Participants

Eric D. Bloch is the Veronica Seidle Associate Professor of Chemistry at Indiana University Bloomington, where his research focuses on the design and application of porous molecular and framework materials for gas storage and separations, catalysis, sensing, and energy-related applications. His group develops metal–organic frameworks, porous coordination cages, and porous molecular solids with tailored structures and functions, combining synthetic chemistry with advanced characterization techniques to understand structure–property relationships. Before joining Indiana University, he was on the faculty at the University of Delaware. Bloch received his B.S. from the University of Wisconsin-Milwaukee, and his Ph.D. from the University of California-Berkeley, followed by postdoctoral research at Harvard University. 

J. Taylor Childers is a computational scientist at Argonne National Laboratory focused on enabling science through supercomputing. Specifically, he explores floating-point precision in the age of low-precision hardware, leverages architecture-independent programming, and investigates how scientists' AI agents can accelerate and improve research outcomes. After completing his PhD in Physics at the University of Minnesota, he held a post-doctoral research position at Heidelberg University, followed by a fellowship at CERN. During LHC Run 1 on the ATLAS experiment, his work centered on trigger electronics and top physics, and he is a co-author of the historic Higgs boson discovery paper. More recently he is involved in the DOE Genesis Mission as a lead in the American Science Cloud project.

Michael Coughlin is an Associate Professor of Physics at the University of Minnesota, where he is building a group working in this new era of data science as applied to multi-messenger astronomy.

Aryan Deshwal is an Assistant Professor in the Department of Computer Science and Engineering (CS&E) at University of Minnesota. His research agenda is AI to Accelerate Scientific Discovery and Engineering Design where he focuses on advancing foundations of AI/ML to solve challenging real-world problems with high societal impact in collaboration with domain experts. He won the College of Engineering Outstanding Dissertation Award for his PhD. He was selected as AAAI New Faculty Highlights Speaker (2025) and Rising Stars in AI by KAUST AI Initiative (2023). His research has received NSF CAREER award, the Innovative Deployed Application Award at IAAAI 2026 and he won multiple outstanding reviewer awards from machine learning (ICML (2020), ICLR (2021), and ICML (2021)) conferences.

Ananth Grama is the Samuel Conte Distinguished Professor of Computer Science and Director of the Institute for Physical Artificial Intelligence at Purdue University. He received his PhD from the University of Minnesota in 1996 and has been at Purdue since. Ananth works on large-scale computing systems, AI models and methods, and their applications in diverse scientific and engineering domains.

Tarek Haddad is a Bakken Fellow, Technical Fellow, and Senior Director of the AI Research Group within Cardiac Implantables Research & Technology at Medtronic. He leads the development of artificial intelligence, machine learning, and stochastic modeling technologies that improve patient outcomes, increase clinical and operational efficiency, and enhance the experience of patients and healthcare providers.

Over his Medtronic career, Tarek has contributed across a broad range of innovation areas, including cardiac lead design, the first Micra system, Bayesian clinical trial methods, digital twin–informed evidence generation, and AI-enabled product development. He also helped establish the AI Center of Excellence within Cardiac Rhythm Management, building the talent, infrastructure, and capabilities needed to deliver multiple AI products and support Medtronic’s broader AI strategy.

He holds an MS in Biostatistics and a BA in Mathematics from the University of Minnesota and is a Master Black Belt in Design for Six Sigma, Design for Reliability, and Manufacturing.

Dr. Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech (VT), where he also serves as a College of Engineering Faculty Fellow and Dean’s Fellow. His research advances the field of knowledge-guided machine learning (KGML), integrating scientific knowledge with AI methods to enable discovery across domains including climate science, hydrology, ecology, geophysics, biology, mechanobiology, quantum mechanics, and fluid dynamics. His honors include the COE Faculty Fellow Award for Excellence in Research (2025), NAIRR Pilot Award with an invited presentation at the White House (2024), NSF CAREER Award (2023), COE Outstanding New Assistant Professor Award (2022), CS Rising Star Faculty Award (2021), and IS-GEO Research Fellow recognition (2019). He serves as Associate Editor for ACM Transactions on Knowledge Discovery from Data (TKDD). He is a co-author of Introduction to Data Mining (2nd edition) and lead editor of Knowledge-guided Machine Learning.

Vincenzo Lordi is the Deputy Division Leader for Science and Technology of the Materials Science at Lawrence Livermore National Laboratory (LLNL). He received his Ph.D. in Materials Science in 2004 and M.S. in Electrical Engineering in 2002 from Stanford University, and his B.S.E. in Chemical Engineering with minors in Applied Mathematics and Materials Science from Princeton University in 1999. His research includes computational materials science from the atomistic to device scale, as well as materials characterization with an emphasis on X-ray, optical, and electron spectroscopies and imaging. Recent research has included the application of machine learning and artificial intelligence to atomistic material simulations, materials for quantum information devices, materials under extreme conditions, optoelectronic materials, and materials for energy applications. He joined LLNL in 2006, following an industrial research position at KLA-Tencor Corp. He was a Hertz Graduate Fellow, Lawrence Postdoctoral Fellow, and Scowcroft National Security Fellow.

Dr. Stella S. Offner is a Professor of Astronomy and a core faculty member in the Oden Institute for Computational Engineering and Sciences. She is the Director of the NSF-Simons AI Institute for Cosmic Origins. Her research interests include star and planet formation, computational astrophysics, high performance computing, and scientific machine learning.

Nikunj Oza leads the Data Sciences Group at NASA Ames Research Center and is AI advisor for NASA’s Earth Science Technology Office. He 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, he was selected co-chair of the NITRD AI IWG. His 60+ research papers represent his research interests: machine learning, anomaly detection, and their applications. He received the Arch T. Colwell Award for one of the five most innovative technical papers among 3300+ SAE technical papers in 2005, and the 2018 NASA Honor Award (team). In 2019, he was named by Cognilytica as one of 50 key US government employees moving the adoption of AI forward across the industry. He received his B.S. in Mathematics with Computer Science from MIT, and M.S. and Ph.D. in Computer Science from the University of California, Berkeley.

Manish Parashar is the inaugural chief artificial intelligence officer at the University of Utah. He is also the executive director of the university’s Scientific Computing and Imaging Institute—home to the $100 million One-U Responsible AI Initiative—and a presidential professor in the Kahlert School of Computing. As the former office director of the National Science Foundation’s Office of Advanced Cyberinfrastructure and co-chair of the National AI Research Resource Task Force, Parashar is a leader in high-performance parallel and distributed computing, large-scale data management, and cyberinfrastructure. His efforts have enabled new insights across multiple scientific domains and earned him several honors, including the Institute of Electrical and Electronics Engineers (IEEE) Computer Society Sidney Fernbach Award and distinguished service awards from the Association for Computing Machinery (ACM) and the Computing Research Association. He is a fellow of the American Association for the Advancement of Science, ACM, and IEEE.

Dr. Jin Qian is an early-career staff scientist in the Chemical Sciences Division at Lawrence Berkeley National Laboratory (LBNL). She obtained her Ph.D from California Institute of Technology (Caltech) in 2019 and established her independent research group at LBNL in 2021. She is a recipient of several prestigious awards, including the United States Department of Energy Early Career award, and the LBNL director's Laboratory Directed Research and Development award. Dr. Qian's research focuses on developing and applying advanced theoretical and computational tools, such as Digital Twin and Real-Space KS-DFT, to understand complex chemical dynamics that are central to energy science.

Dr. Rahul Ramachandran is a Senior Research Scientist at NASA’s Marshall Space Flight Center, where he directs the center’s data science and artificial intelligence initiatives. He leads the Pathfinder AI for Science Portfolio for the Office of the Chief Science Data Officer, overseeing the development of foundation models including Prithvi-EO, Prithvi-WxC, and Surya Helio FM. Dr. Ramachandran is working on the concept of Accelerated Knowledge Discovery, a framework shifting AI from an analytic tool to a scientific collaborator through agentic software. His operational leadership includes modernizing the Global Hydrology Resource Center DAAC into NASA’s first cloud-native archive and establishing the Satellite Needs Working Group Management Office to align federal priorities with NASA’s Earth observations. A recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), NASA Exceptional Achievement Medal and AGU Leptoukh Lecture, Dr. Ramachandran fosters cross-sector partnerships to support open science and collaborative research.

Subramanian Sankaranarayanan is a senior scientist and group leader in the Theory and Modeling Group at the Center for Nanoscale Materials, Argonne National Laboratory, and a Professor of Mechanical and Industrial Engineering at the University of Illinois Chicago. His research focuses on computational materials science, atomistic and multiscale simulations, artificial intelligence for materials discovery, and microelectronics materials. His group develops physics-informed AI, molecular simulation, and autonomous workflow approaches to understand and design materials for energy, electronics, catalysis, interfaces, and nanoscale systems.

Dr. Amarda Shehu is a Professor of Computer Science, Associate Dean for Research, and Vice President and Chief AI Officer at George Mason University. In these roles, she leads Mason’s institution-wide AI strategy spanning research, education, workforce development, and external partnerships. She previously directed the Institute for Digital Innovation and has launched multiple transdisciplinary centers to accelerate cross-campus collaboration and translation. Shehu is also the primary architect of Mason’s AI education and literacy pipeline, including the university’s new M.S. in Artificial Intelligence degree and the general-education “AI for All” course. She chairs the university’s AI-in-Government Council, convening academia, industry, and public agencies to advance responsible, mission-driven AI adoption. Her national service includes prior work as an NSF Program Director in the CISE Directorate (2019–2022), and she continues to help shape AI, biosecurity, and innovation agendas across academia, government, and industry. An active AI researcher, Shehu has published over 200 papers with students and collaborators, sustaining a long-running research program at the intersection of AI and molecular biology that pioneers probabilistic, machine learning, and generative methods for protein science, genomics, and molecular design. Her contributions have been recognized through multiple awards for research, education, mentorship, and service, and by professional honors including election as a 2022 Fellow of the American Institute for Medical and Biological Engineering, IEEE Senior Member status, and membership in the Virginia Academy of Sciences, Engineering, and Medicine.

Dr. Sparks is a Professor of Materials Science and Engineering at the University of Utah. He holds a BS in MSE from the UofU, MS in Materials from UCSB, and PhD in Applied Physics from Harvard University. He was a Royal Society Wolfson Visiting Fellow at the University of Liverpool and a recipient of the NSF CAREER Award and a speaker for TEDxSaltLakeCity. He is active in TMS, MRS, and ACERS societies. He is currently the Editor-in-Chief for the Integrating Materials and Manufacturing Innovation and the Director of Graduate Affairs for the John and Marcia Price College of Engineering. When he’s not in the lab you can find him running his podcast “Materialism,” creating materials educational content for his YouTube channel, or canyoneering with his 4 kids in southern Utah.

Brian Stucky is a Computational Biologist and Acting Chief Scientific Information Officer with the U.S. Department of Agriculture’s Agricultural Research Service (ARS). Brian helps lead ARS’s research computing initiative, SCINet, which provides high-performance computing infrastructure, research support services and programs, and training in scientific computing for ARS’s large research community. He is particularly interested in open-source AI and machine learning models, tools, and infrastructure for science. Brian’s professional background includes broad expertise in scientific research and technical and high-performance computing. He holds a bachelor’s degree in computer science from Bethel College in North Newton, KS, and a Ph.D. in ecology and evolutionary biology from the University of Colorado at Boulder.

Ben Teplitzky is an an AI and machine learning Research Fellow at Boston Scientific. He develops algorithms for analysis of biological signals in order to improve healthcare outcomes. He holds a PhD from the University of Minnesota in biomedical engineering.

Dr. Terao's research sits at the crossroads of artificial intelligence and experimental particle physics. Current interests include differentiable physics modeling, Foundation Models (FMs), and their application toward the development of an autonomous AI laboratory, or AI scientist.

Terao applies differentiable physics modeling to solve inverse problems—such as reconstructing inputs, inferring model parameters, and estimating and propagating model uncertainties. In the area of Foundation Models, Terao leads efforts in autonomous, self-supervised learning of physics directly from sensory data, including waveforms, 2D images, and 3D scenes. This work produced the first sensor-level FM in High Energy Physics (HEP), trained on 3D point cloud datasets. Sensor-level FMs automate the extraction of physics knowledge directly from raw data, and by aligning these learned representations with symbolic FMs, such as Large Language Models, the representations can be expressed in language and mathematics.

Together, these directions point toward an autonomous lab, or AI scientist. Here, differentiable physics modeling serves as an anchor of fundamental physics knowledge, grounding the otherwise black-box Foundation Models that can learn anything from data, and ensuring that scientific discovery remains rooted in physical principles.

Hamid R. Tizhoosh is a Professor of Biomedical Informatics in the Department of Artificial Intelligence and Informatics at Mayo Clinic, Rochester, MN, USA. He received his B.Sc./M.Sc. in engineering and computer science from RWTH Aachen University, Germany, and his Ph.D. in medical image analysis from Otto von Guericke University Magdeburg in 2000. He is the founder and director of Kimia Lab, focusing on search and retrieval of medical data, particularly in computational pathology. Prior to joining Mayo Clinic, he held research positions at the Universities of Toronto and Waterloo and has extensive experience in commercialization and startup development. Since 1996, his work has spanned artificial intelligence, computer vision, and medical imaging, including contributions to image filtering, segmentation, and large-scale search. More recently, his research addresses the limitations of foundation models in medical imaging, emphasizing case-level learning, multi-scale representations, and multimodal, retrieval-based approaches for explainable and clinically grounded AI. Dr. Tizhoosh has authored two books and over a hundred peer-reviewed publications.

Nhan Tran is director for the AI Program at Fermilab. He is also interim director of the Intelligent Microelectronics Systems Division in the Fermilab Technology Directorate. His research is focused on accelerator-based experiments to search for new phenomena, such as CMS at the LHC, LDMX, and DarkQuest, and the interface between AI, electronics, and instruments to advance science. He is also a founder and coordinator for the Fast ML Research Foundation.

Dr. Vatsavai is a Chancellor's Faculty Excellence Professor of Computer Science at North Carolina State University. His research bridges spatiotemporal data mining, high-performance computing, and geospatial AI (GeoAI), focusing on scalable algorithms and AI frameworks for massive spatiotemporal data—with applications in national security, resource monitoring, climate change, disaster response, and human terrain mapping. Previously a lead data scientist, he helped establish data science research within the computational sciences division at ORNL. He has published over 100 articles, serves as PC co-chair for IEEE Big Data 2026, and holds MS and PhD degrees in computer science from the University of Minnesota.

Chris Yang is a professor in the School of Computing and Informatics at Drexel University and an IEEE Fellow for contributions to AI in healthcare informatics. He served as a Program Director in the Directorate for Computer and Information Science and Engineering (CISE) at the National Science Foundation (NSF) from 2022 to 2026, where he led several cross-cutting programs in AI, data science, and interdisciplinary research. He is the Editor-in-Chief of the Journal of Healthcare Informatics Research and the founding Steering Committee Chair of the IEEE International Conference on Healthcare Informatics. His research focuses on AI for science and healthcare, including multi-agent systems, large language models, explainable AI, AI fairness, multimodal learning, predictive modeling, pharmacovigilance, drug repositioning, social media-based health interventions, and social network analytics. His research aims to develop trustworthy and impactful AI methods that address complex challenges in science, medicine, and society.

Alina Zare teaches and conducts research in machine learning and artificial intelligence as the Malachowsky Family Endowed Professor in the Electrical and Computer Engineering Department. She also serves as the Director for the campus-wide Artificial Intelligence and Informatics Research Institute at the University of Florida. Dr. Zare’s research has focused on new AI algorithm development for automatically understanding and processing data and imagery.  Her research work has included application to automated plant root phenotyping, sub-pixel hyperspectral image analysis, large-scale remote sensing, target detection and underwater scene understanding using synthetic aperture sonar, LIDAR data analysis, Ground Penetrating Radar analysis, and buried landmine and explosive hazard detection.

Dimitris Zermas, Ph.D., is a Machine Learning Manager at John Deere, where he leads the development of advanced computer vision and deep learning solutions for precision agriculture. Previously, he served as Director of Machine Learning at Sentera, where he established the company's ML capabilities and helped drive innovation in imagery-based analytics. Dr. Zermas received his Ph.D. in Computer Science and Engineering from the University of Minnesota, with research focused on integrating machine learning and computer vision for agricultural applications. His work spans areas such as 3D point cloud segmentation, UAV-based crop monitoring, and scalable infrastructure for model training and deployment. He is passionate about bridging academic research with real-world impact in agriculture through large-scale, intelligent systems.

Dr. Zhang is Professor and Founding Chief of Division of Computational Health Sciences at the University of Minnesota. He was named as McKnight Presidential Fellow and hold several leadership roles, including Chair of AI and Data science for Healthcare (AID-H) within the UMN’s AI Hub, Associate Director for Health Data Science & AI within Center for Learning Health System Sciences, the Director of Natural Language Processing/Information Extraction (NLP/IE) research program, and previously served as NLP Director at UMN’s Clinical and Translational Science Institute. Dr. Zhang’s research is at the forefront of advancing and integrating novel AI and LLM with clinical research and practice, analyzing multi-modal biomedical data, including electronic health records, biomedical literature, and patient-generated data. His research is fully supported by multiple federal grants as a Principal Investigator, focusing on transformative AI projects including mining safety use of dietary supplements, discovering drug repurposing of Alzheimer’s disease, predicting breast cancer treatment related cardiotoxicity, identifying medical language bias in kidney transplantation, and develop knowledge graph on complementary and integrative health and develop federated learning for rare disease recognition for ciliopathies. He has published over 170 peer-reviewed articles, including Natural Medicine, Natural Communications, NPJ Digital Medicine, ACL, EMNLP, ICML, IJCAI. His work has been reported by The Wall Street Journal, and interviewed by CBS News. Dr. Zhang is elected Fellow of International Academy of Health Sciences Informatics (FIAHSI), American College of Medical Informatics (FACMI), AMIA and American Institution for Medial and Biological Engineering (FAIMBE). He is the current Chair of AMIA Natural Language Processing (NLP) Working Group, and Associate Editor of NPJ Digital Medicine.