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Sanjay Ranka is a Professor
in the Department of Computer Information Science and Engineering
at University of Florida. His current research interests are
high performance and parallel computing with a focus on energy
efficiency; and big data science with a focus on data mining/machine
learning algorithms for spatiotemporal applications. His work
is driven by applications in CFD, remote sensing, health care
and transportation. He teaches courses on algorithms, data
science and parallel computing. From 1999-2002, he was the
Chief Technology Officer at Paramark (Sunnyvale, CA). At Paramark,
he developed a real-time optimization service called PILOT
for marketing campaigns. Paramark was recognized by VentureWire/Technologic
Partners as a top 100 Internet technology company in 2001
and 2002 and was acquired in 2002. He has also held positions
as a tenured faculty at Syracuse University and as a visitor
at IBM T.J. Watson Research Labs. Sanjay earned his Ph.D.
(Computer Science) from the University of Minnesota and a
B. Tech. in Computer Science from IIT, Kanpur, India. He has
coauthored four books, 230+ journal and refereed conference
articles. His recent co-authored work has received a best
paper award at BICOB 2014, best student paper award at ACM-BCB
2010, best paper runner up award at KDD-2009, a nomination
for the Robbins Prize for the best paper in journal of Physics
in Medicine and Biology for 2008, and a best paper award at
ICN 2007. He is a fellow of the IEEE and AAAS, and a past
member of IFIP Committee on System Modeling and Optimization.
He is an associate Editor-in-Chief of the Journal of Parallel
and Distributed Computing and an associate editor for ACM
Computing Surveys, IEEE/ACM Transactions on Computational
Biology and Bioinformatics, Sustainable Computing: Systems
and Informatics, Knowledge and Information Systems, and International
Journal of Computing. Additionally, he is a book series editor
for CRC Press for Bigdata. In the past, he has been an associate
editor for IEEE Transactions on Parallel and Distributed Systems
and IEEE Transactions on Computers. He was the program chair
for 2015 High Performance Computing, 2013 International Parallel
and Distributed Processing Symposium, 2010 International Conference
on Contemporary Computing and co-general chair for 2009 International
Conference on Data Mining and 2010 International Conference
on Green Computing
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Title: High
Performance Computing and Data Science for Large Scale Spatiotemporal
Applications
Abstract: Research in
high performance and data science has become an important
avenue for novel discoveries in a number of science, engineering,
and defense applications. Many of these applications generate
gigabytes to terabytes of data per day, require petaflops
to exaflops of computing power and are spatiotemporal in nature.
Large scale processing and mining of these applications require
careful understanding of the underlying spatial and temporal
locality and relationships. In this talk, I will present my
research on development of performance and/or energy efficient
algorithms and software for such applications in numerical
analysis, computational fluid dynamics, urban transportation,
and remote sensing.
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