Keynote Speakers

Tanya Berger-Wolf

Tuesday, August 27th, 9:00 AM – 10:30 AM

Title: AI for Nature: From Science to Impact

Bio: Dr. Tanya Berger-Wolf is a Professor of Computer Science Engineering, Electrical and Computer Engineering, and Evolution, Ecology, and Organismal Biology at the Ohio State University, where she is also the Director of the Translational Data Analytics Institute. As a computational ecologist, her research is at the unique intersection of computer science, AI, wildlife biology, and social sciences. She is leading the US National Science Foundation funded Imageomics Institute and the newly funded AI for Biodiversity Change (ABC) Global Climate Center.  

Berger-Wolf is a member of the US National Academies Board on Life Sciences, US National Committee for the International Union of Biological Sciences (IUBS),  and the Advisory Committee for the Global Partnership on AI (GPAI) AI on Biodiversity working group, among many others. 

Berger-Wolf is also a co-founder of the AI for conservation non-profit Wild Me (now part of the Conservation X Labs), home of the Wildbook project, which has been chosen by UNSECO as one of the 100 AI projects worldwide supporting the UN Sustainable Development Goals.

Sanjeev Arora

Wednesday, August 28th, 9:00 AM – 10:30 AM

Title: From Word Prediction to Complex Skills: Compositional Thinking and Metacognition in LLMs

Bio: Sanjeev Arora is the Charles C. Fitzmorris Professor of Computer Science at Princeton University, as well as the founding Director of Princeton Language and Intelligence. He works on developing mathematical and conceptual understanding of today’s AI models, as well as better ways to design them. He has received the Packard Fellowship (1997), Simons Investigator Award (2012), Gödel Prize (2001 and 2010), ACM Prize in Computing (formerly the ACM-Infosys Foundation Award in the Computing Sciences) (2011), and the Fulkerson Prize in Discrete Math (2012). He is a fellow of the American Academy of Arts and Sciences and member of the National Academy of Science. 

Xihong Lin

Thursday, August 29th, 9:00 AM – 10:30 AM

Title: Empower an End-to-End Scalable and Interpretable Data Science Ecosystem using Statistics, AI and Domain Science

Bio: Xihong Lin is Professor and former Chair of Biostatistics, and Coordinating Director of the Program in Quantitative Genomics at Harvard School of Public Health, and Professor of Statistics at Harvard University. Dr. Lin works on the development and application of statistical and machine learning methods for analysis of big and complex genomic and health data, such as large scale whole genome sequencing (WGS) studies and multi-ethnic biobanks, Electronic Health Records, and whole genome variant functional annotations. The methods and tools her lab has developed have been widely used in analyzing WGS and biobank data, including the Trans-Omics Precision Medicine Program (TOPMed) of the National Heart, Lung, and Blood Institute (NHLBI), the Genome Sequencing Program of the National Human Genome Research Institute (NHGRI), the UK biobank, and the NIH All of Us Program.  Dr. Lin was elected to the US National Academy of Medicine in 2018 and the US National Academy of Sciences in 2023. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Presidents’ Award from the Committee of Presidents of Statistical Societies (COPSS). She also received the 2017 COPSS FN David Award, the 2022 National Institute of Statistical Sciences Sacks Award for Outstanding Cross-Disciplinary Research, and the 2022 Zelen Leadership in Statistical Science Award. She is an elected fellow of American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute. Dr. Lin’s research has been supported by the MERIT Award (2007-2015) and the Outstanding Investigator Award (2015-2029) from the National Institute of Health. Dr. Lin is the former Chair of COPSS and the former Editor of several biostatistical journals. She has served on several US National Academies committees.