Frank Liu, Ph.D.
is the Principal Investigator (PI) of DnC2S. He is a Distinguished Scientist and Group Leader at ORNL. His research activities span from machine learning, HPC, big data analysis, and environmental modeling to VLSI design and VLSI CAD. He is an Adjunct Professor at Texas A&M University and an IEEE Fellow.
Mahantesh Halappanavar, Ph.D.
is the PNNL co-PI of DnC2S. He is a chief computer scientist at PNNL. His research has spanned multiple technical foci and includes combinatorial scientific computing, design, and implementation of parallel graph algorithms, machine learning, and diverse scientific computing applications. He is an Adjunct Professor at Washington State University and a senior member of ACM and IEEE.
Yu Cao, Ph.D.
is the ASU co-PI of DnC2S. He is a full professor in Electrical and Computer Engineering at Arizona State University. His research interests include neural-inspired computing, hardware design for on-chip learning, and reliable integration of nanoelectronics. He is a Fellow of the IEEE.
Peng Li, Ph.D.
is the UCSB co-PI of DnC2S. He is a full professor of Electrical and Computer Engineering at the University of California at Santa Barbara. His research interests are in brain-inspired computing, robust machine learning, electronic design automation, and hardware machine learning systems. He is an IEEE Fellow.
Malachi Schram, Ph.D.
is the Jefferson Lab Co-PI of DnC2S. He is Date Science Department Head at Jefferson Lab. His research is focused on machine learning and artificial intelligence for a diverse number of scientific applications, such as nuclear physics, high energy physics, and material science.
Vikas Chandan, Ph.D.
is a Scientist and Team Leader at Pacific Northwest National Laboratory. He conducts research on modeling, control and optimization of energy systems, in particular data driven techniques for building energy management. He is a senior member of IEEE.
Sam Chatterjee, Ph.D.
is a Data/Operations Research Scientist and Team Leader at Pacific Northwest National Laboratory. His research focuses on complex infrastructure networks using machine learning, probabilistic risk analysis, decision making under uncertainty, multi-objective optimization, game theory, and network science. He is an Affiliate Professor at Northeastern University–Boston and senior member of IEEE.
Jan Drgona, Ph.D.
is a Data Scientist at Pacific Northwest National Laboratory. His research interests fall in the intersection of model-based optimal control, constrained optimization, and machine learning.
Dan Lu, Ph.D.
is a research staff at Oak Ridge National Laboratory. Her research focuses on machine learning, uncertainty quantification, inverse modeling, sensitivity analysis, and design of experiments.
Sayak Mukherjee, Ph.D.
is a staff scientist at Pacific Northwest National Laboratory. His research focuses on modeling, and learning-based control of networked dynamical systems with applications to large-scale grid stability and control.
Gopikrishnan Raveendran Nair
is a Computer Engineering PhD student at Arizona State University. His research interests span Artificial Intelligence and Hardware accelerators and is working on FPGA based accelerators for Graph Neural Networks.
Pradeep Ramuhalli, Ph.D.
is a Distinguished Research Scientist at Oak Ridge National Laboratory. His research focus is on instrumentation, control, and decision-making technologies for complex system monitoring, health prognostics, and autonomous operation.
Karthik Somayaji NS
is a first year PhD student at the University of California, Santa Barbara. His interests mainly lie in using reinforcement learning (RL) to understand complex systems. He works on single and multi-objective optimization using reinforcement learning and also on uncertainty quantification aware RL methodologies.
is a Computer Engineering Ph.D. student at Arizona State University. His research interests focus on multiple branches related to machine learning, including continual learning, novelty detection, and graph-based perception.
is an Electrical and Computer Engineering PhD candidate at University of California, Santa Barbara. His works focus on hardware acceleration of specific machine learning algorithms, typically algorithms with probabilistic learning models on FPGA platform.
David Womble, Ph.D.
is the Director of Artificial Intelligence Programs at Oak Ridge National Laboratory. His research interests include numerical algorithms and methods for machine learning and high-performance computing, including the solution of linear and nonlinear systems, multigrid and multiscale algorithms, time-series analysis, and scalable algorithms in HPC.
is a Computer Engineering Ph.D. student at Arizona State University. His research interests focus on graph-based neural network, dynamical modeling, and interpretable machine learning.
Bryan Maldonado, Ph.D.
is an associate research staff member in Oak Ridge National Laboratory’s Buildings and Transportation Science Division. His research interests include model-based optimal control, statistical machine learning, nonlinear dynamical systems, and integrated energy systems.
Agniva Chowdhury, Ph.D.
is a postdoctoral research associate at the Oak Ridge National Laboratory. Currently, he works in the intersection of machine learning , applied mathematics and randomized algorithms, with applications to physics-informed neural networks for solving supervised-learning tasks that are governed by the laws of physics.
Katarzyna Borowiec, Ph.D.
is a R&D Associate Staff at Oak Ridge National Laboratory. Her research focuses on the AI&ML applications for challenging problems including fusion and nuclear engineering with the focus of design optimization, digital twin modeling, bi-fidelity modeling, and autonomous control with reinforcement learning.
Suhas Sreehari, Ph.D.
is a staff scientist in the National Security Sciences Directorate at Oak Ridge National Laboratory. His interests are in the theoretical aspects of machine learning, computational imaging, anomaly detection, and statistical signal processing. He holds a PhD in electrical engineering from Purdue University. He is a Senior Member of IEEE, Chairman of the IEEE East Tennessee Section, and the recipient of the 2020 SIAM Imaging Sciences Prize and the 2018 IEEE Signal processing Young Author Award.
Wenceslao Shaw Cortez, Ph.D.
is a Data Scientist at PNNL. His research interests include nonlinear systems, constraint-satisfying control, decentralized/multi-agent systems, adaptive/data-driven/learning control, and optimal control for real-world applications.
is a PhD student at University of California, Santa Barbara. His research interest is about system modeling by data-driven methods and reinforcement learning.