Extracting Implicit Social Information from the Space Around Us

Generative AI for Graduate Students & Post Docs

Self-Defending Ledgers: Automating Distributed Ledger Security Using Multi-Agent Reinforcement Learning and Game Theory

Md Tariqul Islam 'Pavel'
Assistant Professor of Cybersecurity
UMBC Department of Information Systems

12–1pm ET, Wednesday, December 3, 2025
UMBC ITE 459 and online via Webex

Distributed ledger technologies (DLTs) continue to face significant security challenges. While attackers constantly adapt their strategies, governance mechanisms often remain static. Our work addresses this critical gap by introducing a framework for self-defending ledgers, where nodes enforce ledger security through adaptive governance driven by multi-agent reinforcement learning (MARL) grounded in game-theoretic principles. We model DLT consensus as a repeated Bayesian game, in which participants hold probabilistic beliefs about peer behavior, allowing agents to make strategic decisions under partial observability of adversarial actions. Our framework enables nodes to model, detect, and respond to a wide range of malicious behaviors, including bribery, selfish mining, equivocation, Sybil attacks, and collusive voting, by continuously updating Bayesian trust beliefs and governance policies based on network observations. We formally prove that networks with an honest majority reach stable equilibria and provide bounds on adversarial influence. Experiments across five major protocols show that agents effectively identify attacks with high accuracy while substantially reducing adversarial success. This work demonstrates the potential of game-theoretic MARL to provide robust, self-adaptive security in varied DLT environments, paving the way for resilient and autonomous ledger governance.

Md Tariqul Islam 'Pavel' is an assistant professor of cybersecurity in UMBC's Department of Information Systems. His research centers on the security, efficiency, and fault-tolerance of distributed computing systems, with a strong emphasis on blockchain, cloud, and vehicular networks. He develops formal models, algorithms, and protocols that address critical vulnerabilities in decentralized ecosystems, spanning inter-blockchain communication, smart contract migration, and trustworthy governance. His work combines cryptography, game theory, and system design to build scalable, resilient infrastructures. He earned his PhD and MS from the University of Kentucky and BS from the University of Dhaka, Bangladesh.

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On Language Models and the Dangers of an Algorithmic View From Nowhere

Dr. John Bai, University of Canterbury

2-3:00pm EST Monday, December 1, 2025, online

Large language models (LLMs) are stochastic text generators, trained using large scale, web-scrapped datasets. Uncritically accepting LLM outputs as "truthful" or "objective" risks perpetuating an algorithmic "view from nowhere", which hides the many human decisions involved in developing, training, and fine-tuning LLMs. This talk aims to demystify language modelling and support critical, de-colonial analyses of (human and artificial) knowledge systems.

Dr. John Bai works as a Senior Lecturer in the School of Psychology, Speech and Hearing at the University of Canterbury. He completed a PhD in Psychology at the University of Auckland and a Postdoc in the Department of Educational Sciences at the Carl von Ossietzky Universität Oldenburg. His research on artificial intelligence in education takes a collaborative, interdisciplinary approach to examine educators' perspectives on the possible futures of education.

This webinar is part of a series on Decentering Intercultural Competence Research and Practice, organized by the Intercultural Competence Research and Practice Working Group (the World Council on Intercultural and Global Competence) in collaboration with the UMBC Center for Intercultural Research and Practice.

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Recording

Prof. Houbing Song, UMBC

11:15-12:45 EST, Tue Nov 25, 2025, UMBC ITE 325b and online

The Computing Research Association (CRA) hosted its 2025 CRA Summit on July 28-31, 2025. Over the course of the week, attendees from academia, industry, and government explored varied perspectives on how artificial intelligence (AI)  is reshaping computing research, undergraduate and graduate education, and workforce development.

The CRA has now released a report summarizes the key discussions and takeaways from the two core academic convenings of the 2025 CRA Summit: the 2025 CRA Summit on AI Undergraduate Education and CRA Summit on AI Graduate Education and Research. 

You can read a short summary of the report and get the full 78 page report here.