Keynote Address:

Prof. Sanjay Ranka
Professor, Department of Computer Science and Engineering
University of Florida, Gainesville, FL, USA

Talk Title: 
Smart Intersection Control Algorithms for Automated and Connected Vehicles

Abstract:
The pace at which autonomous and connected vehicle technology is reaching the consumer market is accelerating. These driver and driverless vehicles will communicate information with other vehicles and transportation infrastructure. In this talk, we will present our work on optimizing signal control to take advantage of these developments. We will provide extensive simulation results as well as our initial testing of our algorithms On Traffic Engineering Research Laboratory (TERL) in Tallahassee, Florida.

We will also describe related projects on I-STREET (Implementing Solutions from Transportation Research and Evaluation of Emerging Technologies) that University of Florida is developing in conjunction with the Florida Department of Transportation and the City of Gainesville. This testbed will encompass the University of Florida campus and the surrounding roadway network in the City of Gainesville.

Biography:
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 data science (three course curriculum), data mining 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. PILOT served more than 10 million optimized decisions a day in 2002 with a 99.99% uptime. 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 positions at Syracuse University and as a researcher/visitor at IBM T.J. Watson Research Labs and Hitachi America Limited.

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, 250+ journal and refereed conference articles. His recent co-authored work has received a best student paper runner up award at IGARSS 2015, 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.

ADVANCE PROGRAM 

CALL FOR PAPERS (CLOSED NOW)

What constitutes a “Big Data” problem? What application domains are best suited to benefit from Big Data analytics and computing? What are the traits and characteristics of an application that make it suited to exploit Big Data analytics? How can Big Data systems and frameworks be designed to allow the integration and analysis of complex data sets? How can research in Big Data Analytics benefit from the latest advances in supercomputing and High Performance Computing (HPC) architectures? The goal of this workshop is to address questions like these that are fundamental to the advancement of Big Data computing, and in the process, build a diverse research community that has a shared vision to advance the state of knowledge and discovery through Big Data computing.

Topics of interest include research contributions and innovative methods in the following areas (but not limited to):

• Scalable tools, techniques and technologies for Big Data analytics (e.g., graph and stream data analysis, machine learning and emerging deep learning methods)

•  Algorithms and Programming Models for Big Data

•  Big Data applications – Challenges and Solutions (e.g., life sciences, health informatics, geoinformatics, climate, socio-cultural dynamics, business analytics, cybersecurity)

•  Scalable Big Data systems, platforms, services, and management

•  Big Data toolkits, workflows, metrics,  and provenance.

We invite paper submissions that describe original research contributions in the area of Big Data computing, and position papers that highlight the potential challenges and opportunities that arise in Big Data computing. We also invite short papers that describe work-in-progress original research. 

Regular papers can be up to 8 pages long and short papers can be up to 4 pages long. All submissions will undergo rigorous peer-review by the technical program committee, and accepted manuscripts will appear in the HiPC workshop (“HIPCW”) proceedings and will be indexed by IEEE digital library. Authors of the accepted manuscripts will be required to present their work at the workshop proceedings.

Paper Submission link: The paper submission opens July 1, 2017. Click on the following link for submitting your papers: https://easychair.org/conferences/?conf=hipcbigdf17

Authors can submit an abstract prior to submitting the full paper for review. The abstract is not mandatory but is recommended to help organizers plan the review phase in a timely fashion (i.e., authors can submit a full paper without having submitted an abstract). However, submissions with only full papers will be reviewed.

Organizing Committee:

• General Chairs:        Dinkar Sitaram (PESIT), Ananth Kalyanaraman (Washington State University)

• Program Chairs:       Madhu Govindaraju (SUNY Binghamton), Saumyadipta Pyne (IIPH, Hyderabad)

• Publicity Chairs:       Arindam Pal (TCS Research)

• Proceedings Chair:  Ren Chen, USC (HiPC proceedings chair)

• Industry Liaison:      Vivek Yadav (FullStackNet, India)

Technical Program Committee:

• Medha Atre, IIT Kanpur

•  Ariful Azad, Lawrence Berkeley National Laboratory

• Biplab Banerjee,   IIT Roorkee

• Suren Byna,  Lawrence Berkeley National Laboratory

• Nabanita Das,   Indian Statistical Institute

• Oded Green,  Georgia Institute of Technology

• Manish Kurhekar, Visvesvaraya National Institute of Technology

• Suresh Marru,  Indiana University

• Arindam Pal, TCS Research

• Laks Raghupathi, Shell, India

• Sudip Seal, Oak Ridge National Laboratory

• Gokul Swamy,  Amazon

• Devesh Tiwari, Northeastern University

• Abhinav Vishnu, Pacific Northwest National Laboratory

• Yinglong Xia,  Huawei Research America

• Jaroslaw Zola, University of Buffalo

Schedule and Review Process: (UPDATED)