Welcome to the Networked & Embedded Systems Laboratory (NESL) at the University of California at Los Angeles (UCLA). Our lab is a part of the Electrical Engineering Department, and the Computer Science Department. NESL is physically located in 1762 Boelter Hall on UCLA's main campus in Westwood, Los Angeles. Our research is in the broad area of embedded and cyber-physical systems for applications in IoT, ubiquitous and mobile computing, and pervasive sensing and control. Our interests are in problems related to making these systems learning-enabled, secure, privacy-aware, human-coupled, wirelessly-networked, and energy-efficient. We take a full-stack approach in our research, working across all layers of systems including algorithms, applications, software, and hardware.
Feel free to explore this web site, and in case of any questions about NESL, please contact Prof. Mani Srivastava. To get a good sense of our current research activity, please look at Prof. Mani Srivastava's publication list on Google Scholar.
• New award on "Internet of Things” for the battlefield (2017-10-10) [Details]
• NESL gets NSF award on Privacy-Aware Trustworthy Control for IoT (2017-09-01) [Details]
• NESL researchers get the Best Demo Paper award at the 2017 IPSN/CPSWeek (2017-04-19) [Details]
• Jason Koh, Sandeep Singh Sandha, Bharathan Balaji, Daniel Crawl, Ilkay Altintas, Rajesh K. Gupta, and Mani B. Srivastava. "Data Hub Architecture for Smart Cities", Sensys 2017, November 2017. [ Details ]
• Eun Sun Lee, Jeya Vikranth Jeyakumar, Bharathan Balaji, and Mani B. Srivastava. "AquaMote - Ultra Low Power Sensor Tag for Animal Localization and Fine Motion Tracking", November 2017. [ Details ]
• Amr Alanwar, Bharathan Balaji, Yuan Tian, SHUO YANG, and Mani B. Srivastava. "EchoSafe: Sonar-based Verifiable Interaction with Intelligent Digital Agents", SafeThings 2017 - ACM Workshop on the Internet of Safe Thing, November 2017. [ Details ]
• Moustafa Alzantot. "Adversarial Attacks on Machine Learning Models", DAIS ITA Annual Fall Meeting 2017 - Deep Learning Workshop, October 2017. [ Details ]