Mine of the Future: Machine Learning and Cybersecurity for Situation-awareness and Safety (Feb 16)

Date: Feb 16, 11:15 – 12:15 pm

Abstract:

Mining industry is rapidly transforming into an AI driven cyber-physical ecosystem where safety and operational reliability depend on robust perception and trustworthy distributed intelligence and continuous monitoring of miners and the environment. Real-world mining environments impose severe constraints like poor illumination, dust, occlusion, GPS-denied conditions, irregular underground topologies, and intermittent connectivity. These factors degrade perception quality, disrupt situational awareness, impair miner trajectory prediction and weaken the reliability of distributed learning systems. Ensuring safety and security in mining requires addressing a broad set of challenges spanning sensing, perception, communication, mobility prediction, automation, and distributed intelligence. Smart learning systems must operate reliably across underground and surface mines, yet the environment imposes severe physical, operational and adversarial constraints. Emerging cyber-physical threats, including backdoor triggers, sensor spoofing, label-flip attacks and poisoned model updates, further jeopardize operational safety, particularly as mines increasingly adopt autonomous vehicles, humanoid assistance, and federated learning for collaborative intelligence. This talk presents a vision for a Unified Smart Safety and Security Architecture that integrates multimodal perception, spatial-temporal modeling, secure federated learning into a cohesive safety fabric. It will discuss some of the solutions proposed for multimodal situational awareness, backdoor attack monitor, IoT-driven equipment health monitoring, hazard understanding, model integrity, and federated learning robustness. By unifying these components, this work outlines a comprehensive research vision for building a futuristic, resilient, proactive and trustworthy intelligent mining system capable of safeguarding miners and maintaining operational continuity under extreme and adversarial conditions.

Biographical Sketch

Sanjay K Madria is a Curators’ Distinguished Professor in the Department of Computer Science at the Missouri University of Science and Technology (formerly, University of Missouri-Rolla, USA). He has published over 300 Journal and conference papers in the areas of mobile and sensor computing, big data and cloud computing, data analytics and cybersecurity. He won five IEEE best papers awards in conferences such as IEEE MDM and IEEE SRDS. He is a co-author of a book (published with his two PhD graduates) on Secure Sensor Cloud published by Morgan and Claypool in Dec. 2018. He has graduated 22 PhDs (incl. of 2 women and 2 African American students) and 34 MS thesis students, with 12 current PhDs. NSF, NIST, ARL, ARO, AFRL, DOE, Boeing, CDC-NIOSH, ORNL, Honeywell, and others have funded his research projects of over $28M. He has been awarded JSPS (Japanese Society for Promotion of Science) invitational visiting scientist fellowship, and ASEE (American Society of Engineering Education) fellowship. In 2012 and in 2019, he was awarded NRC Fellowship by National Academies, US. He is ACM Distinguished Scientist and served as an ACM and IEEE Distinguished Speaker He is an IEEE Senior Member as well as IEEE Golden Core Awardee.

Location: LOV 353 and ZOOM