Participants

Srinivas Aluru is a Regents’ Professor and Senior Associate Dean in the College of Computing at Georgia Institute of Technology. During 2016-2024, he served as Executive Director of the campuswide Interdisciplinary Research Institute in Data Engineering and Science (IDEaS). His academic home is in the School of Computational Science and Engineering, where he served as Interim Chair from June 2019 to August 2020. He co-led the NSF South Big Data Regional Innovation Hub which nurtures big data partnerships among organizations in the sixteen Southern States (2015-present), and the NSF Transdisciplinary Research Institute for Advancing Data Science (2017-2024). Aluru conducts research in high-performance computing, parallel algorithms, data science, bioinformatics and systems biology, and applied artificial intelligence. He is a recipient of the NSF CAREER award, IBM faculty award, Swarnajayanti Fellowship from the Government of India, and the John. V. Atanasoff Discovery Award from Iowa State University. Aluru is a Fellow of the AAAS, ACM, IEEE, and SIAM. He is the 2025 recipient of the IEEE Computer Society Charles Babbage Award.

Arnab Bannerjee is currently an Assistant Professor of Physics and Astronomy at Purdue University. His research interests include synthesis and high field properties of quantum magnets, measures of topological quantum properties, fabrication and testing of quantum devices based on magnetism, and simulations of magnetic systems on quantum computers. The current research of Dr. Banerjee and his interdisciplinary Quantum Spins Laboratory group at Purdue University focuses on the growth and characterization of new quantum magnets with emergent and exotic quantum properties that could be harnessed to make appropriate quantum spintronic devices. These include thermal, dynamical, electrical, and optical characterization of highly frustrated quantum magnets, magnetic topological insulators and semimetals and superconductors both in the bulk and nanoscale at cryogenic temperatures and high fields, to make them device-ready. The applications are complemented by the development of quantum algorithms to study and understand complex behavior in 2D magnetic systems.

Mahdi Bayat is an assistant professor in the Institute for Health Informatics (IHI) at the University of Minnesota. He also serves as the imaging informatics area leader at the Clinical and Translational Science Institute (CTSI) of UMN. The goal of his research is to create highly scalable and computationally accelerated medical imaging and analysis methods to assist in enhanced diagnosis and treatment of diseases and biomedical discoveries. By harnessing the power of AI and deep learning, he aims to enhance the quality, create new modalities, facilitate image analysis, and obtain novel imaging and multi-modality informatics to fill essential gaps in medical research and patient care.

Devesh Bhatt is a Senior Fellow at Honeywell Aerospace Advanced Technology Labs and is currently leading Honeywell’s broad internal and external technology and tool development efforts for the design and verification of software-intensive, safety-critical systems. Recently, the CLEAR requirements language and Text2Test tool have been deployed for significant improvements in the development/certification cost and quality of Honeywell aerospace products. His research interests include methods for specification, construction, and verification of complex safety-critical systems and software. Dr. Bhatt was a co-principal investigator in the DARPA ARCOS (Automated Rapid Certification of Software) program, developing new techniques and tools for assured system/software development. Dr. Bhatt received a B.Tech. in Electrical Engineering from Indian Institute of Technology Kanpur and Ph.D. in EE/CS from Stony Brook University.

Yao-Yi Chiang is an Associate Professor in the Computer Science & Engineering Department at the University of Minnesota. Dr. Chiang's research interests are in spatial artificial intelligence. He develops machine learning methods to understand complex interactions between environmental systems and human activity—often using sparse, uneven, and multi-scale spatiotemporal data. Dr. Chiang has received funding from various government agencies, including NSF, NEH, NIH, DARPA, IARPA, NGA, and industry partners such as NTT Global Networks and BAE Systems. He was a visiting researcher at Google AI, a machine learning consultant at Meta, and the chief scientist at AirMap. In addition, Dr. Chiang founded Kartta Foundation, a non-profit organization that provides software and services to refine and assemble geographic knowledge for the public good. Kartta Foundation manages Kartta Labs, a previous Google product.

Aryan Deshwal is an Assistant Professor of Computer Science & Engineering at the University of Minnesota. His research focuses on artificial intelligence (AI) and machine learning (ML) with an emphasis on advancing foundations of AI/ML to solve challenging real-world problems with high societal impact. The overarching theme of his research program is AI to Accelerate Scientific Discovery and Engineering Design with close collaboration with domain experts to solve high-impact science and engineering challenges.

Caiwen Ding is an Associate Professor in the Department of Computer Science & Engineering at University of Minnesota Twin Cities. His research interests include Algorithm-system co-design of machine learning/artificial intelligence; computer architecture and heterogeneous computing; privacy-preserving machine learning; machine learning for electronic design automation (EDA). He is a recipient of the NSF CAREER Award, Amazon Research Award, and CISCO Research Award. He won several Best Paper Award.

Jana Doppa Jana Doppa is the Huie-Rogers Endowed Chair Professor of Computer Science and Berry Distinguished Professor in Engineering at Washington State University. He was elected as an AAAI Senior Member, was selected for an Early Career Award in AI by the IJCAI Conference and received an NSF CAREER Award. His research is on AI to Accelerate Science and Engineering with a focus on both foundational AI research and use-inspired AI for domains including hardware, material science, agriculture, and additive manufacturing. He and his collaborators won five Best Paper Awards from top-tier AI and electronic design automation venues. He received the WSU Faculty Mid-Career Award; Voiland College of Engineering Anjan Bose Outstanding Researcher Award, Outstanding Junior Faculty in Research Award, and Reid-Miller Teaching Excellence Award.

Christopher J. Duffy is an Emeritus Professor in the Civil and Environmental Engineering Department of Penn State University, and held visiting appointments with Los Alamos National lab (1998-99), Cornell University (1987-88), Ecole Polytechnic Lausanne (2006-07), Smithsonian Institution, University Bristol, UK (20014-2016) University of Bonn, DE (2015). Duffy and his Penn State team focused on developing spatially-distributed, physics-based computational models for multi-scale, multi-process water resources applications supported by automated data services. Past research as PI/Co-PI include: NSF Critical Zone Observatory, NSF INSPIRE, NSF EarthCube, EPA, CNH, DARPA and World Modelers and NSF HDR . Current research involves water resources expertise and consulting for implementation of “Knowledge Guided Machine Learning for Operational Flood Forecasting” with the University of Minnesota.

Chris Florian is a biogeochemist who leads the National Ecological Observatory Network (NEON)’s Terrestrial Instrument Science team, which provides scientific oversight, data processing algorithms, QA/QC, and user support for the automated instrument data products at NEON’s 47 terrestrial field sites. Chris has been working for Battelle’s NEON program since 2017. Before moving to his current role, he supported the Surface-Atmosphere Exchange data products which quantify exchange of sensible heat, water, and carbon between the ecosystem and the atmosphere. He has also supported other efforts at Battelle, including the NSF-funded Biology Guided Neural Networks and Imageomics projects, which focus on leveraging machine learning to answer biological questions. Before joining Battelle, Chris received a BA in Ecology and Evolutionary Biology from the University of Colorado, Boulder, and a dual PhD in Geology from the University of Colorado, Boulder, and the University of Iceland.

Dr. Jasmine Foo is a Distinguished McKnight University Professor and Northrop Professor at the School of Mathematics, University of Minnesota.  She leads a research group that develops and applies stochastic models to understand cancer initiation, tumor progression, and treatment response. By integrating these models with clinical and experimental data, her work aims to improve the design of more effective cancer therapies. Her group is also interested in developing novel statistical frameworks that integrate clinical information with data from novel ex-vivo experimental platforms to produce clinically actionable predictions.

Baskar Ganapathysubramanian is the Joseph C. and Elizabeth A. Anderlik Professor in Engineering and Professor of Mechanical Engineering at Iowa State University. His research leverages applied mathematics, scientific computation, and machine learning to model, design, and control complex systems with application to food, energy and environment, and health. He is particularly interested in energy and environment related phenomena. Recent examples include flow physics across complex geometries (buildings, vehicles),   charge transport in organic electronic devices and electrochemical systems, coupled phenomena during soft matter manufacturing, and enabling resilient agriculture. We develop mathematical techniques and computational tools — model reduction, multiscale frameworks, multiphysics simulators, control algorithms, data-driven methods — to efficiently represent these systems.

Joydeep Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin, where he joined the faculty in 1988 after finishing his Ph.D. at USC. He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab). Dr. Ghosh's research interests lie primarily in data mining and web mining, ethical/trustworthy/responsible AI, scalable machine learning algorithms, especially for predictive and prescriptive analytics, and applications to a wide variety of complex real-world problems, including health informatics. Dr. Ghosh has taught graduate courses on data mining and web analytics to both UT students and to industry, for over three decades and has been voted as "Best Professor" multiple times by students. He has also received several top awards for lifetime research contributions, including the IEEE CS Technical Achievement Award (2015) and ICDM Research Contributions Award (2020, Citation ) for lifetime research contributions to Data Mining and Machine Learning.

Ananth Grama is the Samuel D. Conte Distinguished Professor of Computer Science and Associate Director of the Center for Science of Information at Purdue University. His research focuses on parallel and distributed computing with applications in modeling, design, advanced manufacturing, machine learning, and artificial intelligence for complex physical systems. His work on computer systems focuses on load balancing, resource management, data management, and security. His recent work on algorithms and analysis focuses on establishing fundamental bounds on hallucinations, online learning, learning in faulty and private environments, and quantum machine learning. He applies these systems concepts and algorithms to a range of applications, including materials modeling, systems biology, transcriptomics, clinical analytics, and structural design.

Paul Hanson is a Distinguished Research Professor and ecosystems ecologist at the University of Wisconsin–Madison, Center for Limnology. His work explores how climate change, land use, and human activity affect the quality of lakes and freshwater systems. Paul is known for leading interdisciplinary teams and international collaborations, often working at the intersection of ecology, computer science, and engineering. He directs the Environmental Data Initiative (EDI), a national effort to make environmental data more accessible, reusable, and impactful. He also co-founded the Global Lake Ecological Observatory Network (GLEON) Fellows Program, which trains graduate students in team science and big data approaches for studying lakes. In the past decade, his collaborative research has focused on knowledge guided machine learning to improve predictions of water quality and on application of AI methods to improve metadata annotation for EDI. 

Jarvis Haupt is an Associate Professor and the Associate Department Head in the Department of Electrical and Computer Engineering at the University of Minnesota. His research interests generally include statistical signal processing, machine learning, and optimization, with recent efforts exploring fundamental aspects of non line-of-sight imaging, problems in magnetic resonance imaging reconstruction, and the optimization dynamics associated with training deep neural networks. His PhD advisees received best student paper awards at the IEEE Global Conference on Signal and Information Processing and the International Workshop on Structural Health Monitoring. He previously served as an Associate Editor for the IEEE Transactions on Signal Processing and is currently Chair of the arXiv Electrical Engineering and System Science (EESS) Editorial Committee.  He is a past recipient of the DARPA Young Faculty Award and was the inaugural recipient of the University of Minnesota ECE Department’s Russell J. Penrose Excellence in Teaching Award.

Qizhi (“KaiChi”) He is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota. He received his M.A. in Applied Mathematics (2016) and Ph.D. in Structural Engineering and Computational Science (2018) from UC San Diego. Prior to joining UMN, he was a postdoctoral research associate in the Scientific Machine Learning Group at Pacific Northwest National Laboratory. Dr. He's research focuses on predictive modeling and simulation of complex mechanical behavior in civil and geo-materials under extreme multiphysics loading conditions. His work aims to develop scalable, hybrid AI–physics frameworks for geohazard analysis, digital twins, and the resilient design of materials and structures. He currently serves on the ASCE/EMI technical committees on Computational Mechanics and Machine Learning in Mechanics and is a member of the editorial board for Computers and Geotechnics.

Mingyi Hong is currently an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota, Minneapolis. His research has been focused on developing optimization theory and algorithms for applications in signal processing, machine learning and foundation models. He is a Senior Area Editor for IEEE Transactions on Signal Processing. His work has received two IEEE Signal Processing Society (SPS) Best Paper Awards (2021, 2022), an International Consortium of Chinese Mathematicians Best Paper Award (2020), and a few Best Student Paper Awards in signal processing and machine learning conferences. He is an Amazon Scholar, and he is the recipient of an IBM Faculty Award, Meta research awards, Cisco Research Awards, and the 2022 Pierre-Simon Laplace Early Career Technical Achievement Award from IEEE SPS. He is an IEEE Fellow.

Lior Horesh is a Principal Research Scientist, Master Inventor and Senior Manager of the Mathematics & Theoretical Computer Science department at IBM Research. His research focuses on algorithmic and theoretical aspects of tensor algebra, numerical analysis, inverse problems, machine learning, quantum computing and the interplay between statistical and symbolic AI for scientific discovery. Horesh's research includes design of the AI-Descartes system, a Generator-Verifier Duo proposition, where symbolic regression-based hypothesis generation is coupled with formal Automatic Theorem Prover (ATP) verifier. The latter, qualify whether, and geometrically, to what extent, candidate hypotheses conform with background theory. Additionally, he led the development of AI-Hilbert, an 'AI Scientist' that leverages algebraic geometry to simultaneously integrate symbolic background theory and numerical data into substantiated symbolic models. This unified approach, marks a step towards evolving the scientific method itself, rather than target its automation or acceleration.

Surya Iyer is President & COO at Polar Semiconductor, and previously held senior management and engineering positions at Cypress Semiconductor Corp. and Applied Materials, Inc. The Walz-Flanagan administration appointed Surya as the Chair of the Governor’s Workforce Development Board, where he serves to create the future generations of Minnesota’s skilled, diverse, and globally competitive workers. Surya serves as a member of the Governing Council for SEMI/FOA (a semiconductor industry association), is a member of U.S. Department of Commerce’s NSTC Workforce Advisory Board (WFAB), a member of the Smart Manufacturing group at SEMI Americas and is an adjunct Professor of Engineering at the University of St. Thomas. Surya attended Wharton, was a fellow at Stanford University, received his post-graduate degrees from Washington University in St. Louis, and an undergraduate engineering degree from IIT-Kanpur. He has 20 patents related to semiconductor processing and equipment design and over 25 peer-reviewed publications.

H. V. Jagadish is Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science at the University of Michigan in Ann Arbor, and Director of the Michigan Institute for Data Science.  Prior to 1999, he was Head of the Database Research Department at AT&T Labs, Florham Park, NJ. Professor Jagadish is well known for his broad-ranging research on information management, and has over 300 major papers and 38 patents, with an H-index of 104.  He is a fellow of the ACM, "The First Society in Computing," (since 2003) and of AAAS (since 2018).  He currently chairs the board of the Academic Data Science Alliance and previously served on the board of the Computing Research Association (2009-2018).  He has been an Associate Editor for the ACM Transactions on Database Systems (1992-1995), Program Chair of the ACM SIGMOD annual conference (1996), Program Chair of the ISMB conference (2005), a trustee of the VLDB (Very Large DataBase) foundation (2004-2009), Founding Editor-in-Chief of the Proceedings of the VLDB Endowment (2008-2014), and Program Chair of the VLDB Conference (2014).  Since 2016, he is Editor of the Springer (previously Morgan & Claypool) “Synthesis” Lecture Series on Data Management.  Among his many awards, he won the ACM SIGMOD Contributions Award in 2013, and the Distinguished Alumnus Award (at IIT Delhi) in 2020.  His popular MOOC on Data Science Ethics is available on both EdX and Coursera.

Anuj Karpatne is an Associate Professor of Computer Science at Virginia Tech where he leads the Knowledge-guided Machine Learning (KGML) Lab. His research vision is to establish KGML as a thriving area of research that serves as a nucleus for foundational innovations in AI/ML to produce scientifically grounded, explainable, and generalizable solutions, driven by inter-disicplinary problems in science that impact society.

Deborah Khider is a Lead Scientist at the University of Southern California’s Information Sciences Institute. Her research lies at the intersection of geoscience and artificial intelligence, with a particular focus on advancing paleoclimatology through computational methods. She develops AI-driven tools for data annotation and retrieval, designs reproducible and AI-assisted workflows for paleoclimate data analysis, and applies deep learning techniques to generate climate-relevant predictions. Her work aims to accelerate scientific discovery by improving access to, and interpretation of, complex paleoclimate datasets. She is also an active contributor to open science and FAIR data initiatives in the geosciences.

Jeffrey Kwang is a Physical Scientist for the USGS Water Resources Mission Area.  He is part of the Data Science Branch where he helps create interactive data visualizations to better communicate USGS science and numerical models to improve our predictions of sediment dynamics. He has expertise in ​​geomorphology, landscape evolution, numerical modeling, river morphodynamics, and data visualization​.

Kwang uses numerical modeling and remote sensing to study how landscapes evolve over a broad range of temporal and spatial scales. Prior to joining the USGS, he researched the role of autogenic dynamics and rock heterogeneity in reorganizing river networks. He also built high-resolution, region-scale models that predicted how agricultural practices have redistributed soil and soil organic carbon. 

Vipin Kumar is Regents Professor at the University of Minnesota, Department of Computer Science & Engineering; William Norris Land Grant Chair in Large-Scale Computing; and Director of the  CSE Data Science Initiative. His 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.  

Zac McEachran is a hydrologist and catchment scientist at the University of Minnesota. He previously worked at the NOAA National Weather Service. His research focuses hydrology, catchment science, machine learning, data science, forecasting, agricultural systems and envirotyping. He also works on using advanced physics based and machine learning modeling tools to help understand the fundamental physical processes of how streamflow is generated at the catchment scale. He is particularly interested in creating feedbacks between developing better operational environmental forecasts and better understanding of catchment processes.

David Mulla is the Larson Chair and Professor for Soil & Water Resources in the Department of Soil, Water, and Climate at the University of Minnesota, Chair for the Board of Directors at the Precision Agriculture Center, and a member of the Executive Committee for the National Institute for Artificial Intelligence on Land, Economy, Agriculture, and Forestry (AI-LEAF).  Dr. Mulla received a Ph.D. degree in Agronomy from Purdue University with emphasis in soil physics.  Dr. Mulla’s research emphasizes precision conservation, water quality, ecosystem services, and AI/ML/geospatial modeling in agriculture. He is widely recognized as a pioneer in precision agriculture.  He was elected a Fellow in Soil Science Society of America (SSSA) and Agronomy Society of America (ASA) in 1997 and 1999, respectively; was awarded the P.C. Robert Senior Precision Agriculture Research Award in 2012 by the International Society for Precision Agriculture; and was awarded the Soil Science Applied Research Award in 2013 by the SSSA. 

Raju Namburu leads the development of the Information Technology Laboratory’s (ITL) computational sciences and engineering and future advanced computing technologies. He represents DoD as a member for the US National Science and Technology council (NSTC) subcommittee for the future advanced computing echo system. Prior to coming to ITL, Dr. Namburu was Chief Scientist (CS), Division Chief -Computational Sciences Division and Director-DoD Supercomputing Resource Center Director at the Computational and Information Sciences Directorate (CISD), U.S. Army Research Laboratory (ARL) in managing 150 scientist and engineers. Dr Namburu was the chief architect in developing the computational sciences campaign strategy for the U.S. Army. As program manager, Dr. Namburu directed and guided one of the Army's premier computational sciences multi-University Consortium, Army High Performance Computing Research Center (AHPCRC) led by Stanford University (2006-2016) and at the University of Minnesota (2000-2006) for the Department of the Army. In addition to strong student outreach programs in HPC, AHPCRC developed key software to solve critical DoD applications on HPC from high-fidelity simulations to graph partitioning to data mining to physics informed machine learning. Dr. Namburu is the working group lead for information systems technology under DoD C4I community of interest and is actively involved in developing various activities for DoD in edge computing ecosystem, High-performance computing, DoD AI strategy, Digital twins, and quantum sciences. Dr. Namburu led various DoD scalable software development projects in computational sciences including establishing DoD mobile network modeling institute.

Nikunj Oza is leader of the Data Sciences Group at NASA Ames Research Center. He received the Arch T. Colwell Award for co-authoring one of the five most innovative technical papers selected from 3300+ SAE technical papers in 2005. In 2019, he was named by Cognilytica as one of 50 key people in the US government working to move the adoption of artificial intelligence forward across industry. Dr. Oza has been one of two NASA representatives to the NITRD Artificial Intelligence (AI) Interagency Working Group (IWG) and the White House OSTP Machine Learning and Artificial Intelligence Subcommittee since 2016. In 2025, Dr. Oza was selected to be a co-chair of the NITRD AI IWG. He received his B.S. in Mathematics with Computer Science from MIT in 1994, and M.S. (in 1998) and Ph.D. (in 2001) in Computer Science from the University of California at Berkeley.

Serguei Pakhmonov is a Professor of Health Informatics at the University of Minnesota College of Pharmacy. He holds a Ph.D. in Linguistics from the University of Minnesota and an M.S. in Biomedical Sciences from the Mayo College of Medicine. He has over 20 years of experience in the field of health informatics that has so far resulted in over 150 peer-reviewed publications. His current research focuses on evaluating and developing novel computational approaches to natural speech and language processing in the biomedical domain, including text of medical records and speech and language produced by patients during cognitive testing. Specifically, He studies effects of medications and neurodegenerative disorders on spontaneous speech and language characteristics and develop novel methods for measuring these effects with machine learning techniques including Artificial Intelligence approaches.  Dr. Pakhomov currently serves as the Natural Language Processing (NLP) Director of the Clinical and Translational Science Institute at the University of Minnesota where he provides thought leadership concerning the development and evaluation of modern NLP methods including those that use Large Language Models for information extraction from medical records and speech and language data collected from patients in research and clinical settings. He also leads the Cognitive Artificial Intelligence Research (CAIR) Lab in the College of Pharmacy dedicated to investigating applications of computational linguistics, artificial intelligence and cognitive science methods in healthcare.

Hoifung Poon is General Manager at Health Futures in Microsoft Research and an affiliated faculty at the University of Washington Medical School. He leads biomedical AI research and incubation, with the overarching goal of structuring medical data to optimize delivery and accelerate discovery for precision health. His team and collaborators are among the first to explore large language models (LLMs) and multimodal generative AI in health applications, producing popular open-source foundation models such as PubMedBERT, BioGPT, BiomedCLIP, LLaVA-Med, BiomedParse, with tens of millions of downloads. His latest publication in Nature features GigaPath, the world’s first whole-slide digital pathology foundation model. His prior work has been recognized with Best Paper Awards from premier AI venues, and he was named the "Technology Champion" by the Puget Sound Business Journal in the 2024 Health Care Leadership Awards. He received his PhD in Computer Science and Engineering from the University of Washington.

Sudarsan Rachuri is a Technology Manager in the Advanced Manufacturing Office, EERE, and DOE. He is the Federal Program Manager for CESMII. and CYMANII. Prior to joining DOE, he was the program manager at the National Institute of Standards and Technology (NIST) and also a research professor at George Washington University and worked in the CAD/CAE/PLM software industry.

Dr. Rachuri is serving as Editor-in-Chief of ASTM International’s journal, Smart and Sustainable Manufacturing Systems (www.astm.org/ssms). Rachuri is also a founding member and served as the vice-chair of the ASTM subcommittee on sustainable manufacturing (E60.13). He also serves on ASTM International's Smart Manufacturing Advisory Committee. Rachuri is the founding member and the Chair of the standards committee on ASME V&V 50 Verification and Validation of Computational Modeling for Advanced Manufacturing.  Dr. Rachuri is a Fellow of ASME and AAAS. He received the 2016 ASTM International President’s Leadership Award and won first prize in the 2017 World Standards Day (WSD) Paper Competition, awarded by The Society for Standards Professionals. Dr. Rachuri was honored with the Excellence in Research Award by the American Society of Mechanical Engineers (ASME) Computers and Information in Engineering (CIE) Division. He is leading the effort to develop a national plan for smart manufacturing with National Academies.

Jordan Read is the Chief Executive Officer of CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.). He steers the organization’s overall strategy, cultivates partnerships, and secures long‑term sustainability. Before joining CUAHSI, he founded and led the U.S. Geological Survey’s Water Data Science Branch, expanding national capabilities in machine learning, data visualization, and reproducible science. Jordan’s research focuses on blending deep learning approaches with process‑based models and applying advanced AI to accelerate efficient, high‑impact scientific discovery.

Mike SanClements, PhD, is Director of Research Initiatives at Battelle, the world’s largest non-profit R&D firm. From 2015-2021 Mike led the NEON Terrestrial Instrument Science Team.  Mike has served as Chair Elect of the Soil Science Society of America and Chair of the Global Ecological Research Infrastructures. He holds a B.S. in Resource Conservation (University of Montana), an M.S. in Soil Science (North Carolina State University), and a Ph.D. in Ecology and Environmental Sciences (University of Maine). Mike is also the author of "Plastic Purge," a 2014 Books for a Better Life Award finalist, and lives in Boulder, Colorado, with his wife Mary and daughter Hadley.

Sachin S. Sapatnekar received his Ph.D. from the University of Illinois at Urbana-Champaign in 1992. He holds the Henle Chair in Electrical and Computer Engineering and is a Distinguished McKnight University Professor at the University of Minnesota. His research interests include novel  computational methods for automating the design of analog and digital integrated circuits, particularly the use of machine learning techniques to advance integrated circuit design. He is a recipient of 12 Best Paper Awards, the Semiconductor Research Corporation's Technical Excellence Award, and the Semiconductor Industry Association University Research Award. He is a Fellow of the IEEE and the ACM.

Sapna Sarupria is an associate professor in the department of Chemistry at the University of Minnesota, Twin Cities (UMN). Before joining UMN in Fall 2021, she was an associate professor in the department of Chemical and Biomolecular Engineering at Clemson University. She received her Master’s from Texas A & M University and her Ph.D. from Rensselaer Polytechnic Institute. She was a postdoctoral researcher at Princeton University. Her research focuses on using molecular simulations to tease out the underlying phenomena governing material behavior. Her research lab integrates molecular simulations and machine learning, and works closely with wet-lab experimentalists. She received the NSF CAREER award, ACS COMP Outstanding Junior Faculty Award, Clemson’s Board of Trustees Award of Excellence and the CoMSEF Impact Award. She is the co-founder of the NSF-funded Institute of Computational Molecular Science Education (I-CoMSE). She is also the co-director of the NSF-funded National Research Traineeship program (NRT) Data-Driven Discovery and Engineering from Atoms to Processes (3DEAP) housed in the CEMS and Chemistry departments at UMN.

Shashi Shekhar is a McKnight Distinguished University Professor and Distinguished University Teaching Professor, Computer Science & Engineering, at the University of Minnesota, and Director of AI-LEAF. He 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-LEAF, 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.

Chaopeng Shen is Professor in Civil Engineering at The Pennsylvania State University. He received a Ph.D. in environmental engineering from Michigan State University, USA, in 2009. His PhD research focused on computational hydrology, and he developed the hydrologic model Process-based Adaptive Watershed Simulator (PAWS), which was later coupled to the community land model to study the interactions between hydrology and ecosystem. He was a Post-Doctoral Research Associate with the Lawrence Berkeley National Laboratory, Berkeley, USA, from 2011 to 2012, working on high-performance computational geophysics. His recent efforts focused on harnessing the big data and machine learning (ML) and physics-informed ML opportunities in advancing hydrologic predictions and understanding. As an early advocate for ML in geosciences, he has written technical, editorial, review and collective opinion papers on hydrologic deep learning to call to attention the emerging opportunities for scientific advances. He currently promotes differentiable modeling which seamlessly integrates neural networks and physics for knowledge discovery. In addition, his research interests also include floodplain systems, scaling issues, process-based hydrologic modeling, and hydrologic data mining. He is currently Editor of Journal of Geophysical Research - Machine Learning & Computation, Chief Special Editor for Frontiers in AI: Water and AI, and an Associate Editor of the Water Resources Research.

Ram D. Sriram is currently the chief of the Software and Systems Division, Information Technology Laboratory, at the National Institute of Standards and Technology (NIST). Prior to joining NIST, he was on the engineering faculty at the Massachusetts Institute of Technology (MIT). Sriram has co-authored or authored nearly 300 publications, including several books. For his contributions over the past four decades Ram has received many awards, including four life achievement/pioneer awards and a DAA from IIT Madras, India. He has also been made a fellow of prominent engineering (ASME, IET, INCOSE, SME, SMA), computer science (ACM, AIAA, IEEE), medical (AIMBE), and science (AAAS, WAS) societies. Sriram has a B.Tech. from IIT, Madras, India, and an M.S. and a Ph.D. from Carnegie Mellon University, Pittsburgh, USA.

Michael Steinbach earned his B.S. degree in Mathematics, an M.S. degree in Statistics, and M.S. and Ph.D. degrees in Computer Science from the University of Minnesota. He is currently a researcher in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, working in the research group of Prof. Vipin Kumar.  His research interests are in the area of data mining, machine learning, biomedical informatics, and statistics.  Dr. Steinbach is a co-author of the data mining textbook, Introduction to Data Mining, published by Addison-Wesley, which is used world-wide and has been translated into many languages.  Previously, he held a variety of software engineering, analysis, and design positions in industry at Silicon Biology, Racotek, and NCR.

Ganesh Subbarayan is the James G. Dwyer Professor of Mechanical Engineering at Purdue University. He serves as the co-director of the SRC Center for Heterogeneous Integration Research in Packaging (CHIRP), and the director of Purdue’s Atalla Institute for Advanced System Integration and Packaging (ASIP). He is a founding executive committee member of the recently formed CHIPS Manufacturing USA (SMART USA) Institute on Digital Twins. He began his professional career at IBM Corporation (1990-1993). He holds a B.Tech degree in Mechanical Engineering (1985) from the Indian Institute of Technology, Madras and a Ph.D. (1991) in Mechanical Engineering from Cornell University. Among others, Dr. Subbarayan received the 2024 Richard Chu Award for Excellence in Thermal and Thermo-Mechanical Management of Electronics, 2022 SRC Technical Excellence Award, 2005 Excellence in Mechanics Award from the ASME Electronics and Photonics Packaging Division. He is a Fellow of ASME as well as IEEE.

Ju Sun is an assistant professor at the Department of Computer Science & Engineering, the University of Minnesota at Twin Cities (UMN). His research interests span computer vision, machine learning, numerical optimization, data science, computational imaging, and healthcare. His recent efforts are focused on the foundation and computation for deep learning and applying deep learning to tackle challenging science, engineering, and medical problems. Before this, he worked as a postdoc scholar at Stanford University (2016-2019), and obtained his Ph.D. degree from Columbia University's Electrical Engineering in 2016. He won the best student paper award from SPARS'15, honorable mention of doctoral thesis for the New World Mathematics Awards (NWMA) 2017, and AAAI New Faculty Highlight Programs 2021, Frontiers of Science Award in Mathematics 2024, and the McKnight Land-Grant Professorship of UMN 2025-2027.

Nathan Szymanski is a postdoctoral researcher in Chemical Engineering and Materials Science at the University of Minnesota. He earned dual Bachelor of Science degrees in Physics and Applied Mathematics from the University of Toledo before pursuing his Ph.D. in Materials Science and Engineering at UC Berkeley under the guidance of Professor Gerbrand Ceder. During his Ph.D., Nathan developed computational methods to streamline materials synthesis and characterization, combining quantum chemistry calculations with machine learning to identify promising battery materials, design synthesis pathways, and interpret experimental data. He was an NSF Graduate Research Fellow and received the Didier de Fontaine Award, which recognizes the top graduate student in computational materials research at Berkeley. Nathan currently works with Professor Chris Bartel at Minnesota on the development of generative AI models and the use of thin-film deposition as a route to synthesize predicted materials. 

Yoga Varatharajah is currently an Assistant Professor in the Department of Computer Science & Engineering at the University of Minnesota Twin Cities. He is broadly interested in leveraging the recent advances in machine learning (ML) to improve the healthcare system. For his research studies, he works very closely with clinical experts to develop novel domain-guided ML applications that reduce physician burden, augment their capabilities, and enhance the overall patient experience while ensuring reliability, scalability, and trust. The ML topics he focuses on are domain-guided ML, self-supervised learning, generative modeling, trustworthy ML, robustness, and ML safety. A particular focus area of his work has been on improving the treatments for neurological diseases, where he developed novel graph-based ML methods to model brain activity patterns in diseases such as Alzheimer's and epilepsy.