Events

Securing Computer Systems using AI Methods and for AI Applications

Published: | 11:38 am | Posted in: Events | Leave a comment

Speaker: Mulong Luo

Date: Jan 31, 11:45am-12:45pm

Abstract: Securing modern computer systems against an ever-evolving threat landscape is a significant challenge that requires innovative approaches. Recent developments in artificial intelligence (AI), such as large language models (LLMs) and reinforcement learning (RL), have achieved unprecedented success in everyday applications.

Secure and Timely Execution in Cyber-physical Systems

Published: | 12:16 pm | Posted in: Events | Leave a comment

Speaker: Jinwen Wang
Date: Jan 15, 11:45am-12:45pm
Abstract: Cyber-physical systems (CPSs), such as self-driving cars, are integral to modern life. Execution timing is critical to CPS, as missing a deadline can result in severe outcomes. In this talk, I will present my work on guaranteeing execution timing in CPS security.

Towards Secure and Resilient Real-Time Cyber-Physical Systems

Published: | 11:12 am | Posted in: Events

Speaker: Abdullah Al Arafat Date: Jan 13, 11:45am-12:45pm Abstract: Recent advances in sensing, communication, and computing have revolutionized the accessibility and integration of safety-critical Cyber-Physical Systems (CPS). However, these systems face stringent timing constraints and escalating security challenges.

Towards Efficient and Practical Privacy-Preserving Computing and AI

Published: | 10:03 pm | Posted in: Events

Speaker: Qian Lou Date: Nov 22, 2:05pm – 3:15pm Abstract: With growing reliance on cloud-based Machine Learning as a Service (MLaaS), privacy concerns are escalating, particularly in fields like healthcare and finance. For instance, clinicians and researchers use AI models to analyze electronic health records for insights into conditions such as depression and Alzheimer’s disease. […]

Incredible Yet Limited Large Language Models in the Wild

Published: | 3:43 pm | Posted in: Events

Speaker: Hanjie Chen Date: Nov 8, 2:05pm – 3:15pm Abstract: Large language models (LLMs) demonstrate remarkable capabilities in handling a wide range of tasks, from generating human-like text to answering complex questions. Despite achieving remarkable performance on existing benchmarks, the effectiveness and limitations of LLMs in realistic scenarios remainlargely underexplored. In this talk, I will […]

Responsible Graph Machine Learning Under a Fairness Lens

Published: | 4:30 pm | Posted in: Events

Speaker: Yushun Dong Date: Nov 1, 2:15 – 3:05 pm Abstract: Graph learning algorithms have been increasingly deployed in a plethora of real-world applications, such as epidemic analysis, healthcare, and financial analysis. Nevertheless, there has been a rise in societal concerns about the algorithmic bias that these algorithms may exhibit. In certain high-stakes applications of […]

The Age of Creative Machines: Their Rise, Impact, and Vision for the Future

Published: | 4:24 pm | Posted in: Events

Speaker: Maya Ackerman Date: Oct 25, 2:15 – 3:05 pm Abstract: What happens when machines become creative? How did creative machines evolve from their beginnings in academia to their industry proliferation? While this shift has sparked excitement, it has also introduced significant challenges, from copyright disputes to the spread of misinformation. What lies at the […]

Harnessing Explainable, Equitable, and Actionable Informatics and AI to Improve Health

Published: | 4:16 pm | Posted in: Events

Speaker: Zhe He Date: Oct 18, 2:15 – 3:05 pm Abstract: Over the past two decades, data science and artificial intelligence (AI) have significantly transformed biomedical research and healthcare. The growing availability of large-scale data, such as electronic health records (EHRs), combined with advancements in computational power, has unlocked new opportunities to address complex challenges […]

Graph Representation Learning for Network Generation, Optimization, and Verbalization

Published: | 4:11 pm | Posted in: Events

Speaker: Liang Zhao Date: Oct 4, 2:05pm – 3:15pm Abstract: Graphs are ubiquitous data structure that denotes entities and their relations, such as social networks, citation graphs, and neural networks. The topology of graphs is discrete data which prevents it from enjoying numerous mathematical and statistical tools that requires structured data. Graph representation learning aims […]