UMBC Distinguished Speaker

From Social Media Mining to Generative AI: Asking and Solving Challenging Problems

Huan Liu, Arizona State University

Wednesday, Feb. 5, 12-1pm EST, ITE 459, UMBC & online

In this talk, we will present emerging opportunities in AI and Big Data, through the lens of social media. Generative AI, in particular, Large Language Models (LLMs), has added new challenges. We use examples to illustrate (1) fundamental problems associated with multi-modal data like social media, challenging common practice and existential understanding in machine learning and data mining, (2) how we embrace the power of LLMs to solve perplexing problems, and (3) developing novel algorithms for responsible LLMs. Seeking interdisciplinary collaborations, we contemplate the promising future of data science and data mining in the rapid development of AI.

Dr. Huan Liu is a Regents Professor and Ira A. Fulton professor of Computer Science and Engineering at Arizona State University. He is the recipient of the ACM SIGKDD 2022 Innovation Award for his outstanding contributions to the foundation, principles, and applications of social media mining and feature selection for data Mining. He co-authored the textbook, Social Media Mining: An Introduction, by Cambridge University Press. He is Editor in Chief of ACM TIST, Founding Field Chief Editor of Frontiers in Big Data, its Specialty Chief Editor of Data Mining and Management, and a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is a Fellow of ACM, AAAI, AAAS, and IEEE.

Securing Distributed Networks: Leveraging Reinforcement Learning and Game Theory for Attack Detection and Mitigation

Dr. Md Tariqul Islam, Syracuse University

10-11am January 30, 2025;  ITE 459, UMBC and online

Reinforcement learning (RL) has demonstrated remarkable success across diverse domains, from mastering complex games to optimizing real-time feedback systems in robotics and industrial control. However, its potential in cybersecurity, particularly for autonomous attack detection and mitigation in distributed systems, remains largely underexplored. Traditional single-agent RL approaches struggle in decentralized environments where multiple entities make independent decisions, necessitating multi-agent reinforcement learning (MARL). Our research explores blockchain networks as an ideal test case due to their decentralized architecture and trustless consensus mechanisms. We developed a novel MARL-based consensus mechanism for Proof-of-Stake blockchains, enabling nodes to collaboratively identify and penalize malicious behavior while preserving decentralization. This approach effectively mitigated six major blockchain attack types with minimal computational overhead. Building on these results, we propose integrating game-theoretic principles into the MARL framework to model adversarial strategies and enhance system resilience. The synergy between reinforcement learning and game theory establishes a robust foundation for dynamic and adaptive security in distributed systems, effectively addressing current vulnerabilities while anticipating and countering future threats. This integrated approach enables the design of resilient, scalable defense mechanisms tailored to the complex dynamics of decentralized architectures.

Introduction to Embedded Systems and Robotics, A Practical Guide

Nayan Kakoty, Rupam Goswami and Ramana Vinjamuri

UMBC CSEE professor Ramana Vinjamuri is one of the authors of a new book, Introduction to Embedded Systems and Robotics, published by Springer in December 2024. It is a technical guide to the fundamentals of embedded systems and robotics and their application to practical problems.

The book is designed to be appropriate for both undergraduate and graduate students and serve as a practical guide for doing robotics projects in their final semesters. It is written to cover the “ABC of embedded systems and robotics: A for acquiring the concepts, B for building robotic systems, and C for creating solutions.” It:

• Includes solved problems at the end of each chapter
• Features projects, such as developing a robotic hand and developing a biomimetic prosthetic finger
• Introduces robotics to undergraduate and graduate scholars using plain language and with the aid of practical problems