Azizul Zahid

I am a researcher in Human Computer Interaction (HCI) and AI at EPIC Lab, Electrical Engineering and Computer science (EECS), University of Tennessee Knoxville (UTK).

I am also a 2nd year PhD student in Computer Engineering working with Dr. Sai Swaminathan.

My reserach area lies in Human-AI Interaction, focusing on Intelligent Robot. I have substantial experience on end-to-end intelligent robots prototyping with utilizing Reinforcement Learning (RL) algorithms.

I earned my B.Sc. from Bangladesh University of Engineering and Technology (BUET) majoring Electrical and Electronic Engineering (EEE).

📢 I am actively looking for summer internship opportunities for 2025. If you know any of opportunities, feel free to reach out to me.

news

Apr 22, 2025	My workshop paper got accepted to ICRA 25 conference.
Mar 10, 2025	My first phd paper as a lead author got accepted to CHASE 25 conference.
Feb 01, 2025	Submitted another paper on RadioGami system to IMWUT’25.
Jan 21, 2025	Started new semester. I am taking two courses (Computer vision (ECE 574) and Robot opt., est. & cont.(ECE 599)). I will also be TA of Computer system organization (ECE 356/358) course.
Jan 06, 2025	Submitted my first phd paper on Human in the loop RL for smart wheelchair as a lead author to CHASE’25.

selected publications

IMWUT’24
NeuroCamTags: Long-Range, Battery-free, Wireless Sensing with Neuromorphic Cameras

Danny Scott, Matthew Bringle, Imran Fahad, and 3 more authors

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Sep 2024

Abs DOI Bib PDF

In this research, we introduce NeuroCamTags, a battery-free platform designed to detect a range of rich human interactions and activities in entire rooms and floors without the need for batteries. The NeuroCamTag system comprises low-cost tags that harvest ambient light energy and utilize high-frequency modulation of light-emitting diodes (LEDs) for wireless communication. These visual signals are captured by an available neuromorphic camera, which boasts temporal resolution and frame rates an order of magnitude higher than those of conventional cameras. We present an event processing pipeline that allows simultaneous localization and identification of multiple unique tags. NeuroCamTags offer a wide range of functionalities, providing battery-free wireless sensing for various physical stimuli, including changes in temperature, contact, button presses, key presses, and even sound cues. Our empirical evaluations demonstrate impressive accuracy at long ranges up to 200 feet. In addition to these findings, we consider a range of applications such as battery-free input devices, tracking of human movement, and long-range detection of human activities in various environments such as kitchens, workshops, etc. By reducing reliance on batteries, NeuroCamTags promotes eco-friendliness and opens doors to exciting possibilities in smart environment technology.
@article{IMWUT24_Scott, author = {Scott, Danny and Bringle, Matthew and Fahad, Imran and Morales, Gaddiel and Zahid, Azizul and Swaminathan, Sai}, title = {NeuroCamTags: Long-Range, Battery-free, Wireless Sensing with Neuromorphic Cameras}, year = {2024}, issue_date = {September 2024}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {8}, number = {3}, url = {https://doi.org/10.1145/3678529}, doi = {10.1145/3678529}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = sep, articleno = {122}, numpages = {25}, keywords = {Activity sensing, Battery-free, Context Aware Computing, Wireless sensing} }