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<NewsItem contentIssues="false" id="155286" important="false" status="posted" url="https://my3.my.umbc.edu/groups/umbc-ai/posts/155286">
<Title>The Future is Here Already &#8211; AI Agents for Enterprise Productivity</Title>
<Tagline>Video of GRIT-X talk by UMBC alumna Gargi Banerjee Dasgupta</Tagline>
<Body>
<![CDATA[
    <div class="html-content"><div>One of the speakers from the <a href="https://research.umbc.edu/grit-x/" rel="nofollow external" class="bo"><strong>2025 Fall GRIT-X event</strong></a> was CSEE alumna <a href="https://my3.my.umbc.edu/groups/umbc-ai/posts/152852/a6c/470fdad1997414dad2a54bff36351a9a/web/link?link=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fgargi-banerjee-dasgupta-4509a61%2F" rel="nofollow external" class="bo"><strong>Gargi Banerjee Dasgupta</strong></a>, who received a Ph.D. in Computer Science in 2003. She is currently the Director of Product Management and AI Leader at IBM, responsible for the technical strategy of <a href="https://www.ibm.com/think/topics/ai-agents" rel="nofollow external" class="bo"><strong>AI assistants built on WatsonX</strong></a>. Previously, she served as the Chief AI Officer for IBM Automation. </div><div><br></div><div>View her ten-minute GRIT-X talk on AI agents here: <a href="https://www.youtube.com/watch?v=3VFXN9nXSCw" rel="nofollow external" class="bo"><strong>The Future is Here Already – AI Agents for Enterprise Productivity</strong></a></div><div><br></div><div>This <a href="https://research.umbc.edu/grit-x/" rel="nofollow external" class="bo"><strong>page</strong></a> has links to Youtube recordings for each of the GRIT-X presentations since 2016.</div><div><br></div></div>
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<Summary>One of the speakers from the 2025 Fall GRIT-X event was CSEE alumna Gargi Banerjee Dasgupta, who received a Ph.D. in Computer Science in 2003. She is currently the Director of Product Management...</Summary>
<Website>https://www.youtube.com/watch?v=3VFXN9nXSCw</Website>
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<PostedAt>Mon, 15 Dec 2025 17:24:51 -0500</PostedAt>
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<NewsItem contentIssues="false" id="154923" important="false" status="posted" url="https://my3.my.umbc.edu/groups/umbc-ai/posts/154923">
<Title>Talk: Distributed Ledger Security Using Multi-Agent Reinforcement Learning and Game Theory, 12/3</Title>
<Tagline>12-1pm ET Wednesday, Dec. 3 in UMBC iITE 459 and online</Tagline>
<Body>
<![CDATA[
    <div class="html-content"><h5><strong>Self-Defending Ledgers: Automating Distributed Ledger Security Using Multi-Agent Reinforcement Learning and Game Theory</strong></h5><p><a href="https://paveltariq.com/" rel="nofollow external" class="bo"><strong>Md Tariqul Islam 'Pavel'</strong></a><br>Assistant Professor of Cybersecurity<br>UMBC Department of Information Systems</p><p>12–1pm ET, Wednesday, December 3, 2025<br>UMBC ITE 459 and online via <a href="https://umbc.webex.com/meet/sherman" rel="nofollow external" class="bo"><strong>Webex</strong></a></p><p><a href="https://en.wikipedia.org/wiki/Distributed_ledger" rel="nofollow external" class="bo"><strong>Distributed ledger technologies</strong></a> (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<strong> <a href="https://en.wikipedia.org/wiki/Multi-agent_reinforcement_learning" rel="nofollow external" class="bo"><strong>multi-agent reinforcement learning</strong> </a></strong>(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.</p><p><a href="https://paveltariq.com/" rel="nofollow external" class="bo"><strong>Md Tariqul Islam 'Pavel'</strong></a> 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.</p></div>
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<Summary>Self-Defending Ledgers: Automating Distributed Ledger Security Using Multi-Agent Reinforcement Learning and Game Theory  Md Tariqul Islam 'Pavel' Assistant Professor of Cybersecurity UMBC...</Summary>
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<PostedAt>Mon, 01 Dec 2025 13:28:41 -0500</PostedAt>
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<NewsItem contentIssues="false" id="153828" important="false" status="posted" url="https://my3.my.umbc.edu/groups/umbc-ai/posts/153828">
<Title>Talk 10/29: Building Trustworthy LLM Agents for Academia through Structured, Interpretable Knowledge Retrieval and Source Attribution</Title>
<Tagline>Manas Gaur and Yash Saxena, 2-1 pm EDT Wed., Oct. 29, online</Tagline>
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<![CDATA[
    <div class="html-content">UMBC Professor <a href="https://manasgaur.github.io/" rel="nofollow external" class="bo"><strong>Manas Gaur</strong></a> and Ph.D. student <strong><a href="https://www.linkedin.com/in/yash-saxena-a18b251bb/" rel="nofollow external" class="bo"><strong>Yash Saxena</strong></a> </strong>talk online on <strong>Building Trustworthy LLM Agents for Academia through Structured, Interpretable Knowledge Retrieval and Source Attribution</strong>, 12-1 PM EDT on Wednesday, October 29.<div><br></div><div>Ensuring the trustworthiness of language model outputs is essential for their adoption in academic research. This work presents a <a href="https://en.wikipedia.org/wiki/Retrieval-augmented_generation" rel="nofollow external" class="bo">retrieval-augmented</a> LLM agent designed to generate verifiable responses with sentence-level source attribution. The system employs a structured two-stage retrieval approach. In the first stage, lightweight neural modules adapt both query and document representations to improve alignment and enhance the quality of initial retrieval.</div><div><br></div><div>The second stage applies an advanced selection method to refine and finalize the evidence set. This pipeline is interpretable and attribution-aware, allowing users to trace each sentence in the generated output back to its supporting source. By combining structured retrieval with fine-grained attribution, the proposed architecture enables generation that is fluent, contextually accurate, and grounded in verifiable evidence. This design aligns with the rigorous standards required for scholarly communication.</div><div><br></div><h4>Session <a href="https://umbc.webex.com/recordingservice/sites/umbc/recording/2d95e582c98f4a518708f6e480c94f48/playback" rel="nofollow external" class="bo"><strong>recording</strong></a> and <a href="https://docs.google.com/presentation/d/1IIcsNmLuNmNbPGr3rk1bFJ-Llq7A-VsW/edit?usp=sharing&amp;ouid=113110094810317774015&amp;rtpof=true&amp;sd=true" rel="nofollow external" class="bo"><strong>slides</strong></a></h4><div><br></div><p><a href="https://my3.my.umbc.edu/groups/library/events/144147" rel="nofollow external" class="bo"><strong>Register and/or join event here</strong></a></p></div>
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<Summary>UMBC Professor Manas Gaur and Ph.D. student Yash Saxena talk online on Building Trustworthy LLM Agents for Academia through Structured, Interpretable Knowledge Retrieval and Source Attribution,...</Summary>
<Website>https://my3.my.umbc.edu/groups/library/events/144147</Website>
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<Tag>agent</Tag>
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<Sponsor>UMBC AOK Library</Sponsor>
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<NewsItem contentIssues="false" id="153488" important="false" status="posted" url="https://my3.my.umbc.edu/groups/umbc-ai/posts/153488">
<Title>Generative AI in Teaching II: Applied Skills and Use Cases</Title>
<Tagline>12-1 pm EDT Monday, October 13, 2025 online via WebEx</Tagline>
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<![CDATA[
    <div class="html-content"><div>Building on <a href="https://my3.my.umbc.edu/groups/umbc-ai/posts/153251" rel="nofollow external" class="bo">Gen AI &amp; Teaching I,</a> this online Webex interactive session led by UMBC professor <a href="https://saph.umbc.edu/ftfaculty/person/cv56922/" rel="nofollow external" class="bo">John Schumacher</a> highlights applied skills and practical use cases. Faculty will practice effective prompting, explore discipline-specific scenarios, and consider assignment design strategies. Examples will touch on advanced features (custom GPTs/GEMs, Canvas Mode, Deep Research Mode, agent mode) related to teaching.</div><div><br></div><p><strong>Reserve a seat and get the WebEx llnk by registering</strong> <a href="https://my3.my.umbc.edu/groups/calt/events/146815" rel="nofollow external" class="bo">here</a>.</p><p>This second session builds on Dr. Shumacher's first session.  Slides and a video of that session are available <a href="https://my3.my.umbc.edu/groups/umbc-ai/posts/153251" rel="nofollow external" class="bo">here</a>.</p><div><div><br></div><div><br></div><div><br></div></div></div>
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<Summary>Building on Gen AI &amp; Teaching I, this online Webex interactive session led by UMBC professor John Schumacher highlights applied skills and practical use cases. Faculty will practice effective...</Summary>
<Website>https://my3.my.umbc.edu/groups/calt/events/146815</Website>
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<ThumbnailAltText>College graduation had labeled with AI on an electronic circuit board. Generative AI in Teaching</ThumbnailAltText>
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<PostedAt>Sun, 12 Oct 2025 19:03:53 -0400</PostedAt>
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<NewsItem contentIssues="false" id="150764" important="false" status="posted" url="https://my3.my.umbc.edu/groups/umbc-ai/posts/150764">
<Title>UMBC graduate course on AI Agent Computing, Fall 2025</Title>
<Body>
<![CDATA[
    <div class="html-content"><div><span>UMBC's g</span>raduate <strong><a href="https://goseng.umbc.edu/" rel="nofollow external" class="bo">Software Engineering Program</a> </strong>will offer a new <span>special topics in software engineering class </span><span><strong> </strong></span><span>(<a href="https://csprd-web.ps.umbc.edu/psc/ps/EMPLOYEE/SA/s/WEBLIB_HCX_CM.H_CLASS_DETAILS.FieldFormula.IScript_Main?institution=UMBC1&amp;term=&amp;class_nbr=7609" rel="nofollow external" class="bo">SENG 691 section 5</a>) </span><span>this Fall on <strong>A</strong><strong>I Agent Computing</strong></span><span>.</span><span> T</span><span>opics will include open-source AI libraries; personal small language models and special-purpose models; AI agents: AI agents with LLMs, ML models, and rule-based systems; AI-enhanced robotics; augmented artificial intelligence; functional safety for autonomous devices; sustainable computing; brain computer interfaces; tools and policies for AI regulation; and wearables biomarkers in medicine and wellness. </span></div></div>
]]>
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<Summary>UMBC's graduate Software Engineering Program will offer a new special topics in software engineering class  (SENG 691 section 5) this Fall on AI Agent Computing. Topics will include open-source AI...</Summary>
<Website>https://csprd-web.ps.umbc.edu/psc/ps/EMPLOYEE/SA/s/WEBLIB_HCX_CM.H_CLASS_DETAILS.FieldFormula.IScript_Main?institution=UMBC1&amp;term=&amp;class_nbr=7609</Website>
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<Tag>agent</Tag>
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<Sponsor>UMBC AI</Sponsor>
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<ThumbnailAltText>a person programming with/for ai agents</ThumbnailAltText>
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<PostedAt>Thu, 26 Jun 2025 08:41:27 -0400</PostedAt>
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<NewsItem contentIssues="false" id="146680" important="false" status="posted" url="https://my3.my.umbc.edu/groups/umbc-ai/posts/146680">
<Title>Talk: Securing Networks with Reinforcement Learning &amp; Game Theory</Title>
<Tagline>10-11am Thursday, Jan. 30, 2025; ITE459 and online</Tagline>
<Body>
<![CDATA[
    <div class="html-content"><span><h4><span>Securing Distributed Networks: Leveraging Reinforcement Learning and Game Theory for Attack Detection and Mitigation</span></h4><h4><a href="https://ischool.syracuse.edu/md-tariqul-islam-pavel/#Biography" rel="nofollow external" class="bo"><span><strong>Dr. Md Tariqul Islam</strong></span></a><span>, Syracuse University</span></h4><h4><span>10-11am January 30, 2025;  ITE 459, UMBC and </span><a href="https://umbc.webex.com/umbc/j.php?MTID=m47153e19db08254c1e0d30e43cad1b24" rel="nofollow external" class="bo"><span>online</span></a></h4><p><br></p><p><span><a href="https://en.wikipedia.org/wiki/Reinforcement_learning" rel="nofollow external" class="bo"><strong>Reinforcement learning</strong></a> (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 <a href="https://en.wikipedia.org/wiki/Multi-agent_reinforcement_learning" rel="nofollow external" class="bo"><strong>multi-agent reinforcement learning</strong></a> (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 </span><span>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.</span></p><p><span><br></span></p><a href="https://ischool.syracuse.edu/md-tariqul-islam-pavel/#Biography" rel="nofollow external" class="bo"><span><strong>Dr. Md Tariqul Islam</strong></span></a><span> is an Assistant Professor of Trustworthy Cyberspace in the School of Information Studies (iSchool) at Syracuse University. His research focuses on advancing the security, efficiency, and fault tolerance of networks and distributed systems, particularly in the domains of cloud and blockchain technologies.</span></span><div><span><br></span><hr><a href="https://ai.umbc.edu/" rel="nofollow external" class="bo"><strong>UMBC Center for AI</strong></a> </div></div>
]]>
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<Summary>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,...</Summary>
<Website>https://informationsystems.umbc.edu/home/calendar/events/</Website>
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<Tag>agent</Tag>
<Tag>ai</Tag>
<Tag>cybersecurity</Tag>
<Tag>game-theory</Tag>
<Tag>machine-learning</Tag>
<Tag>multiagent</Tag>
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<Sponsor>UMBC AI</Sponsor>
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<PostedAt>Thu, 23 Jan 2025 10:32:43 -0500</PostedAt>
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