Bio: Aditi Raghunathan is an Assistant Professor at Carnegie Mellon University. She works broadly in machine learning, and her goal is to make machine learning more reliable and robust. Raghunathan's work spans both theory and practice, and leverages tools and concepts from statistics, convex optimization, and algorithms to improve the robustness of modern systems based on deep learning.Until recently, Raghunathan was a postdoc at Berkeley AI Research. She received her Ph.D. from Stanford University in 2021 where she was advised by Percy Liang. Her thesis won the Arthur Samuel Best Thesis Award at Stanford. Previously, she obtained her BTech in Computer Science from IIT Madras in 2016.

Bio: Marta Garnelo leads the research team at Fundamental as Chief Science Officer, where she is currently focused on the challenge of building foundation models for tabular data. Before moving to Barcelona for this, she spent over seven years as a researcher at DeepMind, focusing on the intersection of probabilistic modeling and deep learning. Marta is perhaps best known for introducing Neural Processes, though her research spans a broad range of topics including meta-learning, multi-agent RL, game theory, and exploring alternatives to the attention mechanism. She earned her PhD from Imperial College London.

Bio: Preslav Nakov is a Full Professor at MBZUAI, Abu Dhabi. Prior to joining MBZUAI, Professor Nakov worked at the Qatar Computing Research, HBKU where he was a principal scientist. Previously, he was a research fellow at the National University of Singapore (2008–2011) and a researcher at the Bulgarian Academy of Sciences (2008). He has been an honorary lecturer at Sofia University, Bulgaria since 2014. Professor Nakov authored a Morgan and Claypool book titled Semantic Relations Between Nominals (2nd edition in 2021) and two books on computer algorithms. He was also the first to receive the Bulgarian President's John Atanasoff award, named after the inventor of the first automatic electronic digital computer. Professor Nakov is one of the leading experts on “fake news”, disinformation, fact checking, propaganda, and media bias detection and has published tens of research papers on solutions and stop-gaps for the ever-growing online social media infodemic. He’s served on the program committees of the major conferences in computational linguistics and artificial intelligence. Most recently, he was a program committee chair of the annual conference of the Association for Computational Linguistics (ACL 2022).

Bio: Samy Bengio (PhD in computer science, University of Montreal, 1993) is a senior director of machine learning research at Apple since 2021. Before that, he was a distinguished scientist at Google Research since 2007 where he was heading part of the Google Brain team, and at IDIAP in the early 2000s where he co-wrote the well-known open-source Torch machine learning library.

His research interests span many areas of machine learning such as deep architectures, representation learning, vision and language processing and more recently, reasoning.

He is action editor of the Journal of Machine Learning Research and on the board of the NeurIPS foundation. He was on the editorial board of the Machine Learning Journal, has been program chair (2017) and general chair (2018) of NeurIPS, program chair of ICLR (2015, 2016), general chair of BayLearn (2012-2015), MLMI (2004-2006), as well as NNSP (2002), and on the program committee of several international conferences such as NeurIPS, ICML, ICLR, ECML and IJCAI.

Bio: Sewon Min is an Assistant Professor in EECS at UC Berkeley, affiliated with Berkeley AI Research (BAIR), and a Research Scientist at the Allen Institute for AI. Her research lies at the intersection of natural language processing and machine learning, with a focus on large language models (LLMs). She studies the science of LLMs and develops new models and training methods for better performance, flexibility, and adaptability, such as retrieval-based LMs, mixture-of-experts, and modular systems. She also studies LLMs for information-seeking, factuality, privacy, and mathematical reasoning. She has organized tutorials and workshops at major conferences (ACL, EMNLP, NAACL, NeurIPS, ICLR), served as a Senior Area Chair, and received honors including best paper and dissertation awards (including ACM Dissertation Award Runner-up), a J.P. Morgan Fellowship, and EECS Rising Stars. She earned her Ph.D. from the University of Washington and has held research roles at Meta AI, Google, and Salesforce. 

Bio: Surbhi Goel is the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. Her research interests lie at the intersection of theoretical computer science and machine learning, with a focus on developing theoretical foundations for modern machine learning paradigms. Previously, she was a postdoctoral researcher at Microsoft Research NYC in the Machine Learning group. She received her Ph.D. in Computer Science from the University of Texas at Austin, where she was advised by Adam Klivans. Among her honors are the Bert Kay Dissertation award, a JP Morgan AI Fellowship, and a Simons-Berkeley Research Fellowship. She is also the co-founder of Learning Theory Alliance (LeT-All), a community building and mentorship initiative for the learning theory community.