Barry Y. Chen

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

Seung-Hwan Lim

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

Derek Rose

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

Xipeng Shen

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

Steven Young

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

Brian Van Essen

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

Brian Kingsbury

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

IBM Research

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

Reid Porter

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

Asit Mishra

NVIDIA

asitm "at" nvidia.com

Adwait Jog

College of William and Mary

ajog "at" wm.edu

Hui Guan

University of Massachusetts Amherst

huiguan "at" cs.umass.edu