Associate Professor, Computer Science and Electrical Engineering
Nilanjan Banerjee joined UMBC’s Computer Science and Electrical Engineering Department in the Fall of 2012 as an Assistant Professor. Specializing in embedded and distributed systems research for mobile, pervasive, and sustainability based computing, Banerjee directs UMBC’s Mobile, Pervasive, and Sensor Systems Laboratory.
Before coming to UMBC, Banerjee taught programming paradigms, mobile and pervasive computing, and mobile phone application development as an assistant professor in the University of Arkansas’ Computer Science and Computer Engineering Department.
He is a 2011 NSF Career Awardee and received the Outstanding Researcher Award, 2011 from the College of Engineering at University of Arkansas. Banerjee has received research grants from NSF, Microsoft Research, Unity Gaming, and Crop boards. He holds a Ph.D. in Computer Science from the University of Massachusetts, where he won the Yahoo! Outstanding Dissertation Award in 2009, and has a BTech from IIT Kharagpur where he wont the best undergraduate thesis award in 2004.
Systems Support for Green Homes
Our goal is to make it easier for off-grid and grid-tied 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.
- Nomadic Health Diagnostics
Providing timely healthcare to patients with critical health conditions can save thousands of lives. A primary component of such a system is non-intrusive implantable sensors that can gather important body statistics. To detect anomalies in real time, we need to transfer and process large volumes of health monitoring data. We are building surrogate devices that download data from nano-sensors and stream it to our back-end server for processing. Further, we are designing efficient machine learning algorithms that can process large volumes of data to infer disparities. Another important design consideration specific to the healthcare application domain is security. Since the data is highly confidential and the surrogate and sensor devices are computationally weak platforms, we are working on light weight security protocols to transfer data from the sensor to the cloud.
Office: ITE 362