Keynote Speakers
Sanguthevar Rajasekaran
UTC Chair, Computer Science and Engineering
Director of Booth Engineering Center for Advanced Technologies (BECAT),
University of Connecticut, USA
Brief Speaker Bio :
Sanguthevar Rajasekaran received his M.E. degree in Automation from the Indian Institute of Science (Bangalore) in 1983, and his Ph.D. degree in Computer Science from Harvard University in 1988. Currently he is the Board of Trustees Distinguished Professor, UTC Chair Professor of Computer Science and Engineering, and the Director of Booth Engineering Center for Advanced Technologies (BECAT) at the University of Connecticut. Before joining UConn, he has served as a faculty member in the CISE Department of the University of Florida and in the CIS Department of the University of Pennsylvania. During 2000-2002 he was the Chief Scientist for Arcot Systems. His research interests include Big Data, Bioinformatics, Algorithms, Data Mining, Machine Learning, Randomized Computing, and HPC. He has published over 350 research articles in journals and conferences. He has co-authored two texts on algorithms and co-edited six books on algorithms and related topics. His research works have been supported by grants from such agencies as NSF, NIH, DARPA, and DHS (totaling around $20M). He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the American Association for the Advancement of Science (AAAS). He is also an elected member of the Connecticut Academy of Science and Engineering.

Title of Talk

Algorithms for Big Data Analytics

We live in the midst of big data. Generation of data is no longer a bottleneck. Analysis of them and extracting useful information are indeed huge bottlenecks. Efficient techniques are needed to process these data. Society at large can benefit immensely from advances in this arena. For example, information extracted from biological data can result in gene identification, diagnosis for diseases, drug design, etc. Market-data information can be used for custom-designed catalogues for customers, supermarket shelving, and so on. Weather prediction and protecting the environment from pollution are possible with the analysis of atmospheric data.

In this talk we present some challenges existing in processing big data. We also provide an overview of some basic techniques. In particular, we will summarize various data processing and reduction techniques. In addition, we will briefly outline the role of machine learning in big data analytics.

Chandrajit Bajaj
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, Computational Neuroscience and Electrical Engineering.
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 was awarded a distinguished alumnus award from 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

Unsupervised Super Resolution Hyperspectral Imaging

One achieves super resolution (SR) in imaging through computational enhancement of the input to yield output images which have improved resolvability of features. This is done by optimizing de-blurring and de-warping operators, and the appropriate leveraging of prior knowledge, and/or information from multiple similar images of the same scene or objects. Modern RGB digital cameras produce SR photographs of a scene by fusing a set of dynamically shifted acquired images of the scene. A related approach, using cross-modality imaging, can be used for producing SR hyperspectral (multi-spectral band) images. A low spatial resolution, hyperspectral image (LHI) and a high spatial resolution RGB image (Hrgb), can be optimally combined to produce a super resolution, hyperspectral image (SRHI). In this talk, I shall present new optimization methods based on low-rank or orthogonal tensor decompositions, and compare them with low-rank approximations of matricized representations of LHI and Hrgb. I shall also discuss variations stemming from different cross-modality imaging application (dynamic scene capture, facial and object recognition, cancer tissue histopathology) and where coupled optimization schemes for image registration and SR are necessary.
Invited Speakers
Dr. S S Iyengar
Ryder Professor of Computer Science
Director, The school of Computing and Information Sciences
Florida International University, Miami
Brief Speaker Bio :
Dr. S.S. Iyengar is currently the Ryder Professor of Computer Science and Director of the School of Computing and Information Sciences at Florida International University (FIU), Miami. He is also the founding director of the Discovery Lab. Prior to joining FIU, Dr. Iyengar was the Roy Paul Daniel’s Distinguished Professor and Chairman of the Computer Science department for over 20 years at Louisiana State University. He has also worked as a visiting scientist at Oak Ridge National Lab, Jet Propulsion Lab, Satish Dhawan Professor at IISc and Homi Bhabha Professor at IGCAR, Kalpakkam and University of Paris and visited Tsinghua University, Korea Advanced Institute of Science and Technology (KAIST) etc. His research interests include High-Performance Algorithms, Biomedical Computing, Sensor Fusion, and Intelligent Systems for the last four decades. His research has been funded by the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), Multi-University Research Initiative (MURI Program), Office of Naval Research (ONR), Department of Energy / Oak Ridge National Laboratory (DOE/ORNL), Naval Research Laboratory (NRL), National Aeronautics and Space Administration (NASA), US Army Research Office (URO), and various state agencies and companies.

Title of Talk

Data Center and Cloud Computing

Cloud Computing is a general term used to describe a new class of network based computing that takes place over the Internet. It is basically a step up from Utility Computing and can be defined as a collection/group of integrated and networked hardware, software and Internet infrastructure (called a platform). Cloud Computing uses the Internet for communication and transport and provides hardware, software and networking services to clients. These platforms hide the complexity and details of the underlying infrastructure from users and applications by providing very simple graphical interface or API (Applications Programming Interface).

Design of efficient Data Centers has also been a topic of significant research from both academia and industry in the recent years. Efficient data centers are of prime importance as most of the modern businesses rely heavily on cloud services. Data centers also play a very important part in supporting and sustaining the fast growing web-based services and applications. They also form the backbone of most of the search engines, content hosting and distribution companies, social network platforms, tasks involving intense computation etc. Most of the networks have had to adapt and reconfigure and thus use the services offered by cloud computing and data centers to respond to the changing application demands and service requirements. Many large organizations like Microsoft and Google have their own cloud based services and boast of data centers with millions of servers.

The use of cloud computing services and applications is increasing rapidly due to the various advantages it brings and thus has led to the rise of vast cloud data centers. Both consumer and business applications are contributing to the growing dominance of cloud services. The exponential growth of Internet of Things (IoT) devices and applications would also expand the need for data centers to manage the large amounts of data and information that is collected and shared. A study by Cisco reveals that more than 94% of workloads and computation instances would need cloud services by 2021. The various advantages that the cloud based services and the data centers offer makes it the most viable option to choose for businesses and companies who plan to either secure their information or scale to newer levels.

Sumeet Dua, Ph.D.
Associate Vice President for Research and Partnerships
Max P. and Robbie L. Watson Eminent Scholar Chair
Professor of Computer Science
Louisiana Tech University
Ruston, LA U.S.A.
Brief Speaker Bio :
Dr. Sumeet Dua is the Associate Vice President for Research and Partnerships, Professor of Computer Science and Cyber Engineering, and the Max. P. & Robbie L. Watson Eminent Scholar Chair at Louisiana Tech University in LA, U.S.A. Prior to his current administrative appointment, he was the Associate Dean for Graduate Studies and the Director for Computer Science, Electrical Engineering, Cyber Engineering and Electrical Engineering Technology programs in the College of Engineering and Science at Louisiana Tech University. His research interests include data mining, bioinformatics, clinical informatics and cybernetics. He has been awarded grants/contracts for over US$7 Million by various funding agencies, including NSF, NIH, AFOSR, AFRL and NASA. He has co-authored/edited 5 books and advised over 25 graduate thesis and dissertations in these areas. He has also served on over 50 National Institutes of Health (NIH) study sections and National Science Foundation (NSF) expert scientific review panels. He has received multiple awards, including the best paper presentation awards at leading international conferences, and most recently the 2016 Louisiana Tech University Foundation Professorship Award for excellence in teaching, research and service. He is a senior member of the IEEE and ACM, and a member of AAAS.

Title of Talk

Feature Engineering for Semi-supervised Machine Learning in Protein Informatics

Feature engineering, an integral data preprocessing step in machine learning, is aimed to boost the accuracy and efficiency of prediction systems. Those efforts principally rely on the creation of methods that imbibe facets of feature extraction, ranking, and selection methods cognizant of the underlying domain knowledge. This talk will emphasize the role of engineered evolutionary features using domain knowledge of hydrophobicity properties of proteins for enhanced prediction of their folding paradigm. Protein folding is frequently directed by local residue interactions that form clusters in the protein core. The interactions between residue clusters serve as potential nucleation sites in the folding process. Evidence postulates that the residue interactions are governed by the hydrophobic propensities that the residues possess. We will discuss a graph-theory-based machine learning framework to extract and isolate protein structural features that sustain invariance in evolutionary-related proteins feature ranking. The results obtained demonstrate that discriminatory residue interaction patterns obtained by these feature engineering methods are shared amongst proteins of the same family and can be effectively employed for both the structural and the functional annotation of proteins for multiple machine learning applications in bioinformatics.

Srinivas Aluru
Co-Executive Director, Institute for Data Engineering and Science
Professor, School of Computational Science and Engineering
Georgia Institute of Technology
Brief Speaker Bio :
Srinivas Aluru is co-Executive Director of the Georgia Tech Interdisciplinary Research Institute (IRI) in Data Engineering and Science (IDEaS) and a professor in the School of Computational Science and Engineering within the College of Computing. He co-leads the NSF South Big Data Regional Innovation Hub which nurtures big data partnerships between organizations in the 16 Southern States and Washington D.C., and the NSF Transdisciplinary Research Institute for Advancing Data Science. Aluru conducts research in high performance computing, data science, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. He pioneered the development of parallel methods in computational biology, and contributed to the assembly and analysis of complex plant genomes. His group is currently focused on developing bioinformatics methods for high-throughput DNA sequencing, particularly error correction and genome assembly. In systems biology, his group is working on network inference methods using mutual information and Bayesian approaches, and network analysis techniques to further the knowledge of partially characterized pathways. His contributions in scientific computing lie in parallel Fast Multipole Method, domain decomposition methods, spatial data structures, and applications in computational electromagnetics and materials informatics. Aluru is a Fellow of the American Association for the Advancement of Science (AAAS) and the Institute for Electrical and Electronic Engineers (IEEE).