I am an Assistant Professor of Electrical and Computer Engineering at Purdue University.

My research is at the intersection of networking and machine learning. My group develops data-driven optimization methodologies for communication and social networks, with a particular emphasis on distributed edge intelligence. Contemporary network architectures we investigate include Fog computing systems, the Internet of Things (IoT), and social learning networks (SLN), and our foundational techniques include convex and non-convex optimization, machine learning, and signal processing.

Prior to joining Purdue, I was the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering at Princeton University, where I received my PhD. My PhD thesis won the 2016 Bede Liu Best Dissertation Award in Electrical Engineering. I also co-founded the big data startup company Zoomi Inc based on my research, which has provided employee performance optimization to more than one million users worldwide.

I am also active in teaching. In addition to lecturing, I co-authored the book The Power of Networks: Six Principles That Connect Our Lives and have taught three Massive Open Online Courses (MOOCs) on networking. Together, these MOOCs have reached over 400,000 students around the world since 2012.

Note to prospective graduate students: While I am not actively hiring, I am always on the lookout for highly motivated students who want to conduct research in my area. If you fit this description, feel free to send me an email that contains at least (i) your CV and (ii) something which demonstrates to me that you have read one of my recent papers. I apologize in advance that I cannot respond to every email.

Note to prospective postdocs: I currently have one position available, focused on machine learning for wireless communications. This position will be joint with other faculty in Purdue ECE. If you are interested, please send me an email with your CV. I apologize in advance that I cannot respond to every email.


News

  • Sept 2021: Our paper on robust automatic modulation classification has been accepted to IEEE Transactions on Cognitive Communications and Networking. Congrats, Rajeev!
  • Aug 2021: Our paper on semi-decentralized federated learning has been accepted to IEEE Journal on Selected Areas in Communications. An abridged version has also been accepted to IEEE Globecom. Congrats, Frank, Shams, and Ali!
  • Jul 2021: I have started as an associate editor for IEEE Transactions on Wireless Communications in the ML and AI area.
  • Jun 2021: Our paper on P300 waveform classification has been accepted to IEEE Access. Congrats, Rajeev!
  • May 2021: Our three-year project on Spectrum System Intelligence (with D. Love, T. Kim, M. Hashemi) supported by ONR has begun. Also, our work on single document text modeling was accepted to ACM Transactions on Intelligent Systems and Technology.
  • Apr 2021: Paper on reinforcement learning for intelligent surfaces, led by Junghoon, has been accepted to IEEE ICC 2021. Congrats, Junghoon! Also, our work on network-aware distributed learning was accepted to IEEE/ACM Transactions on Networking.
  • Mar 2021: Our project on Machine Learning for Satellite Communications (with D. Love) has been funded by MIT Lincoln Labs.
  • Feb 2021: Paper on automated modulation classification led by Rajeev accepted to IEEE CISS 2021. Congrats Rajeev!
  • Jan 2021: Two papers accepted to IEEE ICC 2021, one on secure automated modulation classification led by Rajeev, and one on learning-based channel estimation led by Myeung. Congrats Myeung and Rajeev!
  • Dec 2020: Our proposal Dynamic Spectrum Sharing 5G Network Enhancements Prototype (with Raytheon BBN Technologies [lead], D. Love, J. Krogmeier, and C. Wang) has been awarded by NSC.
  • Dec 2020: Our paper on device sampling for federated learning, led by Henry, was accepted to IEEE INFOCOM 2021. Congrats, Henry!
  • Nov 2020: Paper on fast convergent federated learning accepted to IEEE Journal on Selected Areas in Communications.
  • Oct 2020: We are organizing the First International Workshop on Distributed Machine Learning and Fog Networks, which will be hosted at IEEE INFOCOM 2021. The details and CFP can be found here. Hope to see you there!