Yu Cao, Ph.D.
is a professor of Electrical Engineering at Arizona State University, Tempe, Arizona. 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.
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.
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.
Mahantesh Halappanavar, Ph.D.
is a Computer Scientist and acting Group Leader at Pacific Northwest National Laboratory. 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 senior member of ACM and IEEE.
Peng Li, Ph.D.
is a Professor of Electrical and Computer Engineering at University of California, 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.
Frank Liu, Ph.D.
is a Distinguished Research Scientist and Group Leader at Oak Ridge Research Laboratory. His research activities span from machine learning, HPC, big data analysis, environmental modeling to VLSI design and VLSI CAD. He is an Adjunct Professor at Texas A&M University and an IEEE Fellow.
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 Post-doctorate RA 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.
Malachi Schram, Ph.D.
is a Scientist and Team Leader at Pacific Northwest National Laboratory. His research is focused on machine learning and artificial intelligent for a diverse number of scientific applications, such as nuclear physics, high energy physics, and material science.
Jingbo Sun, Ph.D.
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.
Ramakrishna Tipireddy, Ph.D.
is a Mathematician at Pacific Northwest National Laboratory. His research expertise includes machine learning, uncertainty quantification, stochastic partial differential equations, computational mechanics, reduced order models for complex stochastic systems and quantum computing.
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.
Jiaxin Zhang, Ph.D.
is a research staff at Oak Ridge National Laboratory. His research broadly revolves around AI/machine learning, uncertainty quantification, computational design, optimization and inverse problem, with applications to advanced materials, additive manufacturing and robotic systems.