I am a 4th year PhD Candidate at the Electrical and Computer Engineering Dept. of Carnegie Mellon University. I enjoy applying economics, optimization and machine learning to wireless networks and sensor data. I have taken rigorous coursework to specialize in related techniques like Deep Learning, Deep Reinforcement Learning and Optimization Methods.
Through my research work, I’ve explored incentive mechanisms for user-centric resource allocation in wireless networks and machine learning techniques for mining contextual information from IoT data. I’ve worked on pricing optimizations for cellular dataplans, auction mechanisms for 5G, reinforcement learning for budget-driven bidding, and deep learning for vehicle location tracking.
Recently, I’ve been intrigued by the possible uses of blockchain in edge networking. I’m interested in understanding the pricing and design of crypto-token systems for enabling edge scenarios like shared computing and networking. I am collaborating with blockchain-based IoT service provider Nodle in this effort. I am also interested in exploring the use of IoT sensor data in solving last-mile challenges in blockchain.
I primarily work out of the Silicon Valley campus. I am a part of the MEWS research group and am advised by Prof. Patrick Tague. I also work in close collaboration with Prof. Carlee Joe-Wong. My research has been supported by various NSF and DARPA grants. I recently proposed my thesis entitled Incentivizing Real-time and User-centric Resource Allocation in Wireless Networks, with Prof. Patrick Tague, Prof. Carlee Joe-Wong, Prof. Aron Laszka and Dr. Anand Raman as my committee.
I did my undergraduate studies at the ECE dept. of Rutgers University, New Brunswick campus. I finished the Honors program in 3 years and graduated Summa Cum Laude in 2013 with Bachelors of Science in ECE. During this time, I also conducted an year of undergraduate research at WINLAB, and was recognized as a James Slade Scholar upon graduation.