Participants

Alphabetically ordered based on last name. 

Chid Apte, IBM

Chair, Math Sciences Council, Exploratory Science Program, IBM Research

Research focus: Dr. Chid Apte is Chair of the IBM Research Mathematical Sciences Council which manages the long-term fundamental research program in Applied Math and Theoretical Computer Science for IBM. Chid has extensive experience over many decades as a research scientist and technical leader in the data science area and has done significant work in data analytics technologies and solutions for IBM and many of its industrial clients. He received his Ph.D. in Computer Science from Rutgers University, and a B. Tech. in Electrical Engineering from the Indian Institute of Technology (Bombay). He is currently based at the IBM Thomas J. Watson Research Center in Yorktown Heights, New York. 

Arindam Banerjee, University of Illinois Urbana-Champaign

Founder Professor at the Department of Computer Science

Research focus: His research interests are in machine learning. His current research focuses on computational and statistical aspects of over-parameterized models including deep learning, spatial and temporal data analysis, generative models, and sequential decision making problems. His work also focuses on applications of machine learning in complex real-world and scientific domains including problems in climate science and ecology. He has won several awards, including the NSF CAREER award (2010), the IBM Faculty Award (2013), and seven best paper awards in top-tier venues.

Tanya Berger-Wolf, The Ohio State University

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. She is leading the US National Science Foundation funded Imageomics Institute and the newly funded AI and 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 and Biodiversity working group, among many others. Berger-Wolf is also a director and co-founder of the AI for conservation non-profit Wild Me (now part of 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.

Nitesh Chawla, University of Notre Dame

Frank Freimann Professor of Computer Science and Engineering

Research focus: artificial intelligence, data science, and network science; research motivated by the question of how technology can advance the common good through interdisciplinary research.

Aryan Deshwal, University of Minnesota

Assistant Professor, Computer Science & Engineering, (starting August 1, 2024)

Research focus: artificial intelligence (AI) and machine learning (ML) with focus on advancing foundations of AI/ML to solve challenging real-world problems with high societal impact. Overarching theme of research program is AI to Accelerate Scientific Discovery and Engineering Design with close collaboration with domain experts to solve high-impact science and engineering applications.

Wei Ding, University of Massachusetts Boston

Distinguished Professor of Computer Science and Executive Director of the Paul English Applied AI Institute at the University of Massachusetts Boston. She was a Program Director at the NSF's Division of Information Intelligent Systems (2019-2023), overseeing areas such as Information Integration and Informatics, Smart Health, and Deep Learning.

Research focus: Her research covers data mining, machine learning, AI, and computational semantics in health sciences, astronomy, and environmental sciences. Wei has over 150 research papers, a book, and three patents. She serves as an Associate Editor for top journals and has received awards like the 2022 NSF Director's Award, IEEE Fellow, and the 2019 WISAY Distinguished Woman in Science Award from Yale University.

Jennifer Dy, Northeastern University

Professor,  Electrical and Computer Engineering; Professor,  Khoury College of Computer Sciences; Director of AI Faculty,  Institute for Experiential AI

Research focus: Her research spans both foundations in machine learning and its application to biomedical imaging, health, science and engineering, with research contributions in unsupervised learning, interpretable models, explainable AI, dimensionality reduction, feature selection/sparse methods, learning from uncertain experts, active learning, Bayesian models, deep representation learning, continual learning, and trustworthy AI.  She received an NSF Career award in 2004. She has served as Secretary for the ICML Board, editorial board member for JMLR, MLJ, IEEE TPAMI, served in various capacities for conferences in machine learning, AI, and data mining (ICML, NeurIPS, ACM SIGKDD, AAAI, IJCAI, UAI, AISTATS, ICLR, SIAM SDM), Program Chair for SIAM SDM 2013, ICML 2018, AISTATS 2023, and AAAI 2024. She is an AAAI Fellow.

David John Gagne, NSF National Center for Atmospheric Research (NCAR)

Machine Learning Scientist II and head of the Machine Integration and Learning for Earth Systems (MILES) group at the NSF National Center for Atmospheric Research (NCAR) in Boulder, Colorado.

Research focus: Dr. David John Gagne II has led the development of machine learning systems that enhance understanding and prediction of high impact weather and critical Earth system processes. He received his Ph.D. in meteorology from the University of Oklahoma in 2016 and completed an NCAR ASP Postdoctoral Fellowship before assuming his current role. He is a NSF AI2ES AI Institute leader and a NSF LEAP Science and Technology Center member. He is a WMO Nowcasting and Mesoscale Research Working Group member, chaired the American Meteorological Society Artificial Intelligence Committee, and serves as an editor for the AI for the Earth Systems journal, and has led summer schools, short courses, and hackathons.

Ananth Grama, Purdue University

Samuel Conte Professor of Computer Science

Research focus: parallel and distributed computing architectures, algorithms, and applications. His research focuses on scalable systems for scientific, data-driven, and AI applications, along with their implications for accuracy, generalizability, and robustness. His work ranges from theoretical models and analysis to software solutions and applications.

Jiawei Han

Michael Aiken Chair Professor, Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign

Research focus:  Artificial intelligence, data and text mining, data and information systems

Paul Hanson, University of Wisconsin Madison

Distinguished Research Professor, Center for Limnology

Research focus: carbon cycling, lake metabolism and phytoplankton, modeling, and CI.

Shih-Chieh Hsu, University of Washington

Professor in Physics, Adjunct Professor in Electrical and Computer Engineering. Also serves as the Director of the NSF HDR Institute: Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery (A3D3)

Research focus: Hsu's research focuses on experimental particle physics using data from proton-proton collisions at the Large Hadron Collider. His work spans several critical areas including dark matter searches with the ATLAS experiment neutrino cross-section measurements with the FASER experiment and the development of innovative artificial intelligence algorithms (AI) for data-intensive discovery. Hsu is at the forefront of accelerating machine learning for particle physics applications using heterogeneous computing, integrating real-time AI capabilities to enhance data processing efficiency. By combining cutting-edge particle physics with advanced computational techniques, he aims to probe fundamental questions about the universe through rapid analysis of complex datasets.

Vandana Janeja, University of Maryland Baltimore County

Professor of Information Systems

Research focus: data science with a focus on spatio-temporal mining, data heterogeneity across multiple domain datasets in application areas of climate change, ethics in data science, misinformation detection.

Shuiwang Ji, Texas A&M

Professor and Presidential Impact Fellow, Computer Science & Engineering; leading the RAISE Initiative, aiming at promoting collaborations among foundational AI research, AI for science, and AI for engineering. 

Research focus: His foundational research centers on developing innovative models and algorithms in the fields of machine learning, geometric deep learning, language models and agents. His use-inspired research aims at tackling challenges in various scientific and engineering disciplines, including physics-informed modeling and simulations, biology, drug discovery, quantum physics and chemistry, materials science, molecular dynamics and simulation, fluid dynamics, and partial differential equations, among others.

Xiaowei Jia, University of Pittsburgh

Assistant Professor, Computer Science

Research focus: Dr. Jia's primary research interest is to advance machine learning and data science to solve real-world problems of great societal and scientific impacts. The bulk of his research has been focused on developing data mining and machine learning models that extract complex spatio-temporal data patterns while also leveraging accumulated scientific knowledge. A major highlight of his research is the physics-guided machine learning paradigm that is beginning to get attention in many scientific domains including hydrology, climate science, mechanical engineering, and agriculture. He is the recipient of the NSF CAREER Award and the NASA Early Career Investigator Award.

Anuj Karpatne, Virginia Tech

Associate Professor, Computer Science

Research focus: develop machine learning (ML) methods to solve scientific and societally relevant problems by advancing the emerging field of scientific knowledge-guided machine learning (KGML).

Vipin Kumar, University of Minnesota

Regents Professor, Department of Computer Science & Engineering; William Norris Land Grant Chair in Large-Scale Computing; Director, CSE Data Science Initiative

Research focus: Kumar’s research spans data mining, high-performance computing, and their applications in Climate/Ecosystems and health care.  He has authored over 400 research articles, and co-edited or coauthored 11 books including two text books ``Introduction to Parallel Computing", "Introduction to Data Mining" that are used world-wide and have been translated in to many languages. Kumar’s research over the past several decades has been focused on advancing machine learning to help address some of the biggest challenges facing the humanity in the areas of climate change, food/water security, and health care. One of his recent major contribution has been the creation of a brand-new field of research at the intersection of AI and Science termed knowledge-guided machine learning (KGML), where scientific knowledge is deeply integrated in the design and training of machine learning models to accelerate scientific discovery.  

Yan Liu, University of Southern California

Professor, Computer Science; Director, USC Machine Learning Center , Viterbi School of Engineering 

Research focus: machine learning with applications to health, sustainability, and social media.

Michael Mahoney, UC Berkeley, and Lawrence Berkeley National Laboratory

Professor, Statistics; Vice President and Director of the Big Data Group at International Computer Science Institute; Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory Amazon Scholar. 

Research focus: Michael W. Mahoney is at the University of California at Berkeley in the Department of Statistics and at the International Computer Science Institute (ICSI). He is also an Amazon Scholar as well as a faculty scientist at the Lawrence Berkeley National Laboratory. He works on algorithmic and statistical aspects of modern large-scale data analysis.  Much of his recent research has focused on large-scale machine learning, including randomized matrix algorithms and randomized numerical linear algebra, geometric network analysis tools for structure extraction in large informatics graphs, scalable implicit regularization methods, computational methods for neural network analysis, physics informed machine learning, and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis. More information is available at https://www.stat.berkeley.edu/~mmahoney/.

Madhav Marathe, University of Virginia

Executive Director and Distinguished Professor in Biocomplexity, Biocomplexity Institute; Professor of Computer Science, School of Engineering and Applied Science

Research focus: Research interests are in network science, national security, sustainability, computational epidemiology, artificial intelligence, foundations of computing, socially coupled system science and high-performance computing.

During his 30 year professional career, he has established and led a  number to transdisciplinary team science groups. During this period, he and his colleagues have developed scalable computational methods to study a number of societal complex systems problems, such as urban transport planning, pandemic science and integrated energy systems. Recently, his group has supported federal and state authorities in their effort  to combat the COVID-19 pandemic. Before joining UVA, he held positions at the Virginia Polytechnic Institute and State University (Virginia Tech). He is a fellow of the SIAM, ACM, AAAS, IEEE. He has published more than 500 articles in peer reviewed journals, conferences and workshops.

Raju Namburu, U.S. Army Engineer Research and Development Center

Chief Technology Officer, U.S. Army Engineer Research and Development Center (ERDC)

Research focus: edge computing ecosystem, High-performance computing, DoD AI strategy and quantum sciences.

Rahul Ramachandran, NASA

Senior Research Scientist, NASA, Huntsville, Alabama

Research focus: Dr. Rahul Ramachandran is a Senior Research Scientist at NASA's Marshall Space Flight Center (MSFC) and leads the Inter-Agency Implementation and Advanced Concepts (IMPACT) team. His research interests span a range of topics, including data science, informatics, and AI/ML. Dr. Ramachandran has numerous peer-reviewed publications and has made significant contributions to improve the way we manage and analyze large geospatial datasets leading to a better understanding of our planet and its complex systems. He has held editorial positions in different journals. Dr. Ramachandran is the recipient of numerous accolades and honors, including the Presidential Early Career Award for Scientists and Engineers (PECASE) and the NASA Exceptional Achievement Medal. Dr. Ramachandran was the American Geophysical Union's 2023 Greg Leptoukh Lecture recipient in recognition of his significant contributions to informatics, computational, or data sciences through research, education, and related activities.

Sanjay Ranka, University of Florida

Distinguished Professor, Computer Science & Engineering

Research focus: His research is focused on developing algorithms and software using machine learning and high-performance computing to provide efficient solutions for real-world applications.​ Current projects include the development of machine learning techniques for scientific data reduction, a smartwatch based platform for collecting and analyzing data from elderly adults, and video/LIDAR-based machine learning for traffic safety and operations. His research is funded by the NIA, NSF, DOE, and FDOT.

Shashi Shekhar, University of Minnesota

McKnight Distinguished University Professor and Distinguished University Teaching Professor, Computer Science & Engineering and Director, AI-CLIMATE

Research focus: Shashi Shekhar is a leading scholar of spatial computing and Geographic Information Systems (GIS). Contributions include scalable algorithms for eco-routing, evacuation route planning and spatial pattern (e.g., colocation) mining, along with an Encyclopedia of GIS, a Spatial Databases textbook, and a spatial computing book for professionals. Shashi is a McKnight Distinguished University Professor, a Distinguished University Teaching Professor, and an ADC Chair at the University of Minnesota. He is serving as the Director of a National AI Research Institute, namely, AI-CLIMATE, an Associate Director of his college's Data Science Initiative, a co-chair of the Computing Research Association (CRA) workgroup on socially responsible computing, a co-Editor-in-Chief of the Geo-Informatica journal (Springer), and a general co-chair of the SIAM International Conference on Data Mining (2024). Earlier, he presented at a Congressional reception (2015), co-chaired CRA Snowbird conference (2022), and served as the President of the University Consortium for GIS (UCGIS). He also served as a member of many National Academies' committees and the CRA board. Recognitions include IEEE-CS Technical Achievement Award, UCGIS Education Award, IEEE Fellow and AAAS Fellow.

Ellad Tadmor, University of Minnesota

Russel J. Penrose Professor of Aerospace Engineering and Mechanics at the University of Minnesota. 

Research focus: He pioneered physics-based and machine learning computer simulation methods and theories that span multiple length and time scales to predict the behavior of materials and nanotechnology from their atomic structure. He has published over 80 papers in these areas and two graduate-level textbooks. Professor Tadmor leads several efforts for advancing the quality and effectiveness of scientific research. He is the Founding Director of the NSF Open Knowledgebase of Interatomic Models (https://openkim.org), which is a web-based cyberinfrastructure tasked with developing standards and improving the reliability of molecular simulations, and the ColabFit project (https://colabfit.org) for advancing the use of machine learning in materials science.Tadmor is on the Editorial Board of the Journal of Elasticity.

Valerie Taylor, Argonne National Laboratory

Director of the Mathematics and Computer Science Division

Research focus: performance analysis and modeling of parallel, scientific applications and currently performance analysis, power analysis and resiliency.

Wei Wang, UCLA

Leonard Kleinrock Professor, Computer Science and Computational Medicine

Research focus: big data analytics, data mining, machine learning, natural language processing, bioinformatics and computational biology, and computational medicine. 

Risa Wechsler, Stanford University

Professor, Physics; Professor, Particle Physics and Astrophysics; Director, Kavli Institute for Particle Astrophysics and Cosmology

Research focus: understanding the growth of structure in the universe, how structure formation drives galaxy formation, and how galaxies can be used to probe the fundamental physics of the universe, including the nature of dark matter and dark energy.

Haifeng Xu, University of Chicago

Assistant Professor, Computer Science

Research focus: Haifeng Xu studies economic aspects of data and machine learning, including designing markets for data and ML algorithms and designing learning algorithms for multi-agent decision making. He has published extensively at leading machine learning and computational economics conferences, and serves as area chair or senior program committee for premier venues such as EC, ICML, AAAI, IJCA, etc. His research has been recognized by multiple awards, including a best paper award at the Web Conference, a best student paper at AAMAS, the AI2050 Early Career Fellow, IJCAI Early Career Spotlight, Google Faculty Research Award and ACM SIGecom Dissertation Award (honorable mention); his research has been funded by varied agencies including NSF, ARO, ONR, Schmidt Science, and Google Research.

Aidong Zhang, University of Virginia

Thomas M. Linville Professor, Computer Science, Biomedical Engineering, and Data Science

Research focus: developing machine learning approaches to interpretable and fair learning, concept-based learning, federated learning, and generative AI; also works on large language models for hypothesis generations for scientific discovery.