Inderjit S Dhillon
Gottesman Family Centennial Professor
University of Texas at Austin
Department of Computer Science
Austin, TX, USA
He is an ACM Fellow, IEEE Fellow, SIAM Fellow, AAAS Fellow and has won the SIAM Linear Algebra Prize. His research interests are in machine learning, large-scale data analysis and bioinformatics. His emphasis is on developing novel algorithms that respect the underlying problem structure and are scalable to massive data sets. Some of his current research topics: high-dimensional data analysis, divide-and-conquer methods for big data analytics, social network analysis, and predicting gene-disease associations.
David A. Bader
Chair, School of Computational Science and Engineering
Executive Director of High Performance Computing
College of Computing
Georgia Institute of Technology, Atlanta
He received his Ph.D. in 1996 from The University of Maryland, and his research is supported through highly-competitive research awards, primarily from NSF, NIH, DARPA, and DOE. Dr. Bader serves as a board member of the Computing Research Association (CRA), on the NSF Advisory Committee on Cyberinfrastructure, on the Council on Competitiveness High Performance Computing Advisory Committee, on the IEEE Computer Society Board of Governors, and on the Steering Committees of the IPDPS and HiPC conferences. He is the editor-in-chief of IEEE Transactions on Parallel and Distributed Systems (TPDS) and Program Chair for IPDPS 2014. Bader also serves as an associate editor for several high impact publications including IEEE Transactions on Computers (TC), ACM Transactions on Parallel Computing (TOPC), and ACM Journal of Experimental Algorithmics (JEA).
Dr. Bader‘s interests are at the intersection of high-performance computing and real-world applications, including computational biology and genomics and massive-scale data analytics. He has co-chaired a series of meetings, the IEEE International Workshop on High-Performance Computational Biology (HiCOMB), co-organized the NSF Workshop on Petascale Computing in the Biological Sciences, written several book chapters, and co-edited special issues of the Journal of Parallel and Distributed Computing (JPDC) and IEEE TPDS on high-performance computational biology. He is also a leading expert on multicore, manycore, and multithreaded computing for data-intensive applications such as those in massive-scale graph analytics. His main areas of research are in parallel algorithms, combinatorial optimization, massive-scale social networks, and computational biology and genomics.
Prof. Bader is a Fellow of the IEEE and AAAS, a National Science Foundation CAREER Award recipient, and has received numerous industrial awards from IBM, NVIDIA, Intel, Cray, Oracle/Sun Microsystems, and Microsoft Research. Dr. Bader has served as a lead scientist in several DARPA programs including High Productivity Computing Systems (HPCS) with IBM PERCS, Ubiquitous High Performance Computing (UHPC) with NVIDIA ECHELON, Anomaly Detection at Multiple Scales (ADAMS) and Power Efficiency Revolution For Embedded Computing Technologies (PERFECT). He has also served as Director of the Sony-Toshiba-IBM Center of Competence for the Cell Broadband Engine Processor. Bader is a co-founder of the Graph500 List for benchmarking “Big Data” computing platforms. Bader is recognized as a “RockStar” of High Performance Computing by InsideHPC and as HPCwire‘s People to Watch in 2012 and 2014.
Title of Talk
Massive-scale streaming analytics
Senior Scientist ST for Advanced Computing Concepts
Current : U.S. Naval Research Laboratory
Previous : Air Force Research Laboratory, IEEE Mohawk Valley Section, Air Force Institute of Technology
Education : University of Miami
Guna Seetharaman is the Navy Senior Scientist (ST) for Advanced Computing
Concepts, and the Chief Scientist for Computation, Center for Computational Science,
Navy Research Lab. He leads a team effort on: Video Analytics, High performance
computing, low–latency, high-throughput, on-demand scalable geo-dispersed
computer-networks. He joined NRL in June 2015. He worked as Principal Engineer
at the Air Force Research Laboratory, Information Directorate, where he led
research and development in Video Exploitation, Wide Area Motion Imagery,
Computing Architectures and Cyber Security. He holds three US Patents, and has
filed more disclosures, in related areas. His team won the best algorithm award at
IEEE CVPR-2014 Video Change Detection challenge, featuring a semantic
segmentation of dynamic scene to detect change in the midst of dynamic clutters.
He served as a tenured professor at the Air Force Institute of Technology, and
University of Louisiana at Lafayette, before joining AFRL. He and his colleagues
cofounded Team CajubBot and successfully fielded two unmanned vehicles at the
DARPA Grand Challenges 2004, 2005 and 2007. He also co-edited a special issue of
IEEE COMPUTER dedicated to Unmanned Vehicles, and special issue of The
European Journal Embedded Systems focused on intelligent autonomous vehicles.
He was elected as Fellow of the IEEE, in 2014, for his contributions in high
performance computer vision algorithms for airborne applications. He also served
as the elected Chair of the IEEE Mohawk Valley Section, Region 1, FY 2013 and FY
Title of Talk
Computing Architectures for Machine Perception in the Post Deep-Learning World
We are in the middle of a surge in popularity, interest and investments in artificial intelligence and neural networks. Successful demonstration of ImageNet classification with deep convolutional neural networks (DCNN), has established the viability of robust image recognition system based on very large collection of labeled images, and powerful GPUS to train the DCNN, and execute the trained classifier on computing devices with modest computing capabilities. Typically the training takes place on a centralized computer, and classification takes place on computing device connected directly to the sensor, referred as computing at the edge. Elements of the analytical framework of image recognition include convolution, shift invariance, correlation and scaling by down-sampling are easier are essential to the pooling process critical for building deep neural networks. Similar abstractions for analyzing large collection of texts, and cyber forensic data, are yet to be well established. The computing capabilities required to build ubiquitous self-aware multimodal-sensor network will entail architectural innovations beyond GPGPUs. We will review the lessons learned in the realm of video analytics and automated text analytics over the past decades and also share insights towards promising new directions. Exciting new possibilities await us, enabled by the convergence of open-source software development tools, data- sharing tools, and innovatively packaged processors with high-bandwidth memory subsystems and highly scalable networked computing.
P. (Saday) Sadayappan
Department of Computer Science and Engineering
595 Dreese Lab, 2015 Neil Avenue
Ohio State University, Columbus, Ohio 43210 USA
+1-614-292-0053 (office), +1-614-292-2911 (fax)
- CSE 5441 (Introducton to Parallel Computing)
- CSE 6441 (Parallel Computing)
- Compiler Optimization for High Performance Computing
- Domain-Specific Compile/Runtime Optimization
- Data Movement Complexity of Computations
- Tensor Contraction Engine (TCE)
- Polyhedral Compiler Optimization
- EAGER: Towards Automated Characterization of the Data-Movement Complexity of Large Scale Analytics Applications, NSF, 2016-2019.
- XPS: FULL: Collaborative Research: PARAGRAPH: Parallel, Scalable Graph Analytics, NSF, 2016-2019.
- Whole-Program Adaptive Error Detection and Mitigation, DOE, 2015-2018 (Project PI: Sriram Krishnamoorthy, PNNL).
- Improving Vectorization, NSF, 2014-2017.
- Compiler/Runtime Support for Developing Scalable Parallel Multi-Scale Multi-Physics Applications, NSF, 2014-2017.
- Characterization of bandwidth requirements of algorithms for extreme scale science, DOE, 2014-2016.
- Domain Specific Language Support for Exascale, DOE, 2013-2016 (Project PI: Daniel Quinlan, LLNL).
- Large-Scale Computation of the Phonon Boltzmann Transport Equation, NSF, 2012-2016 (PI: Sandip Mazumder).
- A Polyhedral Transformation Framework for Compiler Optimizations, DOE, 2010-2014.
- An environment for high-productivity high-performance computing using GPUs/Accelerators, NSF, 2009-2013.
- Global graphs: A middleware for data intensive computing, NSF, 2009-2013 (PI: Srinivasan Parthasarathy).
- Petascale simulations of quantum systems by stochastic methods, NSF, 2009-2013 (Project PI: David Ceperley, Univ. Illinois).
- Customizable domain-specific computing, NSF, 2009-2014 (Project PI: Jason Cong, UCLA).
Computer Science, and Institute of Computational Engineering and Sciences,
Center for Computational Visualization,
The University of Texas - Austin
Brief Speaker Bio:
Chandrajit Bajaj is the director of the Center for Computational Visualization, in the Institute for Computational and Engineering Sciences (ICES) and a Professor of Computer Sciences at the University of Texas at Austin. Bajaj holds the Computational Applied Mathematics Chair in Visualization. He is also an affiliate faculty member of Mathematics, Electrical Engineering, Bio-Medical Engineering, Neurobiology, and a fellow of the Institute of Cell and Molecular Biology. He is currently on the editorial boards for the International Journal of Computational Geometry and Applications, and the ACM Computing Surveys, and past editorial member of the SIAM Journal on Imaging Sciences. He is a Distinguished Alumunus of the Indian Institute of Technology, Delhi, (IIT, Delhi). He is also a Fellow of The American Association for the Advancement of Science (AAAS), Fellow of the Association for Computing Machinery (ACM), Fellow of the Institute of Electrical and Electronic Engineers (IEEE), and Fellow of the Society of Industrial and Applied Mathematics (SIAM).
Title of Talk
The Promise of Machine Learning for Infrared Spectroscopic Image Analysis
A key theme in near and mid-infrared imaging data sciences is the image acquisition coupled to robust computational analysis of spatial and spectral information, to simultaneously predict shape and functional properties of complex scenes. Current capabilities allow for the rapid recording of full hyper-spectral image sets, typically 100 GB, having 10 megapixels, with each pixel containing 2000 spectral wavelengths, with an absorbance value between [0, 1], with noise levels from [10−3, 0.1]. The barriers limiting progress today then is accurate and rapid computational image analysis for the robust elucidation of spatial structure and their material properties.. This talk shall dwell on the success, and current challenges of state of the art machine learning algorithms (spectral de-noising, multi-label classification, functional un-mixing, ..). We quantify the improvement, or lack thereof, using error estimates for image classification and spectral signature recovery.
Prof. Giri Narasimhan
Bioinformatics Research Group (BioRG),
Florida International University
Brief Speaker Bio:
Giri Narasimhan a professor in the School of Computing and Information Sciences at Florida International University (FIU), where he heads the Bioinformatics Research Group (BioRG). He holds a Bachelor’s degree in Electrical Engineering from the Indian Institute of Technology-Bombay and a PhD in Computer Science from the University of Wisconsin-Madison. His interdisciplinary research involves problems from the fields of Algorithms, Bioinformatics, Biotechnology, Data Mining, Machine Learning, Theoretical Computer Science, Graph Theory, Optimization, High Performance Computing, Computational Finance and Statistics. His research has been funded by the National Science Foundation, the National Institutes of Health, National Institute of Justice, Florida Department of Health, Department of Defense and private industry. He has published one monograph, two edited volumes, and over 140 refereed articles in books, journals, and conference proceedings. He is the coach of the FIU Programming Team and is a Co-Director of the Ultimate Software Academy for Teaching and outreach at FIU. As the Associate Dean for Research and Graduate Studies in the College of Engineering and Computing during 2009-15 he managed the research portfolio of the college and oversaw the graduate programs in the college.
Research Group: http://www.cs.fiu.edu/~giri