Speakers

Aadirupa Saha has been an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago (UIC) since Fall 2025. She is a member of the UIC CS Theory group, as well as IDEAL Institute. Prior to this, she was a Research Scientist at Apple MLR, working on Machine Learning theory. She completed her postdoctoral research at Microsoft Research (NYC) and earned her PhD from the Indian Institute of Science (IISc), Bangalore.

Saha's primary research focuses on AI alignment through Reinforcement Learning with Human Feedback (RLHF), with applications in language models, assistive robotics, autonomous systems, and personalized AI. At a high level, her work aims to develop robust and scalable AI models for designing prediction systems under uncertain and partial feedback.

Her research primarily focuses on designing Efficient Human Aligned Prediction Models: Few specific research areas include Online learning theory, Bandits & RL, Federated Optimization, and Differential Privacy. Of late, she has also been working on some problems at the intersection of Mechanism Design, Game Theory and Algorithmic Fairness. Aadirupa has organized several workshops and tutorials in the recent years, including a UAI-23 tutorial, two ICML workshops [2023], [2022] and two TTIC workshops [2023], [2022], and also served for different panel discussions. 

Website: https://aadirupa.github.io/ Email: aadirupa.saha@gmail.com

Arun is a Postdoctoral Associate at the Singapore-MIT Alliance for Research and Technology Center. His research focuses on developing adaptive and efficient AI systems that autonomously make complex decisions in dynamic real-world environments. 

Prior to this, I was a Research Fellow in the Department of Computer Science at the National University of Singapore, where I worked with Bryan Low. I obtained my Doctor of Philosophy (Ph.D.) degree from Indian Institute of Technology Bombay (IIT Bombay), where Manjesh K. Hanawal and N. Hemachandra supervised me. My doctoral thesis received two awards: Naik and Rastogi Excellence in Ph.D. Thesis Award and COMSNETS Best Ph.D. Thesis Award. He contributes to framing agentic AI problems as explicit bandit/RL formulations and their demonstrations.

Website: https://arunv3rma.github.io/ Email: arun.verma@smart.mit.edu

Djallel Bouneffouf is a Research Scientist at IBM Research in New York. Her research focuses on multi-armed and contextual bandits, reinforcement learning, and their applications to recommendation systems, healthcare, and decision-making under uncertainty. She contributes to contextual bandit algorithms and their deployment in real-world agentic AI systems. She gave tutorials on Bandits, LLMs, and Agentic AI at AAAI 2026, and Multi-Armed Bandit Applications for Large Language Models at ACM SIGKDD (KDD). 2024.

Website: https://scholar.google.com/citations?user=i2a1LUMAAAAJ Email: djallel.bouneffouf@ibm.com