LENS Lab

Overview

The Learning and Emerging Networked Systems (LENS) Laboratory at 002 WEB is co-directed by Prof. Srinivas Shakkottai and Prof. Dileep Kalathil. The objectives of the laboratory are to conduct analytical and experimental research in machine learning with networked systems applications, and to facilitate the integration of research into undergraduate and graduate education. The latter objective is directly aimed at enhancing the academic experience of engineering students at Texas A&M University. Key to the success of this program is the current enthusiasm among students for emerging technologies, including distributed learning and control platforms that operate over the network stack.

We work with different programming environments, including OpenAIGym, python packages for machine learning, open source communication stacks such as srsLTE, and OpenFlow for software defined networking (SDN). Hardware support includes GPU workstations from Lambda Labs, Software Defined Radios (SDR) from National Instruments in both sub-six and mm-wave bands, Amazon DeepRacers, assorted Android-based smart devices, as well as other equipment for laboratory and field experiments. We are also supported in conducting real-world field experiments by the Bush Combat Development Complex based at a former airbase located about ten miles away from the main campus.

Participants are encouraged to act as a community of experts and talk about their experiences with one another. Prototyping often poses many technical challenges. Getting involved at the LENS lab entails being exposed to the frustrations and the rewards associated with open-ended engineering problems.

The lab is supported through the sponsorship of several organizations including Department of Electrical and Computer Engineering at Texas A&M University, National Instruments, Google, the National Science Foundation, and The US Army Futures Command, among others.

Aggie DeepRacers

This is an initiative specifically aimed at applied machine learning in a robotics context using the Amazon DeepRacer platform. More details will be forthcoming as we develop the initiative over Fall 2020.

Teaching

Several courses taught by the faculty involved formally include application development on different platforms. These include:

  • ECEN 424 Fundamentals of Networking
  • ECEN 489 Artificial Intelligence
  • ECEN 489 Mobile Applications with Android
  • ECEN 689 Reinforcement Leanring
Research

The faculty involved each has their own research program. Prof. Shakkottai’s research interests include caching and content distribution, wireless networks, multi-agent learning and game theory, reinforcement learning, as well as network data analytics.

Resources

Please find the internal resources page here (password required).