Knowledge-guided Machine Learning

Dr. Michael Parks is the Director of the Computer Science and Mathematics Division in the Computing and Computational Sciences Directorate at Oak Ridge National Laboratory. Dr. Parks' research interests include: Numerical analysis, Scientific machine learning, Nonlocal models and mathematics, Multiscale mathematics; Atomistic-to-continuum coupling, Numerical linear algebra; Linear solvers. Dr. Parks is an associate editor for the SIAM Journal on Numerical Analysis and Journal of Peridynamics and Nonlocal Modeling.  

Dr. Parks has received numerous awards for his research including the Sandia Employee Recognition Award in 2010 for creating the PDLAMMPS code, in  2009 for technical work on Sandia project Peridynamics as a Rigorous Coarse-Graining of Atomistics for Multiscale Materials Design and the Sandia Award for Excellence in 2008 for technical and programmatic leadership in multiscale simulation.

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Prof. Vipin Kumar is a Regents Professor at the University of Minnesota, where he holds the William Norris Endowed Chair in the Department of Computer Science and Engineering. Kumar received the B.E. degree in Electronics & Communication Engineering from Indian Institute of Technology Roorkee (formerly, University of Roorkee), India, in 1977, the M.E. degree in Electronics Engineering from Philips International Institute, Eindhoven, Netherlands, in 1979, and the Ph.D. degree in Computer Science from University of Maryland, College Park, in 1982. He also served as the Head of the Computer Science and Engineering Department from 2005 to 2015 and the Director of Army High Performance Computing Research Center (AHPCRC) from 1998 to 2005.

Prof. Kumar's research spans data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. He has authored over 300 research articles, and has coedited or coauthored 10 books including two text books ``Introduction to Parallel Computing'' and ``Introduction to Data Mining'', that are used world-wide and have been translated into many languages. Kumar's current major research focus is on bringing the power of big data and machine learning to understand the impact of human induced changes on the Earth and its environment. 

Prof. Kumar has been elected a Fellow of the Association for Advancement of Artificial Intelligence (AAAI), the American Association for Advancement for Science (AAAS), Association for Computing Machinery (ACM), Institute of Electrical and Electronics Engineers (IEEE), and Society for Industrial and Applied Mathematics (SIAM). He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013), the Distinguished Alumnus Award from the Computer Science Department, University of Maryland College Park (2009), and IEEE Computer Society's Technical Achievement Award (2005). Kumar's foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards in high-performance computing, and Test-of-time award from 2021 Supercomputing conference (SC21).

Dr. Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech, where he develops data mining and machine learning methods to solve scientific and socially relevant problems. A key focus of Dr. Karpatne’s research is to advance the field of science-guided machine learning for applications in several domains including climate science, hydrology, ecology, geophysics, trait-based biology, mechanobiology, quantum mechanics, and fluid dynamics. He has received the Outstanding New Assistant Award by the College of Engineering at VT in 2022, the Rising Star Faculty Award by the Department of Computer Science at VT in 2021 and was named the Inaugural Research Fellow by the IS-GEO (Intelligent Systems for Geosciences) Research Coordination Network for 2019. Dr. Karpatne currently serves as the editor-in-chief of the quarterly newsletter SIGAI AI Matters. Dr. Karpatne is also a co-author of the second edition of the textbook, Introduction to Data Mining. He received his Ph.D. in Computer Science at the University of Minnesota in 2017 under the guidance of Prof. Vipin Kumar.

Dr. Nikhil Muralidhar is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology, where he directs the Scientific AI (ScAI) lab that conducts research to develop novel data mining, machine learning solutions to problems of scientific and societal relevance. A key focus area of Dr. Muralidhar's research is to develop knowledge-guided machine learning (KGML) techniques to address problems in fluid dynamics, disease modeling, cyber-physical systems, quantum mechanics and nuclear engineering. His research has been published in several top conferences and journals including IJCAI, IEEE ICDM, ACL, COLM, SIAM SDM, ACM TIST, Physics of Fluids. Dr. Muralidhar's research work has received several awards including first place in the COVID-19 Symptom Data Challenge organized by Catalyst Health & Facebook in 2020 and second place in the COVID-19 Grand Challenge Hosted by C3.ai in 2020.  Dr. Muralidhar has also received the award for academic excellence at George Mason University in 2015. Dr. Muralidhar is a reviewer for multiple top conferences including ICML, ICLR, NeurIPS, SIAM SDM, IEEE Big Data. He has also co-authored a book chapter for the book titled: Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data . Dr. Muralidhar obtained his Ph.D degree in computer science from Virginia Tech in 2022 under the guidance of Prof. Naren Ramakrishnan and Prof. Anuj Karpatne. He completed his M.S. degree in computer science from George Mason University in 2015 and his B.S. degree in Computer Science from Virginia Tech in 2012.

Dr. Anika Tabassum is a scientific AI research scientist at Oak Ridge National Laboratory, where she contributes to developing and applying deep Learning for multimodal data (spatiotemporal, graph, image) evolved from energy, e.g., battery, power, materials, and turbulence. Her research interest broadly lies in domain-guided ML to aid scientific simulations. 

Dr. Tabassum has been selected as RISING STAR 2023 by UT Austin and an outstanding postdoctoral award from her division at ORNL in 2022. She received her Ph.D. from the Department of Computer Science at Virginia Tech where she worked on bringing domain-guided ML to address multiple challenges to prepare and mitigate power system failures and disaster vulnerabilities. Her Ph.D. research work was funded by NSF Urban Computing fellowship. She won 1st prize in designing the COVID-19 forecasting model for the Facebook-CDC challenge. She has published in multiple venues as NeuRIPS, AAAI, ACM SigKDD, CIKM, IEEE BigData, IAAI, and journals like ACM TIST and Elsevier.

Prof. Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech, where he develops data mining and machine learning methods to solve scientific and socially relevant problems. A key focus of Dr. Karpatne’s research is to advance the field of science-guided machine learning for applications in several domains including climate science, hydrology, ecology, geophysics, trait-based biology, mechanobiology, quantum mechanics, and fluid dynamics. He has received the Outstanding New Assistant Award by the College of Engineering at VT in 2022, the Rising Star Faculty Award by the Department of Computer Science at VT in 2021 and was named the Inaugural Research Fellow by the IS-GEO (Intelligent Systems for Geosciences) Research Coordination Network for 2019. Dr. Karpatne currently serves as the editor-in-chief of the quarterly newsletter SIGAI AI Matters. Dr. Karpatne is also a co-author of the second edition of the textbook, Introduction to Data Mining. He received his Ph.D. in Computer Science at the University of Minnesota in 2017 under the guidance of Prof. Vipin Kumar.

Dr. Arka Daw joined ORNL in 2024 as a Distinguished Staff Fellow (DSF). He is a member of the Center for Artificial Intelligence Security Research (CAISER). He is also affiliated to the Emerging Cyber Systems Group in the Cyber Resilience and Intelligence Division of the National Security Sciences Directorate. His research primarily focusses on enhancing the generalizability, robustness, and reliability of deep learning models to ensure their safety and trustworthiness. He is also enthusiastic about tackling interdisciplinary challenges in AI for science.  Dr. Daw has been an active reviewer for top-tier machine learning conferences/journals such as NeurIPS, ICLR, ICML, AAAI, IJCAI, KDD, SDM, and IEEE-TNNLS. Dr. Daw received his PhD in Computer Science from Virginia Tech, specializing in developing uncertainty quantification techniques for physics-informed machine learning (PIML) models. He also holds a Bachelor’s degree in Electronics and Communications Engineering from Jadavpur University, India

Dr. Ramakrishnan (Ramki) Kannan is a distinguished scientist leading the Discrete Algorithms group at Oak Ridge National Laboratory (ORNL). His research expertise spans distributed machine learning and graph algorithms on High-Performance Computing (HPC) platforms, focusing on accelerating scientific discovery by significantly reducing computation times, often from weeks to seconds.

Dr. Kannan's notable achievements include leading the DSNAPSHOT project for COVID-19, a finalist for the ACM Gordon Bell Award in 2020 and 2022, Summit ranking 3rd on the Graph500 benchmark and achieved 1 ExaFLOPS on a KnowledgeGraph AI application on Frontier and  UT-Battelle Research Accomplishment Award in 2023.

With a track record of securing over $8M in research funding and leading projects exceeding $1 million for the Department of Defense, Dr. Kannan currently serves as the Deputy Director for the DOE Mathematical Multifaceted Integrated Capability Center (MMICC) [Sparsitute](https://sparsitute.lbl.gov/). He co-authored "Knowledge-guided Machine Learning" with Prof. Anuj Karpatne of Virginia Tech and Prof. Vipin Kumar of the University of Minnesota, a significant publication in 2022. Dr. Kannan holds over 24 patents issued by the USPTO and has been recognized as an IBM Master Inventor. He earned his Ph.D. under Professor Haesun Park at Georgia Institute of Technology and his M.Sc (Engg) under Professor Y. Narahari at the Indian Institute of Science.