Information Sciences Institute, University of Southern California
Dr. Wael AbdAlmageed is a senior computer scientist with the University of Southern California’s Viterbi School of Engineering Information Sciences Institute (USC/ISI). His research focus is applying large-scale machine learning techniques to computer vision and image processing problems. His research interests also include implementing machine learning and computer vision algorithms on modern high performance computing platforms. Prior to joining ISI, Dr. AbdAlmageed was a research scientist with the University of Maryland at College Park, where he lead several research efforts for various NSF, DARPA and IARPA programs. He obtained his Ph.D. with Distinction from the University of New Mexico in 2003 where he was also awarded the Outstanding Graduate Student award. He has two patents and over 50 publications in top computer vision and high performance computing conferences and journals.
wamageed "at" isi.edu
Deep Learning Competence Center, Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI)
Dr. Damian Borth is the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, the Principle Investigator of the NVIDIA AI Lab at the DFKI, and founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damians research focuses on large-scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. His work has been awarded by NVIDIA at GTC Europe 2016, the Best Paper Award at ACM ICMR 2012, the McKinsey Business Technology Award 2011, and a Google Research Award in 2010. Damian currently serves as a member of the steering group at the VolkswagenStiftung, the review committee at the Baden-Wurttemberg Stiftung, the assessment committee for the Investment Innovation Benchmark (IIB) and several other steering and program committees of international conferences and workshops. He is also a founding member the Financial Data Science Association and scientific director of the Certified Financial Data Science Program at Deutsche Vereinigung fur Finanzanalyse und Asset Management (DVFA).
damian.borth "at" dfki.de
Lawrence Livermore National Laboratory
Dr. Maya Gokhale has been a Computer Scientist at the Lawrence Livermore National Laboratory (LLNL) since 2007. Her career spans research conducted in academia, industry, and National Labs, most recently Los Alamos National Laboratory. Maya received a Ph.D. in Computer Science from University of Pennsylvania in 1983. Her current research interests include data intensive architectures and reconfigurable computing. She is co-author of more than one hundred technical publications. Maya is a member of Phi Beta Kappa, a Distinguished Member of Technical Staff at LLNL, and a Fellow of the IEEE.
gokhale2 "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.
rosedc1 "at" ornl.gov
Information Sciences Institute, University of Southern California
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
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
University of Texas, Austin
Bill March is a Postdoctoral Fellow at the Institute for Computational Engineering and Sciences at the University of Texas at Austin. Previously, he earned a BSc in Discrete Mathematics and a PhD in Computer Science from the Georgia Institute of Technology. His research focuses on developing novel, scalable algorithms for computational bottlenecks in machine learning tasks. Past work includes applications in astrostatistics and computational chemistry, along with scalable methods to train kernel-based learning models.
march "at" ices.utexas.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.
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
Google Research
Derek Murray is a senior software engineer in the Google Brain team, currently building the TensorFlow system for large-scale machine learning. His principal research interest is distributed systems for parallel computation, with a particular emphasis on expressive programming constructs like streaming and iteration. Previously he was a researcher at Microsoft Research Silicon Valley, where he worked on the Naiad system for incremental, iterative, and interactive processing. He received his PhD in Computer Science from the University of Cambridge, his MSc in High Performance Computing from the University of Edinburgh, and his BSc from the University of Glasgow.
mrry "at" google.com
Kitware
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 2017 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
