University of Maryland
Joseph JaJa is Professor of Electrical and Computer Engineering, and the Institute for Advanced Computer Studies at the University of Maryland, College Park. . Dr. JaJa received his Ph.D. degree in Applied Mathematics from Harvard University and has since published extensively in a number of areas including parallel and distributed computing, theoretical computer science, circuits and systems, and data-intensive computing. His current research interests are in high performance computing, statistical machine learning, and scientific visualization. Dr. JaJa has served in a number of leadership positions at Maryland including Interim CIO and VP, Director of the Institute for Advanced Computer Studies, and Director of Cyberinfrastructure of the National Socio-environmental Synthesis Center. Dr. JaJa has received numerous awards including the IEEE Fellow Award in 1996, the 1997 R&D Award for the development software for tuning parallel programs, the ACM Fellow Award in 2000, and the Internet2 IDEA Award in 2006.
joseph "at" umiacs.umd.edu
Lawrence Livermore National Laboratory
Dr. Barry Y. Chen is the Knowledge Systems and Informatics Group Leader at the Lawrence Livermore National Laboratory where he serves as principal investigator on several projects developing and applying scalable machine learning algorithms for clustering, classification, anomaly, and change detection
chen52 "at" llnl.gov
Oak Ridge National Laboratory
Dr. Seung-Hwan Lim has been a Research Staff Member at the Oak Ridge National Laboratory (ORNL). His career spans research and development conducted in industry and national laboratory. He earned a PhD in Computer Science and Engineering from the Pennsylvania State University. His current research focuses on data intensive system architectures for machine learning, graph processing, and statistical inference algorithms. He earned a BS in Computer Engineering from Seoul National University. Prior to graduate studies, he worked for Samsung’s smartphone business as a software engineer, in charge of device drivers of communication network layers.
lims1 "at" ornl.gov
Oak Ridge National Laboratory
Dr. Derek Rose is a Research Staff Member working at Oak Ridge National Laboratory in the Imaging, Signals, and Machine Learning Group. His current active research areas include segmentation and classification of subcellular structures in fluorescence microscopy and vehicle detection and tracking using correlation filters and deep learning. Further research interests include neural networks, visual attention mechanisms, natural image statistics, bag-of-visual words models, sparse coding, and neuromorphic computing and biologically inspired architectures.
rosedc "at" ornl.gov
Chih-Chieh Yang is a postdoctoral research scientist in the Data Centric Systems group at IBM Research. He received both his B.S. and M.S. from National Tsing Hua University, and accumulated industry experiences working for the top Taiwanese IC design house, Mediatek, for several years before starting and eventually earning his Ph.D from University of Illinois at Urbana-Champaign. His past research experiences include designing software components that facilitate the development of distributed applications and high-level parallel programming abstractions. His current focus is on scaling distributed machine learning applications to both current state-of-the-art supercomputers and future extreme scale HPC systems.
chih.chieh.yang "at" ibm.com
North Carolina State University
Prior to joining NC State, Shen was the Adina Allen Term Distinguished Associate Professor in the Computer Science Department at The College of William and Mary. He was a Visiting Researcher at M.I.T., Microsoft Research, and Intel Labs between 2012 and 2013, and an assistant professor at The College of William and Mary from 2006 to 2012. His research in data locality for exascale computing won the prestigious Early Career Research Award from the U.S. Department of Energy in 2011. His research in input-centric program dynamic optimizations won the CAREER Award from the US National Science Foundation in 2010. He is a receipt of Google Faculty Research Award. For the high impact of his research, he has been appointed an IBM Canada CAS Research Faculty Fellow since 2010. He is currently an ACM Distinguished Speaker, and a senior member of IEEE. Dr. Shen received his Ph.D. and M.S. in Computer Science from University of Rochester, his M.S. in Pattern Recognition and Intelligent Systems from Chinese Academy of Sciences, and his B.E. in Industrial Engineering from North China University of Technology.
xshen5 "at" ncsu.edu
Oak Ridge National Laboratory
Dr. Steven Young is a research scientist at Oak Ridge National Laboratory working in the Computational Data Analytics Group. He earned a Ph.D. in Computer Engineering from The University of Tennessee where he studied machine learning in the Machine Intelligence Lab. He also holds a B.S. in Electrical Engineering from The University of Tennessee. His current research involves applying machine learning to large scale datasets with a focus on deep learning methods.
youngsr "at" ornl.gov
Lawrence Livermore National Laboratory
Dr. Brian Van Essen has been a Computer Scientist at Lawrence Livermore National Laboratory (LLNL) since 2010. His research interests include operating systems and architectures for data-intensive HPC, deep learning, and embedded systems. Brian earned his Ph.D. in Computer Science and Engineering (CSE) from the University of Washington in Seattle in 2010. He also holds a M.S. in CSE from UW, plus a M.S. and a B.S. in Electrical and Computer Engineering (ECE) from Carnegie Mellon University. Prior to his graduate studies, Brian co-founded two startups in the area of reconfigurable computing.
vanessen1 "at" llnl.gov
Massachusetts Institute of Technology
Una-May O'Reilly is founder and co-leader of the AnyScale Learning For All (ALFA) group at Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory. ALFA focuses on scalable machine learning, evolutionary algorithms, and frameworks for large scale knowledge mining, prediction and analytics. She received the EvoStar Award for Outstanding Achievements in Evolutionary Computation in Europe in 2013. She is a Junior Fellow (elected before age 40) of the International Society of Genetic and Evolutionary Computation, now ACM Sig-EVO. She now serves as Vice-Chair of ACM SigEVO. She is the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), and editor for Evolutionary Computation (MIT Press), and action editor for the Journal of Machine Learning Research.
unamay "at" csail.mit.edu
University of New Mexico
Trilce Estrada is an assistant professor in the department of Computer Science at the University of New Mexico. Her research interests include self-managed distributed systems, Big Data analysis, crowd sourcing, and machine learning. Recently she got awarded the National Science Foundation's Early Career Award for the proposal entitled CAREER: Enabling Distributed and In-Situ Analysis for Multidimensional Structured Data The goal of her research program is to solve computationally intensive and data intensive problems in science, health, and education, especially in scenarios where resources and trained professionals are scarce. I believe that a computer is only as good as the difference it can make in the world, and I strive to achieve this level of impact with her work.
trilce "at" unm.edu
IBM Watson Group
Dr. Brian Kingsbury is a principal research staff member in the IBM AI Foundations lab. He earned a BS in electrical engineering from Michigan State University and a PhD in computer science from the University of California, Berkeley. His research interests include deep learning, large-vocabulary speech transcription, and keyword search. Brian has contributed to IBM's entries in numerous competitive evaluations of speech technology, including Switchboard, SPINE, EARS, Spoken Term Detection, and GALE. He was co-PI and technical lead for LORELEI, an IBM-led consortium that participated in the IARPA Babel program. He has served on the Speech and Language Technical Committee of the IEEE Signal Processing Society; as an ICASSP speech area chair; as an associate editor for IEEE Transactions on Audio, Speech, and Language Processing; and as a program chair for the International Conference on Representation Learning.
Guojing Cong is a Research Staff Member at the IBM TJ Watson research center in Yorktown Heights, New York. His current research interests include large-scale machine learning on HPC systems and graph analysis in the social media and security settings. In the past he has worked on parallel graph algorithms, large-scale combinatorial optimizations, finance risk analytics, and performance analysis and tuning for HPC systems. He received his PhD in Computer Engineering from the University of New Mexico. He is a senior member of IEEE.
gcong "at" us.ibm.com
Los Alamos National Laboratory
Reid Porter is a research scientist in the Computing, Computational and Statistical Sciences Division at Los Alamos National Laboratory. Reid obtained a doctorate in electrical engineering from the Queensland University of Technology, Australia, in 2002. Reid's thesis work focused on using reconfigurable computing to accelerate learning and inference in convolutional neural networks for satellite image analysis, and highlights his early interest at the intersection of machine learning, signal processing, and computer architecture. Reid's interest in these topics has continued to grow while working on a variety projects at Los Alamos. He has more recently contributed theory, algorithms and software for interactive machine learning systems to better support domain experts in specialized science and defense applications. From 2015 to 2018 Reid was a technical leader in Computer Vision at Kitware Inc. where he focused on moving object detection, tracking and activity detection in video and wide area motion imagery.
rporter "at" lanl.gov