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08/28/2026 |
Dr. Apurv Shukla
University of Michigan-Dearborn, USA |
Leaderboards with Receipts: Certifying LLM Rankings and Directing Preference Collection (in-person) |
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Bio: Dr. Apurv Shukla is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Michigan–Dearborn. His research studies how autonomous decision-making systems operate under information, strategic, and physical constraints, with the goal of building safe, efficient, and economically aligned learning systems for critical infrastructure — including healthcare, power systems, markets, and platforms. His recent work develops the theory and algorithms of preference-based pure exploration: sequential experiment design for identifying best alternatives and Pareto frontiers from preference feedback. He got his PhD from Columbia University and was previously a postdoc at Texas A&M and University of Michigan, Ann Arbor.
Abstract: Every published model leaderboard invites two questions: is the ranking real, and how many more votes until it is? Current practice — Elo scores, uncalibrated confidence intervals, uniform vote collection — answers neither. In this talk I treat a leaderboard as what it is: a certificate in a fixed-confidence identification problem. Under a Bradley–Terry model, the information carried by a set of votes is a weighted graph Laplacian, and every ranking claim acquires a price: an Elo gap is certified at gap 2 over twice the effective resistance between the two models in the vote graph. This yields a procedure that certifies claims at confidence 1−δ , directs each next vote toward the current bottleneck, and stops when the claim set is certified. On Bradley–Terry ground truth fitted to 1.16M real Chatbot Arena battles, the certified best model costs about 10% of the votes required by confidence-interval practice, with zero errors across all runs — and the same machinery, pointed at preference annotation for alignment, certifies a helpful–harmless frontier at roughly 3× lower annotation cost than random collection.
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09/11/2026 |
Kezia Oketch
University of Notre Dame, USA |
Cultural Artifacts, Tribal Heterogeneity, and Language Models (in-person) |
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Bio: Kezia Oketch is a fourth-year Ph.D. student in Analytics at the University of Notre Dame’s Mendoza College of Business. She is a Research Associate at the Human-centered Analytics Lab (HAL), where she is advised by Professor Ahmed Abbasi. Her research lies at the intersection of artificial intelligence, computational social science, and information systems, with a focus on making AI systems more contextually intelligent. She studies how AI understands culture, evaluates information, and models human behavior, developing computational methods and frameworks to improve the reliability, fairness, and trustworthiness of AI in real-world settings. Her long-term goal is to advance contextually intelligent AI that can adapt to the complexity and diversity of human contexts. After completing her Ph.D., she plans to pursue a faculty career focused on advancing human-centered AI and mentoring the next generation of scholars.
Abstract: Information systems (IS) that rely on language models often assume linguistic homogeneity. Yet in multilingual societies with tribal heterogeneity, cultural and tribal heterogeneity manifest within a language in the form of cultural-linguistic artifacts such as loanwords, code-mixing, tribal lexicons, and evolving vernaculars. Focusing on Swahili as written/spoken across Kenyan tribes, this paper shows how such cultural-linguistic artifacts degrade language model performance on health-related psychometric tasks, undermining equitable service delivery in AI-driven systems. Drawing on systemic functional linguistics, we develop a taxonomy of cultural-linguistic artifacts that reveals how linguistic variation drives language model errors in assessment and generation tasks across tribal subgroups. To mitigate these inequities, we introduce Cultural-Linguistic Alignment and Steering System (CLASS), a model-agnostic steering method that aligns language model behavior with culturally grounded variation through regression-based conditioning. Evaluation results show that CLASS reduces language model errors by up to 93.75% without retraining, offering a cost-effective and interpretable pathway for mitigating inequities in black-box AI deployments. These findings extend IS research on human-centered computational design by illuminating how language models may exhibit cultural-linguistic misalignment and by providing actionable mechanisms for mitigating such misalignment in AI systems.
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09/25/2026 |
Dr. Tapadhir Das
University of the Pacific, USA |
Resilient AI against Adversarial Interactions in Autonomous Vehicular Perception (Remote) |
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Bio: Dr. Tapadhir Das is an Assistant Professor in the Department of Computer Science and Director of the Dependability And Security Laboratory at the University of the Pacific. His primary research focuses on protecting cyber-physical systems, such as intelligent vehicles and smart grid infrastructure, from cyberattacks and on developing resilient and trustworthy AI frameworks. As a researcher, Tapadhir has co-authored over 40 international conference papers, journal articles, and book chapters. He is the recipient of an IEEE CARS 2024 Best Paper Award and an IEEE IECON 2024 Best Paper Presentation Award. Tapadhir has also been recognized with the 2026 Hoefer Award for Outstanding Student-Faculty Research at the University of the Pacific, along with the 2026 Alumni Scientific Achievement Award from the Oregon Institute of Technology. He currently serves on the organizing and program committees of several IEEE conferences and is on the editorial and review boards for several high-impact journals.
Abstract: Autonomous Vehicles (AVs) are one of the most revolutionary developments in the field of vehicular technology, with companies like Waymo, Tesla, and Zoox leading the charge. These smart vehicles improve road safety, accessibility, and environmentally friendly driving practices through advanced sensing capabilities. However, the widespread adoption of this technology is currently hindered by major threats: adversarial attacks, intentional vandalism, and environmental degradation. These impact critical AV perception operations like pedestrian detection, sign recognition, and scene understanding. This presentation will discuss these current threats and how new & advanced AI methods can be leveraged to increase resiliency in AV perception systems against these evolving threats.
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10/09/2026 |
Dr. xxx
Example University |
title of the talk |
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Bio: Dr. xxx.
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10/23/2026 |
Dr. Traci Carte
Illinois State University, USA |
Encouraging Agentic AI Use in Higher Education: A Repeated-Measures Study of Student Attitudes and Intentions (in-person) |
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Bio: Dr. Traci Carte earned her PhD degree from University of Georgia and she is curently a professor of information systems at School of Information Technology, Illinois State University, USA. Recently, she concluded a six-year term as director of the School of Information Technology and previously served as department chair at Kennesaw State University. During her time in administrative roles, she successfully led ABET reaccreditation twice and spent considerable time on curriculum development. She has been recognized by her peers and former students with 15 separate teaching awards. Her research expertise is in the area of collaboration using technology, and her work has been published in such journals as MIS Quarterly, Information Systems Research, and the Journal of AIS. She received an MISQ best paper award, an AMCIS best paper award and multiple outstanding reviewer awards. In 2024 she was named an AIS Fellow. She provides leadership to the discipline through the Association of Information Systems where she has served as a program chair for both ICIS and AMCIS as well as serving as a track chair at ICIS multiple times, a mini track chair at AMCIS multiple times, and as a frequent reviewer. Most recently, she served 4 years as the VP of Conferences for AIS.
Abstract: Artificial intelligence (AI) tools are increasingly embedded in higher education possibly leading to enhanced learning outcomes when used to support, rather than replace, student cognition. However, inappropriate use may undermine critical thinking and promote over-reliance. To better understand how students engage with AI and to encourage more effective use, this study examines whether a structured intervention can influence student attitudes and intentions toward AI. Drawing on the Technology Acceptance Model (TAM) and Expectation-Confirmation Theory, we conducted a repeated-measures investigation across two contexts: a low-stakes laboratory experiment and a higher-stakes field study. The intervention emphasized guided prompt engineering strategies designed to position AI as a learning support tool rather than a shortcut to solutions. In low-stakes conditions with limited task knowledge, AI use reinforced positive attitudes, consistent with expectation confirmation. In contrast, in higher-stakes conditions where students had greater task familiarity, AI use led to negative disconfirmation and declining attitudes. Across both settings, behavioral intentions remained relatively stable, suggesting greater resistance to short-term change. These findings highlight the importance of modeling purposefully, agentic AI use in educational settings.
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11/06/2026 |
Arman Behnam
Illinois Institute of Technology, USA |
Structure-agnostic Causal Representation Learning (in-person) |
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Bio: Dr. xxx.
Abstract: xxx.
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11/20/2026 |
Dr. xxx
Example University |
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Bio: Dr. xxx.
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Associate Professor, School of Computing, College of Tech Futures