computational and Applied Mathematics Seminar, 1999
  141. Florida State University, Tallahassee, FL: Computer Science Seminar, 1999
  142. Florida State University, Tallahassee, FL: Computational Science and Engineering Seminar, 1999
  143. NASA Langley Research Center, Hampton, VA: ICASE Colloquium, 1999
  144. Old Dominion University, Norfolk, VA: Computer Science Colloquium, 1999
  145. Mississippi State University, Starkville, MS: Mathematics Colloquium, 1998
  146. University of Mississippi, Oxford, MS: Mathematics Colloquium, 1998
  147. University of Mississippi, Oxford, MS: Computer Science Seminar, 1998
  148. Mississippi State University, Starkville, MS: Undergraduate Mathematics Seminar, 1998
  149. Tulane University, New Orleans, LA: Applied Mathematics Seminar, 1998
  150. Corps of Engineers Waterways Experiment Station, Vicksburg, MS: Information Technology Laboratory Colloquium, 1998
  151. NASA Jet Propulsion Laboratory, Pasadena, CA: Colloquium, 1998
  152. National Institute of Standards and Technology, Gaithersburg, MD: Information Technology Laboratory Seminar, 1998
  153. University of Illinois, Urbana-Champaign, IL: NCSA Colloquium, 1998
  154. University of Chicago, Chicago, IL: Computer Science Colloquium, 1998
  155. University of Kentucky, Lexington, KY: Computational Science Center Colloquium, 1998
  156. Sandia National Laboratory, Albuquerque, NM: Massively Parallel Computing Research Laboratory Seminar, 1997
  157. Rice University, Houston, TX: Center for Research in Parallel Computing Colloquium, 1997
  158. Mississippi State University, Starkville, MS: Graduate Student Seminar (Computer Science), 1997
  159. University of Utah, Salt Lake City, UT: Applied Mathematics Seminar, 1997
  160. Los Alamos National Laboratory, Los Alamos, NM: Monte Carlo Seminar, 1997
  161. Lawrence Livermore National Laboratory, Livermore, CA: Monte Carlo Seminar, 1997
  162. NASA Goddard Space Flight Center, Greenbelt, MD: CESDIS Colloquium, 1997
  163. Purdue University, West Lafayette, IN: Computer Sciences Colloquium, 1997
  164. Argonne National Laboratory, IL: Reactor Analysis Seminar, 1997
  165. University of Wisconsin, Madison, WI: Numerical Analysis Seminar, 1997
  166. University of Iowa, Iowa City, IA: Mathematics Colloquium, 1997
  167. Catholic University of America, Washington, DC: Mathematics Colloquium, 1996
  168. University of Southern Mississippi; Hattiesburg, MS: Scientific Computing Colloquium, 1996
  169. Lawrence Berkeley National Laboratory, Berkeley, CA: NERSC Colloquium, 1996
  170. Università degli Studi di Padova; Padova, Italy: Seminario di Analisi Numerica, 1996
  171. Università degli Studi di Milano; Milano, Italy: Seminario di Matematica, 1996
  172. Università degli Studi di Bologna; Bologna, Italy: Seminario di Matematica, 1996
  173. University of Wisconsin, Madison, WI: Numerical Analysis Seminar, 1996
  174. University of Illinois at Urbana-Champaign, Urbana, IL: Computer Science Colloquium, 1996
  175. San Francisco State University, San Francisco, CA: Mathematics Colloquium, 1996
  176. IBM-T. J. Watson Laboratory, Yorktown Heights, NY: Physical Sciences Seminar, 1996
  177. University of Maryland, College Park, MD: Numerical Analysis Seminar, 1996
  178. University of Michigan, Ann Arbor, MI: Special Nuclear Engineering Seminar, 1996
  179. University of Michigan, Ann Arbor, MI: Special Applied Mathematics Seminar, 1996
  180. University of Illinois at Urbana-Champaign, Urbana, IL: Computer Science Colloquium, 1996
  181. Johns Hopkins University, Baltimore, MD: Applied Mathematics Special Seminar, 1996
  182. Virginia Tech., Blacksburg, VA: Graduate Seminar Series, Department of Computer Science, 1995
  183. NASA Ames Research Center, Moffett Field, CA: NAS New Technology Seminar (Two Talks Given), 1995
  184. SUNY, Stony Brook, NY: Joint AMS/BNL Applied Mathematics Seminar, 1995
  185. Courant Institute of Mathematical Sciences-NYU, New York, NY: Modeling and Simulation Seminar, 1995
  186. Stanford University, Palo Alto, CA: Computer Science Colloquium, 1995
  187. Naval Research Laboratory, Washington, DC: Computational Neuroscience Seminar, 1995
  188. Catholic University of America, Washington, DC: Mathematics Colloquium, 1995
  189. University of Arizona, Tucson, AZ: Computer Science Colloquium, 1994
  190. Arizona State University, Tempe, AZ: Computational and Applied Mathematics Proseminar, 1994
  191. University of Illinois, Champaign, IL: Condensed Matter Physics Colloquium, 1994
  192. George Washington University, Washington, DC: Mathematics Department Colloquium, 1994
  193. National Institute of Standards and Technology, Gaithersburg, MD: Computational and Applied Mathematics Laboratory Seminar, 1994
  194. IDA Center for Communications Research-La Jolla, San Diego, CA: Colloquium, 1994
  195. Courant Institute of Mathematical Sciences-NYU, New York, NY: Numerical Analysis Seminar, 1994
  196. AT&T, Bell Laboratories, Murray Hill, NJ: Mathematics of Communications Division Colloquium, 1994
  197. Georgetown University, Washington, D.C.: Computer Science Colloquium, 1994
  198. NASA-Ames Research Center, Moffett Field, CA: NAS-RIACS Seminar, 1993
  199. Lawrence Livermore National Laboratory, Livermore, CA: Parallel Computing Seminar, 1993
  200. Lawrence Berkeley National Laboratory, Berkeley, CA: Parallel Computing Seminar, 1993
  201. The American University, Washington, DC: Mathematics Colloquium, 1993
  202. Baltimore-Washington Local SIAM Dinner, College Park, MD: Meeting Seminar, 1992
  203. Bell Communications Research, Morristown, NJ: Mathematics Colloquium, 1992
  204. Columbia University, New York, NY, Applied Physics: Colloquium, 1992
  205. IBM Corporation, Kingston, NY: Mathematical Sciences Seminar, 1992
  206. University of Maryland, Baltimore County, MD, Mathematics: Applied Mathematics Colloquium, 1992
  207. University of Southern California, Los Angeles, CA, Mathematics: Nonlinear Analysis Seminar, 1991
  208. University of Pittsburgh, Pittsburgh, PA, Mathematics: Numerical Analysis Seminar, 1991
  209. SUNY, Stony Brook, NY, Applied Mathematics: Computational Mathematics Seminar, 1991
  210. University of California, Los Angeles, CA, Computer Science: Seminar, 1991
  211. California Institute of Technology, Pasadena, CA, Applied Mathematics: Seminar, 1991
  212. Thinking Machines Corporation, Cambridge, MA: Guest Lecture, 1991
  213. BBN Inc., Cambridge, MA: Applied and Computational Mathematics Colloquium, 1991
  214. Brookhaven National Laboratory, Upton, NY, Mathematical Sciences: Seminar, 1991
  215. National Institute of Standards and Technology, Gaithersburg, MD: Computational and Applied Mathematics Seminar, 1991
  216. Naval Research Laboratory, Washington, DC: Acoustics Branch Seminar, 1990
  217. IBM-T. J. Watson Laboratory, Yorktown Heights, NY: Mathematical Sciences Seminar, 1990
  218. NASA-Ames Research Center, Moffett Field, CA: NAS-RIACS Seminar, 1990
  219. Yale University, New Haven, CT, Computer Science: Seminar, 1990
  220. John von Neumann National Supercomputer Center, Princeton, NJ: Colloquium, 1990
  221. University of Maryland, College Park, MD: Numerical Analysis Seminar, 1989
  222. Washington Area Connection Machine User's Group, Catholic University of America., Washington, DC: Meeting Seminar, 1989
  223. Courant Institute of Mathematical Sciences-NYU, New York, NY: Applied Mathematics Seminar, 1989
  224. Naval Research Laboratory, Washington, D.C., Connection Machine Facility: Seminar, 1989
  225. NASA-Goddard Space Flight Center, Greenbelt, MD: Seminar, 1989
  226. Courant Institute of Mathematical Sciences-NYU, New York, NY: Parallel Computation Seminar, 1988
  227. Division of Computer Research and Technology, NIH, Bethesda, MD: Seminar, 1988
  228. Supercomputing Research Center, Bowie, MD: Colloquium, 1988
  229. California Institute of Technology, Pasadena, CA, Computation and Neural Systems: Seminar, 1988
  230. Mathematical Research Branch, NIDDK, NIH, Bethesda, MD: Seminar, 1987
  231. National Cancer Institute, Frederick, MD: Mathematical Biology Seminar, 1987
  232. Tulane University, New Orleans, LA: Mathematics Colloquium, 1987
  233. Courant Institute of Mathematical Sciences-NYU, New York, NY: Numerical Analysis Seminar, 1987
  234. Hunter College, New York, NY: Computer Science Colloquium, 1987
  235. Courant Institute of Mathematical Sciences-NYU., New York, NY: Mathematical Biology Seminar, 1987

Invited Conference Presentations:

  1. FAME 2008: The Florida Annual Meeting and Exposition, sponsored by the Florida Section of the American Chemical Society, Orlando, FL: 30-minute invited in the Biophysics Symposium entitled: Novel Stochastic Methods in Biochemical Electrostatics. (May, 2008)
  2. Symposium: Applied Characterization of Random Number Generators and Related Topics, Institute of Statistical Mathematics , Tokyo, Japan, 30-minute invited talk entitled Random Number Generation: A Practitioner's Overview. (January, 2008)
  3. SC07: International Conference for High Performance Computing, Networking, Storage and Analysis, Educational Program, Reno, NV: 120-minute invited talk entitled Monte Carlo in Reno. (November, 2007)
  4. ICIAM 2007: The Sixth International Congress on Industrial and Applied Mathematics, Zürich, Switzerland: invited minisymposium Computational Science and Engineering (CSE) with membership in a 60-minute invited panel entitled: CSE has landed: who will give it a home and budget? (July, 2007)
  5. ICIAM 2007: The Sixth International Congress on Industrial and Applied Mathematics, Zürich, Switzerland: 30-minute invited minisymposium Stochastic Numerics: Monte-Carlo methods, SDEs, PDEs with a talk entitled: Recent Developments in the Scalable Parallel Random-Number Generators (SPRNG) Library. (July, 2007)
  6. ICIAM 2007: The Sixth International Congress on Industrial and Applied Mathematics, Zürich, Switzerland: 30-minute invited minisymposium Stochastic Numerics: Monte-Carlo methods, SDEs, PDEs with a talk entitled: Monte-Carlo Methods for Problems in Biological Electrostatics. (July, 2007)
  7. ICIAM 2007: The Sixth International Congress on Industrial and Applied Mathematics, Zürich, Switzerland: 30-minute invited minisymposium Computational Science and Engineering (CSE) with a talk entitled: Computational Science Education in the United States. (July, 2007)
  8. MCM2007:  The Sixth IMACS Seminar on Monte Carlo Methods, Reading University, Reading, UK: 60-minute invited talk entitled: Monte Carlo Methods for Partial Differential Equations . (June, 2007)
  9. Grid Computing Symposium, North Carolina Agricultural and Technical University, Greensboro, NC: 45-minute invited talk entitled: Grid Computing at FSU . (April, 2007)
  10. Workshop on Numerics for Stochastic Differential Equations and Application, School of Computational Science, Florida State University, Tallahassee, FL: 60-minute invited talk entitled: Using Simple SDEs (Stochastic Differential Equations) to Solve Complicated PDEs (Partial Differential Equations) . (September, 2005)
  11. Workshop on Computational Stochastic Differential Equations, The Mathematical Research and Conference Center, Institute of Mathematics, Polish Academy of Sciences, Będlewo (Poznań), Poland: 30-minute invited talk entitled: A Monte Carlo Method for Solving Boundary-Value Problems Arising in Continuum Molecular Electrostatics. (September, 2005)
  12. 17th IMACS World Congress, Scientific Computation, Applied Mathematics and Simulation, IMACS 2005, Paris, France: 25-minute invited talk in the Workshop on Large-Scale Linear Algebra Grid Computing entitled: A Bio-Inspired Job Scheduling Algorithm for Monte Carlo Applications on a Computational Grid. (July, 2005)
  13. 17th IMACS World Congress, Scientific Computation, Applied Mathematics and Simulation, IMACS 2005, Paris, France: 25-minute invited talk in the Workshop on Monte Carlo Methods for PDEs and Applications in Turbulence, Biochemistry, and Finance entitled (presented by co-author Nikolai Simonov): The Method of Random Walk on Spheres for Solving Boundary-Value problems for Molecular Electrostatics. (July, 2005)
  14. 17th IMACS World Congress, Scientific Computation, Applied Mathematics and Simulation, IMACS 2005, Paris, France: 25-minute invited talk in the Workshop on Monte Carlo Methods for PDEs and Applications in Turbulence, Biochemistry, and Finance entitled: Computational Investigation of Optimal Quasirandom Sequences in Numerical Finance. (July, 2005)
  15. Fifth International Conference on Computational Science: ICCS 2005, Emory University, Atlanta, GA: 25-minute invited talk in the Workshop on Parallel Monte Carlo Algorithms for Diverse Applications in a Distributed Setting entitled: An Efficient Monte Carlo Approach for Solving Linear Problems of Biomolecular Electrostatics. (May, 2005)
  16. Fifth International Conference on Computational Science: ICCS 2005, Emory University, Atlanta, GA: 25-minute invited talk in the Workshop on Parallel Monte Carlo Algorithms for Diverse Applications in a Distributed Setting entitled: On the Scrambled Soboĺ Sequence. (May, 2005)
  17. Society for Industrial and Applied Mathematics 2005 Conference on Computational Science and Engineering, Orlando, FL: 30-minute invited talk in the Minisymposium on Monte Carlo Computations in Biology and Materials Science entitled: Monte Carlo Methods in Biological Electrostatics. (February 2005)
  18. Society for Industrial and Applied Mathematics 2005 Conference on Computational Science and Engineering, Orlando, FL: 30-minute invited talk in the Minisymposium on Critical Issues in the Application of Multi-scale Techniques to Computational Nanotechnology entitled (presented by co-author Ashok Srinivasan): Continuum Molecular Electrostatics via Monte Carlo Methods. (February 2005)
  19. American Mathematical Society 2004 Spring Southeastern Section Meeting, Florida State University, Tallahassee, FL: 30-minute invited talk in the Special Session on Application of Mathematics to Problems in Biology entitled: Monte Carlo Methods for Calculating Some Physical Properties of a Large Molecule. (March, 2004)
  20. Seventh Joint Conference on Information Sciences (JCIS 2003)/Seventh International Conference on Computer Science and Informatics, Research Triangle Park, NC: 40-minute invited talk entitled: Quasi-Monte Carlo Methods for Some Problems in Linear Algebra. (September, 2003)
  21. Fourth IMACS Seminar on Monte Carlo Methods, Berlin, Germany: 20-minute invited talk entitled: Grid-based Quasi-Monte Carlo Applications. (September, 2003)
  22. Fourth IMACS Seminar on Monte Carlo Methods, Berlin, Germany: 20-minute invited talk entitled (presented by co-author Nikolai Simonov): Random Walk Algorithms for the Estimation of Effective Properties for Digitized Porous Media. (September, 2003)
  23. Fourth IMACS Seminar on Monte Carlo Methods, Berlin, Germany: 20-minute invited talk entitled: On the Scrambled Halton Sequence. (September, 2003)
  24. Fourth IMACS Seminar on Monte Carlo Methods, Berlin, Germany: 20-minute invited talk entitled (presented by co-author Aneta Karaivanova): Parallel Quasirandom Walks on the Boundary. (September, 2003)
  25. Fourth IMACS Seminar on Monte Carlo Methods, Berlin, Germany: 20-minute invited talk entitled (presented by co-author Abdujabar Rasulov): Branching Random Walks Using Quasirandom Sequences.  Is That Possible? (September, 2003)
  26. Fifth International Congress on Industrial and Applied Mathematics (ICIAM 2003), Sydney, Australia: 30-minute invited minisymposium talk entitled:  Computing the Capacitance of the Unit Cube to High Accuracy. (July, 2003)
  27. Fifth International Congress on Industrial and Applied Mathematics (ICIAM 2003), Sydney, Australia: 30-minute invited minisymposium talk entitled:  Monte Carlo Methods for Computing Electrostatic Internal Energies of Large Molecules. (July, 2003)
  28. Fourth International Conference on Large-Scale Scientific Computations (LSSC'03), Sozopol, Bulgaria: 50-minute invited plenary talk entitled:  Computational Infrastructure for Parallel, Distributed, and Grid-based Monte Carlo Computations. (June, 2003)
  29. Algorithms and Complexity for Continuous Problems: Schloss Dagstuhl International Conference and Research Center for Computer Science, Dagstuhl, Germany:  25-minute invited talk (presented by co-author Emanouil Atanassov) entitled Efficient Generation of Low Discrepancy Sequences. (September 2002)
  30. Algorithms and Complexity for Continuous Problems: Schloss Dagstuhl International Conference and Research Center for Computer Science,, Dagstuhl, Germany:  25-minute invited talk (presented by co-author Aneta Karaivanova) entitled Quasi-Monte Carlo Methods for Some Linear Algebra Problems, Convergence and Complexity. (September 2002)
  31. Fifth International Conference on Numerical Methods and Applications (MN&A 02), Borovets, Bulgaria; 45-minute invited plenary talk entitled Stochastic Methods for Partial Differential Equations: Avoiding Complicated Deterministic Constructs in Applications. (August 2002)
  32. Institute for Mathematics and It's Applications, Foundations of Computational Mathematics 2002, University of Minnesota, Minneapolis, MN; 50-minute invited semiplenary talk entitled Stochastic Methods for Partial Differential Equations: Theory and Applications. (August 2002)
  33. Centre de Recherché Mathematiques: Workshop on Random Number Generation and Highly Uniform Uniform Point Sets, Université de Montréal, Québec, Canada: 60-minute invited talk entitled Random Number Requirements of Large Monte Carlo Applications: A Developer's Perspective. (June 2002)
  34. Scalable Parallel Random Number Generators (SPRNG) Workshop, Sandia National Laboratory, Albuquerque, NM: a 120-minute invited  talk entitled Recent Developments and Future Plans for the Scalable Parallel Random Number Generators (SPRNG) Library. (February, 2002)
  35. Third IMACS Seminar on Monte Carlo Methods, Salzburg, Austria: 30-minute invited talk entitled A Feynman-Kac Formula Implementation for the Linearized Poisson-Boltzmann Equation.  (September, 2001)
  36. European Conference on Numerical Mathematics Advanced Applications 2001, Ischia Porto, Naples, Italy: 25-minute invited minisymposium talk entitled Continuous Path Brownian Trajectories for Diffusion Monte Carlo Via First- and Last-Passage Distributions, (July 2001)
  37. European Conference on Numerical Mathematics Advanced Applications 2001, Ischia Porto, Naples, Italy: 25-minute invited minisymposium talk entitled SPRNG: A Scalable Library for Pseudorandom Number Generation, (July 2001)
  38. European Conference on Numerical Mathematics Advanced Applications 2001, Ischia Porto, Naples, Italy: 25-minute invited minisymposium talk entitled Feynman-Kac Path-Integral Implementation for Poisson's Equation Using an F-conditioned Green's Function, presented by C.-O. Hwang, (July 2001)
  39. Third International Conference on Large-Scale Scientific Computations, Sozopol, Bulgaria: 30-minute invited talk entitled Continuous Path Brownian Trajectories for Diffusion Monte Carlo Via First- and Last-Passage Distributions, (June 2001)
  40. The 2001 International Conference on Computational Science, San Francisco, CA: 20-minute invited minisymposium talk entitled A Feynman-Kac Path-Integral Implementation for Poisson's Equation, presented by C.-O. Hwang, (May 2001)
  41. Journées savoisiennes de mathématiques appliquées, Methodes Particulaires de Simulation Numerique (Particle Methods for Numerical Simulation), Université de Savoie, Le Bourget-du-Lac, France: 60-minute invited talk entitled New Monte Carlo Methods for Problems in Materials and Biology. (May, 2001)
  42. The Fourth International Conference on Supercomputing in Nuclear Applications, Toranoman-Pastoral, Tokyo, Japan: 35-minute invited talk entitled Theory and Software for Parallel Random Number Generation. (September, 2000)
  43. Numerical Methods and Applications, Sofia, Bulgaria: 35-minute Invited Talk entitled SPRNG: A Scalable Library for Pseudorandom Number Generation. (July 1998)
  44. NATO Advanced Study Institute: Quantum Monte Carlo Methods in Physics and Chemistry, Cornell University, Ithaca, NY: 90-minute talk entitled Serial and Parallel Random Number Generation: Theory and Practice. (June 1998)
  45. American Nuclear Society Mathematics and Computation Division, American Nuclear Society Annual Meeting, Nashville, TN: 90-minute roundtable entitled Current Issues in Computational Methods. (June 1998)
  46. Programming Environment and Training Workshop entitled "High-Performance Monte Carlo Tools," Stennis Space Center, MS: 45-minute invited talk entitled Future Trends in Random Number Generation. (April 1998)
  47. Institute for Mathematics and Its Applications, Special Year in High Performance Computing: Workshop on Algorithms for Parallel Processing, University of Minnesota, Minneapolis, MN: 60-minute talk entitled A Scalable Library for Pseudorandom Number Generation: Theory and Practice. (September, 1996)
  48. Centre de Recherché Mathematiques: Workshop on Pseudorandom Number Generation, Université de Montréal, Québec, Canada; 90-minute talk entitled A Scalable Library for Pseudorandom Number Generation: Theory and Practice. (June 1996)
  49. DARPA/ITO Computing Systems and Software Principal Investigator's Meeting, San Antonio, TX; 10-minute talk entitled Scalable Pseudorandom Number Generation Tools for Monte Carlo Computations. (March 1996)
  50. Mathematical Sciences Institute Workshop on Stochastic Modeling, The University at Stony Brook, NY; 30-minute invited talk entitled A Gradient Random Walk Method for Two-Dimensional Reaction-Diffusion Equations. (January 1995)
  51. Argonne Theory Institute; Parallel Monte Carlo Simulation: Issues, Tools, and Techniques; Argonne National Laboratory, IL: 2-hour invited talk entitled Parallel Monte Carlo Methods for Partial Differential Equations. (June 1990)
  52. West Virginia University Conference on Computational Research on Materials, Morgantown, WV, invited talk entitled Techniques of Parallel Processing: The Need for New Algorithms. (March 1990)
  53. AMS/IMS/SIAM Conference on "Statistical Multiple Integration'', Arcata, CA: invited talk entitled Random Walks, Elliptic Equations, and Massively Parallel Computing. (June 1989)
  54. SIGGRAPH `88, Atlanta, GA; invited talk to the International Iris User Forum on Scientific Visualization entitled Simulation of Networks of Neurons and scientific images contributed to Scientific Visualization, the National Center for Supercomputing Applications' contribution to the SIGGRAPH `88 Video and Animation Show. (August 1988)

Contributed Conference Presentations:

  1. SIAM Conference on Parallel Processing for Scientific Computing, Atlanta, GA: minisymposium entitled Parallel Stochastic Methods in Computational Biology, 30-minute contributed talk entitled Novel Stochastic Methods in Biochemical Electrostatics. (March, 2008)
  2. SIAM Conference on Parallel Processing for Scientific Computing, Atlanta, GA: minisymposium entitled Parallel Stochastic Methods in Computational Biology, 30-minute contributed talk entitled Estimation of Migration Rates and Effective Population Numbers by Using Importance Sampling, presented for authors Hongmei Chi and Peter Beerli. (March, 2008)
  3. SIAM Conference on Parallel Processing for Scientific Computing, Atlanta, GA: poster entitled The Scalable Parallel Random Number Generators (SPRNG) Library Version 4.0, with co-author Jane Ren. (March, 2008)
  4. SC07: International Conference for High Performance Computing, Networking, Storage and Analysis, Technical Program, Reno, NV: poster entitled Decentralized Replica Exchange Parallel Tempering: An Efficient Implementation of Parallel Tempering using MPI and SPRNG, with co-authors Yaohang Li and Andrey Gorin. (November, 2007)
  5. Society for Neuroscience 2007 Annual Meeting, Neuroscience 2007, San Diego, CA: presentation 925.7, poster entitled: Spontaneous Episodic Activity: Why Episode Duration is Correlated with the Length of the Preceding but not Following Interval, with co-authors Joël Tabak and Richard Bertram. (November, 2007)
  6. MCM2007:  The Sixth IMACS Seminar on Monte Carlo Methods, Reading University, Reading, UK: 30-minute contributed talk (presented by co-author Bart Vandewoestyne) entitled: An Empirical Investigation of Different Scrambling Methods for Faure Sequences. (June, 2007)
  7. MCM2007:  The Sixth IMACS Seminar on Monte Carlo Methods, Reading University, Reading, UK: 30-minute contributed talk entitled: Monte Carlo Methods for Calculating Coefficient Dependence in Poisson-Boltzmann Problems. (June, 2007)
  8. MCM2007:  The Sixth IMACS Seminar on Monte Carlo Methods, Reading University, Reading, UK: 30-minute contributed talk on behalf of Wesley Petersen entitled: Playing with Parallelism with Playstations. (June, 2007)
  9. GFMC40:  A Symposium Celebrating 40 Years of Green's Function Monte Carlo, Courant Institute of Mathematical Sciences, New York University, New York, NY: 15-minute contributed talk entitled: Monte Carlo Methods for Partial Differential Equations . (May, 2007)
  10. Applications of Mathematics in Biology, Physiology, and Medicine, Courant Institute of Mathematical Sciences, New York University, New York, NY: 30-minute contributed talk entitled: Using Simple SDEs (Stochastic Differential Equations) to Solve Complicated PDEs (Partial Differential Equations) . (October, 2006)
  11. Schweizer Numerik Kolloquium/Colloque Numérique Suisse 2006, Ecole Polytechnique Fédérale de Lausanne (EPFL), Swiss Federal Institute of Technology, Chaire d'Analyse et Simulation Numériques, Institut d'Analyse et Calcul Scientifique, Lausanne, Switzerland: contributed poster entitled: Monte Carlo Methods for Partial Differential Equations: Computing Permeability. (April, 2006)
  12. International Conference on Differential Equations: From Theory to Computational Science and Engineering, Eidgenössische Technische Hochschule (ETH Zürich), Swiss Federal Institute of Technology, Zürich, Switzerland: 30-minute contributed talk entitled: Stochastic Method for Elliptic Problems: Applications to Biological and Materials Science.  (October, 2005)
  13. Fifth IMACS Seminar on Monte Carlo Methods: MCM2005, Florida State University, Tallahassee, FL: 25-minute contributed talk (presented by co-author Hongmei Chi) entitled: Combined LCGs with Sophie-Germain Moduli.  (May, 2005)
  14. Fifth IMACS Seminar on Monte Carlo Methods: MCM2005, Florida State University, Tallahassee, FL: 25-minute contributed talk (presented by co-author Yaohang Li) entitled: Test of the Rule 30 Cellular Automata Random Number Generator.  (May, 2005)
  15. Fifth IMACS Seminar on Monte Carlo Methods: MCM2005, Florida State University, Tallahassee, FL: 25-minute contributed talk (presented by co-author Abdujabor Rasulov) entitled: Monte Carlo Solution of Initial Boundary Problem for Some Nonlinear Parabolic Equations.  (May, 2005)
  16. Fifth IMACS Seminar on Monte Carlo Methods: MCM2005, Florida State University, Tallahassee, FL: 25-minute contributed talk entitled: Monte Carlo Applications on the Computational Grid.  (May, 2005)
  17. Fifth IMACS Seminar on Monte Carlo Methods: MCM2005, Florida State University, Tallahassee, FL: 25-minute contributed talk (presented by co-author Nikolai Simonov) entitled: Random Walk Algorithms for Solving some Boundary-Value Problems in Biomolecular Electrostatics.  (May, 2005)
  18. Algorithmes et Applications Paralleles en Algebre Linear (Parallel Matrix Algorithms and Applications): PMAA04, Centre International de Rencontres Mathématiques (CIRM), Luminy, France: 25-minute contributed talk entitled: A Monte Carlo Scheme for Load Balancing.  (October, 2004)
  19. International Association for the Developme