Nilanjan Banerjee is an Associate Professor at University of Maryland, Baltimore County. He is an expert in mobile and sensor systems with focus on designing end-to-end cyber-physical systems with applications to physical rehabilitation, physiological monitoring, and home energy management systems. His research is funded by the National Science Foundation, National Insitutes of Health, Office of Naval Research, Army Research Lab, Microsoft, and Technology Development Corporation. He has published more than 60 peer-reviewed conference and journal articles in top conferences including MobiSys, IPSN, Mobicom, Ubicomp, RTSS, Sensors, IEEE Transactions on Networking, IEEE Transactions on Multi-scale Computing, and IEEE Sensors. He is a National Science Foundation CAREER awardee and has received a Microsoft Research Software Engineering Innovations Award, UMBC Up and Coming Inventor, a UMBC Innovation Collaborative, a Yahoo! Outstanding Dissertation Award, and a Best Undergraduate Thesis award. His research and commercialization effort has been highlited in several news outlets including the Baltimore Sun, the Washington Post, and the MIT technology review. He holds a Ph.D. and a M.S. in Computer Science from the University of Massachusetts Amherst and a BTech. (Hons.) from Indian Institute of Technology, Kharagpur.

Recent News

Research Paper News

    IEEE Sensors August 2016
  • Wearable radar breathing detection paper accepted to IEEE Sensors.

  • IEEE Sensors Journal February 2016
  • Differential Capacitance paper accepted to IEEE Sensors Journal.

  • Infocom 2016 Novemeber 2015
  • RAM paper accepted to IEEE Infocom 2016.

  • IEEE Transactions in Multi-Scale Computing Systems October 2015
  • Advanced Gesture Recognition paper accepted to IEEE Transactions on Multi-scale computing systems.

  • IEEE Sensors Journal September 2015
  • Distracted Driving paper accepted to IEEE Sensors Journal.

  • Euroviz March 2015
  • Energy Visualization paper accepted to EuroVis 2015.

  • Host Feb 2015
  • "Post Fabrication" paper on Security Vulnerabilities in IEEE Host 2015

  • IPSN Jan 2015
  • SunaPlayer paper on emulation of solar cells in ACM/IEEE IPSN 2015

  • IPSN Jan 2015
  • Tongue-n-Cheek paper on tongue gesture recongition in paralysis patients in ACM/IEEE IPSN 2015

Research Funding News

    DCS August 2016
  • Study Human Adaptations ($170,000).

  • NSF April 2016
  • NSF Grant ($200,000).

  • TEDCO March 2016
  • TEDCO Grant ($150,000).

  • Hrabowski Innovation Fund December 2015
  • Hrabowski Innovation Fund ($24,000).

  • Constellation Inc. November 2015
  • E2 Energy to Educate Grant ($25,000).

  • NIH November 2015
  • NIH Grant for Pediatric Asthma Detection ($2,000,000).

  • NSF September 2015
  • NSF Grant on Utility-driven Energy Management Services. ($1,000,000).

  • NSF September 2015
  • NSF Grant on Energy Assessment System. ($500,000).

  • ONR April 2015
  • ONR Grant on Anomaly Detection in Cyber-physical Systems. ($300,000).

  • TEDCO Jan 2015
  • TEDCO Grant on developing a Sleep Monitoring System with Johns Hopkins University ($150,000).

  • TEDCO September 2014
  • TEDCO Grant on developing Textile capacitive sensor system.

  • NSF September 2014
  • NSF-NIH grant on developing assistive care systems for severly paralyzed patients.
  • Microsoft February 2013
  • Microsoft Research Software Engineering Innovations Award.
  • NSF, Unity Education, Microsoft Earlier than 2013
  • Several Awards including NSF CAREER, NSF CSR Awards (2012, 2013), Gift from Unity Education.

Award and Media News

    Baltimore Sun September 2016
  • News article mentioning our textile sensor project [article]

  • HOST April 2016
  • HOST Demo on Hardware Security wins third prize

  • MIT Technology Review April 2016
  • News article mentioning our textile sensor project [article]

  • Baltimore Sun and Washington Post March 2016
  • News article on our commercialization effort [article]

  • Hrabowski Innovation Fund November 2015
  • Awarded the UMBC Innovation Collaborative.

  • Inventor's Lunch November 2015
  • Awarded the UMBC Up and coming Inventor.

  • PerCom15 March 2015
  • InviZ Demo wins Best Demo Award (Runner-up) at IEEE PerCom 2015.

  • NSF March 2014
  • Jackson Schmandt and Alexander Nelson received the NSF SFS Fellowship and NSF Graduate Fellowship, Honorable Mention.

  • Microsoft June 2013
  • Microsoft Research Software Engineering Innovations Award, 2013

  • NSF Feb 2011
  • National Science Foundation Early CAREER Award.

Research Projects

Home and Building Energy Management

Our goal is to make it easier for home residents to make smart choices about managing energy. Renewable technologies, such as solar and wind, are becoming more widely adopted, however, current best practices for energy use and conservation do not necessarily apply in green homes. This project seeks to better understand energy generation and consumption in green homes, and to explore automated techniques for helping residents to achieve better utilization of resources. This includes building demand response systems, energy analytics for home energy usage, and visualization systems for home energy usage. Project website

PerEnergy2015 PMC 2014 IGCC2014 CHIEA2014 PerCom2014 BuildSys2013

Wearable Assistive Devices

An estimated 1.5 million individuals in the United States are hospitalized each year because of strokes, brain injuries and spinal cord injuries. Severe impairment such as paralysis, paresis, weakness and limited range of motion are common sequels resulting from these injuries, requiring extensive rehabilitation. This project is developing invisible sensing systems (using textile-based capacitive sensor arrays and micro-doppler radars) embedded into bed sheets, pillows, wheelchair pads, and clothing, for environmental control and physical therapy for such paralysis patients. The system detects gestures regardless of evolving environmental and patient conditions and provides explicit real-time feedback to the user. Through the use of low-cost and ultra-low power capacitive sensing and micro-radars built into headgears, the system reduces hospital visits and therapy costs.

IPSN2015 DATE2015 PerCom2015 RTSS2013 Sensor2013

Hierarchical Low Power Systems

In a number of projects, we are working on developing hierarchical processing systems. These include combining processors of different capabilities and energy consumption, as well as platforms with varying capabilities and energy consumption into a single integrated system that has a wide operating range and low energy consumption. We have applied the concept to developing systems for gait analysis, sensor microserver design, solar power nodes for emergency control.

IPSN2015 DATE2015 TGIS2013 RTSS2013 ToN2011 Mobicom2008

MobiSys2007 Ubicomp2007 Infocom2007 MobiSys2005

hardware pcb

Embedded Hardware Security

We study power-supply sidechannel leakage on FPGAs and ASICs through hardware experimentaion and simulation. Our goal is discovery of new side-channel volnerabilites along with techniques and EDA tools for coutermeasures in embedded systems. We are espcially focused on security for low-power embedded systems.

HOST 2015 VTS 2015 LATS 2015

Human Assisted Computer Vision

Sidewalk navigation for the visually impaired, especially those who use wheelchairs, can be a daunting task. While laws advocate proper standards for accessibility-compatible sidewalks, several develop cracks and obstacles over time and many have curbs and steps. Emerging wearable devices such as Google glasses provide an opportunity for continuous vision-based systems that can navigate individuals around accessibility issues on sidewalks. Unfortunately, real-time vision-based navigation systems are scarce. The problem stems from a basic limitation of vision algorithms---without a priori contextual information on a scene, it is impossible for a vision algorithm to search for objects of interest. To address this critical problem, this project proposes a cyber-physical system that augments machine vision algorithms with a priori contextual information collected using human crowdsourcing. The key idea is to use humans in conjunction with custom system build a rich library of information on scenes with accessibility issues. This library can then be used to design context aware machine vision algorithms that can efficiently detect accessibility problems in real-time. Project website