Mingsheng Long, Tsinghua University

Mingsheng Long is an Associate Professor with tenure in the School of Software at Tsinghua University. He earned the BE and PhD degrees from Tsinghua University in 2008 and 2014 respectively, and worked as a researcher at UC Berkeley from 2014 to 2015. His research is dedicated to machine learning theory, algorithms, and applications, with special interests in transfer learning and domain adaptation, foundation models and deep learning, and informed learning with scientific knowledge. His work on transfer learning won the Test of Time Award of IJCAI FTL (2021) and has received more than 20,000 citations in Google Scholar. He serves as an Associate Editor of IEEE TPAMI and TMLR, and regularly as an Area Chair of ICML, NeurIPS, and ICLR.

Markus Reichstein, Max Planck Institute for Biogeochemistry

Markus Reichstein is Director at the Max-Planck-Institute for Biogeochemistry, and Professor for Global Ecology at the University of Jena. He is founding co-director of the ELLIS program “Machine Learning for Earth and Climate Science” and the recently established ELLIS Unit Jena within the Michael-Stifel-Center Jena for Data-driven and Simulation Science Jena. and member of the German National Committee Future Earth for Sustainability research. He has been serving as lead author for the IPCC, as member of the German Committee Future Earth on Sustainability Research, and the Thuringian Panel on Climate for advising the state on climate protection and adaptation.

Markus’s main research interests revolve around the response and feedback of ecosystems (vegetation and soils) to climatic variability with an Earth system perspective. Of specific interest is the interplay of climate extremes with ecosystem and societal resilience. He is addressing these topics with a combination of artificial intelligence and classical modelling approaches to exploit the wealth of experimental, ground- and satellite-based Earth observations together with theoretical knowledge. Recent awards for his research include the Piers J. Sellers Mid-Career Award by the American Geophysical Union (2018), and the Gottfried Wilhelm Leibniz Preis by the German Science Foundation (2020).

He is also Principal Investigator in the European Research Council Synergy Grant USMILE dedicated to the development and application of machine learning for a better Earth system understanding and modelling. Furthermore, Markus is chairing the Global Research Program and Knowledge-Action Network “Emergent Risks and Extreme Events – Reducing Disaster Risks under Environmental Change” (www.risk-kan.org).

Markus is excited about linking system thinking with data-driven science and artificial intelligence for understanding complex systems, such as the climate-environmental-societal system and believes that such approaches can help societies become more resilient and sustainable.

Pradeep Ravikumar, Carnegie Mellon University

Pradeep Ravikumar is a Professor in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. He was previously an Associate Director at the Center for Big Data Analytics, at the University of Texas at Austin. His thesis has received honorable mentions in the ACM SIGKDD Dissertation award and the CMU School of Computer Science Distinguished Dissertation award. He is a Sloan Fellow, a Siebel Scholar, a recipient of the NSF CAREER Award, and was Program Chair for the International Conference on Artificial Intelligence and Statistics (AISTATS) in 2013. He is Associate Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and action editor for the Machine Learning journal, and the Journal of Machine Learning Research.

Dr. Ravikumar's research group at CMU works on the foundations of statistical machine learning, with recent focus on "next generation" machine learning systems, that are explainable, robust to train and test time corruptions, resilient to distribution shifts, and are learnt under resource constraints by leveraging or discovering various notions of "structure" and domain knowledge.

Behnam Neyshabur, Google Research

Behnam Neyshabur is a senior staff research scientist at Google Research. Before that, he was a postdoctoral researcher at New York University and a member of Theoretical Machine Learning program at Institute for Advanced Study (IAS) in Princeton. In summer 2017, he received a PhD in computer science at TTI-Chicago. Behnam's primary interest is reasoning and algorithmic capabilities of giant language models but he has also not lost his interest in the science of deep learning.

Erin Hartman, UC Berkeley

Erin Hartman is an Assistant Professor of Political Science at the University of California, Berkeley. Her research sits at the intersection of the social sciences and statistics. Her mission is to create a body of research that bridges these two worlds — with an emphasis on answering causal questions — within which experts from both worlds can have dialogue with one another and foster beneficial collaborations. In particular, she focuses on developing methods that allow researchers to generalize experimental results beyond the units, treatments, outcomes, and contexts upon which they were conducted. She also conducts work on survey design and analysis, informed by her time running the Analytics team's polling operation for President Obama's 2012 re-election campaign, and at her start-up BlueLabs. Erin's research in survey design and analysis focuses on statistical methods for leveraging detailed individual level data to overcome the non-random nature of modern survey data.

David Sontag, MIT

David Sontag is a Professor of Electrical Engineering and Computer Science at MIT, part of the Institute for Medical Engineering & Science, the Computer Science and Artificial Intelligence Laboratory, and the J-Clinic for Machine Learning in Health. His research focuses on advancing machine learning and artificial intelligence, and using these to transform health care. Previously, he was an Assistant Professor of Computer Science and Data Science at New York University.

Pradeep Ravikumar, Carnegie Mellon University

Pradeep Ravikumar is a Professor in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. He was previously an Associate Director at the Center for Big Data Analytics, at the University of Texas at Austin. His thesis has received honorable mentions in the ACM SIGKDD Dissertation award and the CMU School of Computer Science Distinguished Dissertation award. He is a Sloan Fellow, a Siebel Scholar, a recipient of the NSF CAREER Award, and was Program Chair for the International Conference on Artificial Intelligence and Statistics (AISTATS) in 2013. He is Associate Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and action editor for the Machine Learning journal, and the Journal of Machine Learning Research.

Dr. Ravikumar's research group at CMU works on the foundations of statistical machine learning, with recent focus on "next generation" machine learning systems, that are explainable, robust to train and test time corruptions, resilient to distribution shifts, and are learnt under resource constraints by leveraging or discovering various notions of "structure" and domain knowledge.

Erin Hartman, UC Berkeley

Erin Hartman is an Assistant Professor of Political Science at the University of California, Berkeley. Her research sits at the intersection of the social sciences and statistics. Her mission is to create a body of research that bridges these two worlds — with an emphasis on answering causal questions — within which experts from both worlds can have dialogue with one another and foster beneficial collaborations. In particular, she focuses on developing methods that allow researchers to generalize experimental results beyond the units, treatments, outcomes, and contexts upon which they were conducted. She also conducts work on survey design and analysis, informed by her time running the Analytics team's polling operation for President Obama's 2012 re-election campaign, and at her start-up BlueLabs. Erin's research in survey design and analysis focuses on statistical methods for leveraging detailed individual level data to overcome the non-random nature of modern survey data.

Alicia Wassink, University of Washington

Prof. Wassink is the Director of the Sociolinguistics Laboratory, and professor of Linguistics in the Department of Linguistics at the University of Washington. Her research interests lie in sociolinguistics (the study of language in its various social contexts, the relationships between language and social network structure, language attitudes and the outcomes of language and dialect contact) and phonetics (the study of the acoustic properties of spoken language, perception, and physiological aspects of human speech). One of Prof. Wassink’s principal languages of study is Jamaican Creole. When she is in Jamaica conducting fieldwork, her home base is the University of the West Indies.

Sara Beery, Google & MIT

Sara Beery will join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023 and is currently a visiting researcher at Google working on urban tree mapping across North America. She received her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide and works toward increasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.