Meet our Panelists (tentitive)
(alphabetically by last names)

• Akshay Krishnamurthy (MSR):  Akshay is a Senior Principal Research Manager at Microsoft Research, New York City. Previously, he spent two years as an Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst, and a year as a Postdoctoral Researcher at Microsoft Research, NYC. He completed his PhD in the Computer Science Department at Carnegie Mellon University, advised by Aarti Singh, and received his undergraduate degree in EECS from UC Berkeley.

His research interests lie in machine learning and statistics, with a particular focus on interactive learning — settings that involve feedback-driven data collection. His recent work centers on decision making with limited feedback, including contextual bandits and reinforcement learning, and how these frameworks manifest in language modeling and generative AI.

• Branislav Kveton  (Adobe): Branislav Kveton is a Principal Research Scientist at Adobe Research. Previously, he was at Amazon from 2021 to 2024, at Google Research from 2018 to 2021, at Adobe Research from 2014 to 2018, at Technicolor's Research Center from 2011 to 2014, and at Intel Research from 2006 to 2011. Before 2006, he was a graduate student in the Intelligent Systems Program at the University of Pittsburgh, advised by Milos Hauskrecht.

His research focuses on proposing, analyzing, and applying algorithms that learn incrementally, run in real time, and converge to near-optimal solutions as the number of observations increases. Most of his recent work applies these ideas to modern generative models and human feedback.

• John Langford (MSR):  John Langford is a Partner Research Manager at Microsoft Research, New York City, where he has been since 2012. Prior to that, he was a Senior Research Scientist at Yahoo! Research, New York (2006–2012), a Research Assistant Professor at TTI-Chicago (2003–2006), a Herman Goldstine Fellow at IBM T.J. Watson Research Center (2002–2003), and a Postdoctoral Researcher at the University of Pennsylvania with Michael Kearns. He holds a Ph.D. in Computer Science from Carnegie Mellon University, advised by Avrim Blum and Sebastian Thrun, and dual B.S. degrees in Physics and Computer Science from the California Institute of Technology.

His research is broadly focused on machine learning, with a particular emphasis on interactive and real-world learning settings. He is widely recognized for his foundational contributions to contextual bandits, learning reductions, and scalable machine learning, and is the creator of the widely used Vowpal Wabbit library. He has served as General Chair of ICML 2016, Program Chair of ICML 2012, and President of ICML (2020–2022), and has delivered numerous influential tutorials at premier venues including ICML, NeurIPS, and KDD.

• Lihong Li (Meta) :  Lihong Li is an AI Research Scientist at Meta Platforms, based in Bellevue, WA. Prior to Meta, he was a Senior Principal Applied Scientist at Amazon (2020–2025), a Research Scientist at Google (2017–2020), and a Senior/Principal/Senior Principal Researcher at Microsoft Research, Redmond (2012–2017). Earlier in his career, he was a Research Scientist at Yahoo! Research (2009–2012). He holds a Ph.D. in Computer Science from Rutgers University, an M.Sc. in Computing Science from the University of Alberta, and a B.Eng. in Computer Science and Technology from Tsinghua University.

His research focuses on machine learning for interactive and agentic systems — settings where an agent maximizes a utility function by taking actions, in contrast to prediction-oriented approaches such as supervised learning. His work spans large language models, reinforcement learning, contextual bandits, and related areas, with applications to industrial recommendation systems, web search, advertising, and conversational AI at leading technology companies. He is a recipient of the 2023 Seoul Test of Time Award, the 2011 WSDM Best Paper Award, and the 2008 ICML Best Student Paper Award, among other honors.

• Paul Liang  (MIT) :  Paul Liang is an Assistant Professor at MIT where he directs the Multisensory Intelligence Group. He completed his PhD in Machine Learning at Carnegie Mellon University and also received his MS in Machine Learning and his BS with University Honors in Computer Science and Neural Computation from CMU. 

His research develops the foundations of self-evolving multisensory AI to advance human capabilities and well-being. His recent work spans multisensory foundations, self-evolving AI systems, and interactive technologies for human experience, with applications in healthcare, robotics, and social intelligence. He has received multiple best paper awards, the CMU Distinguished Dissertation Award, research awards from Microsoft, Nvidia, and Meta, and the Alan J. Perlis Teaching Award.

• Armando Solar-Lezama (MIT) :  Armando Solar-Lezama is a Distinguished Professor at MIT and has served as Associate Director and Chief Operating Officer of CSAIL, where he leads the Computer Assisted Programming Group. He completed his PhD at UC Berkeley in 2008 and received his BS degrees in Computer Science and Mathematics from Texas A&M University. 

His research focuses on program synthesis, an area at the intersection of programming systems and artificial intelligence, and on the use of automated reasoning and machine learning to push the limits of programming automation. His recent work focuses on developing neurosymbolic programming, a new class of learning techniques that incorporate some of the benefits of traditional programming languages, such as modularity, compositionality, and predictability to build learning systems that are more predictable and robust.