Deepak Ganesan

Deepak Ganesan- University of Massachusetts Amherst. USA

Deepak Ganesan is a Professor in the Department of Computer Science at UMASS Amherst. He received his Ph.D. in Computer Science from UCLA in 2004 and his bachelors in Computer Science from IIT, Madras in 1998.  Dr. Ganesan’s research is at the intersection of wireless and mobile sensing, mobile health applications, machine learning techniques for sensor data processing, and low-power embedded systems. His research focuses on the design of novel wireless and sensor systems based on strong theoretical foundations and grounded in real-world deployment and experimentation. His awards include the NSF CAREER Award, several Google and IBM faculty awards, UMass Junior Faculty Fellowship, and a UMass Lilly Teaching Fellowship. He has been a Program co-chair for ACM MobiSys 2017, ACM SenSys 2010 and IEEE SECON 2013. His recent work has been recognized by a Best Paper Award Nominations or Awards at various top conferences in computer science including MobiSys 2017, Mobicom 2014, CHI 2013, Ubicomp 2013 and SECON 2007.

Research stay at UC3M: DEPARTMENT OF TELEMATIC ENGINEERING

Project:

The work will focus on research questions in two areas. The first is novel communication mechanisms at the intersection of Visible Light Communication (VLC) and our prior research in the area of ultra-low power sparse sampling cameras and wireless backscatter communication. Strategies for combining VLC with sparse sampling with camera to enable more robust communication in the presence of interference and reflections will be considered. In addition to sparse sampling, the work will also explore communication methods that combine VLC with ultra-low power backscatter communication to enable hybrid VLC-Backscatter low-power radios. The second direction of interest is 5G mobile network architectures and technologies that are adaptive and can handle diverse traffic requirements and demand fluctuations from a heterogeneous set of applications. In particular, the work will explore how analytics can be distributed over 5G mobile network architectures to achieve distributed ultra-low latency processing for tactile computing applications.