Anima Anandkumar is Bren Professor at Caltech and Senior Director of AI Research at NVIDIA. She received her B.Tech from the Indian Institute of Technology Madras, and her Ph.D. from Cornell University. She did her postdoctoral research at MIT and an assistant professorship at the University of California Irvine. She has received several honors such as the IEEE fellowship, Alfred. P. Sloan Fellowship, NSF Career Award, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network.

Kun Zhang is the Acting Chair of Machine Learning, Professor of Machine Learning, and Director of Center for Integrative Artificial Intelligence (CIAI). Zhang’s research interests lie in machine learning and artificial intelligence, especially in causal discovery and inference, causal representation learning, and machine learning under data heterogeneity. He aims to make causal learning and reasoning transparent in science, AI systems, and human society. On the application side, he is interested in biology, neuroscience, computer vision, computational finance, and climate analysis. His research has been motivated by real problems in healthcare, biology, neuroscience, computer vision, computational finance, and climate analysis.

M. Pawan Kumar is a research scientist at DeepMind. Prior to that, he was a faculty member in the Department of Engineering Science at the University of Oxford during 2015 – 2021, where he led the OVAL group which focused on the design and analysis of optimization algorithms for problems arising in computer vision and machine learning. During 2012 – 2015, he was a faculty member in the Center for Visual Computing at Ecole Centrale Paris. 

Swarat Chaudhuri is a Professor of Computer Science and the director of the Trishul laboratory at UT Austin. His research lies at the interface of programming languages, formal methods, and machine learning. His aim as a researcher is to develop a new class of intelligent systems that are reliable, transparent, and secure by construction and can solve reasoning-intensive tasks beyond the scope of contemporary AI. He has received the NSF CAREER award, the ACM SIGPLAN John Reynolds Dissertation award, the Morris and Dorothy Rubinoff Dissertation award from the University of Pennsylvania, Meta and Google Research awards, and several ACM SIGPLAN and SIGSOFT distinguished paper awards. 

Wenda Li is a lecturer in hybrid AI at the University of Edinburgh and a visiting research fellow at the University of Cambridge. Prior to this, I was a research associate and a PhD student working with Prof. Larry Paulson at the University of Cambridge. His research interests include machine learning for theorem proving, interactive theorem proving, verified symbolic computing, and mechanised mathematics. 

Yilun Zhou currently works at Salesforce as a research scientist. He finished his Ph.D. degree at MIT Department of Electrical Engineering and Computer Science (EECS) working on model transparency and interpretability advised by Prof. Julie Shah. His long-term research goal is to enable trustworthy and responsible machine learning, and his recent research explores LLM-generated free-text self-explanations, their mathematical reasoning abilities, as well as evaluations, utilities and societal implications of model explanations in general.

Piotr Miłoś is an associate professor in the Polish Academy of Sciences and University of Warsaw, a team leader at Ideas Ncbr and a member of the ELLIS Society. He is interested in methods that can deliver robust decision-making capabilities in complex scenarios.

Albert Q. Jiang is a PhD student at the Cambridge Computer Laboratory, supervised by Professor Mateja Jamnik and Professor Wenda Li. He also part-time at Mistral AI. I’m a contributor to Mistral-7B-v0.1 and Mixtral-8x7B-v0.1. He studies how to learn abstract mathematical reasoning, with large (>20B) and small (<1B) language models.