Ph.D. Students

PhD admissions to my group:  Potential PhD students are welcome to contact me. Due to the large number of requests, I may not be able to respond to every message. Certain online platforms (unrelated to Cornell) host fake, misleading reviews about professors. For an accurate perspective on my lab, please reach out to my current or recent PhD students listed below. Anyone not listed below is not my PhD student.

I typically admit one student per year into my lab, after careful vetting. I am interested in students with a strong math background (undergrad in ECE, math or statistics). My research group is small and elite, with highly motivated, independent thinkers who thrive in a rigorous research environment. Excellence in a PhD, as witnessed by outstanding publications, is a must — see the deep papers coauthored with my students.

PhD Progress:  The ability to generate creative ideas and follow them to completion is crucial. The A-exam (after two years) is a sieve — students with strong research sail through, while inadequate progress results in a terminal Master’s degree. Academic honesty is essential — stealing authorship, fabricating results, or engaging in online harassment (including public platforms) are grounds for dismissal.

Research Topics:  My PhD students do serious math courses in real analysis, measure theoretic probability, nonlinear systems and statistical learning theory in the first year of the PhD program. The PhD student can enrol in Electrical & Computer Engineering, or Center for Applied Math or Mechanical & Aerospace Engineering.
My current research areas include:

• Social Networks Comprising Large Language Models and Sensing – Network Science with Reinforcement Learning: Is is predicted that in 10 years, 95% of web traffic will be due to LLMs interacting with other LLMs. How do multiple LLMs and humans interact in a social network? How to mitigate hallucination and spread of misinformation amongst LLMs? How to use networks of LLMs as a real time adaptive sensor? How to  model & control the dynamics of information flow in social networks?
• Behavioral Economics, Statistical Signal Processing & Machine Learning- The human sensor interface: How does human decision making interface with signal processing algorithms? How to optimize sensing with human behavioral constraints? Stochastic Optimization, Partially Observed Markov Decision Processes (POMDPs), RL and Game Theory
• Adversarial Sensing and Inverse Reinforcement Learning: How should cognitive radars autonomously reconfigure their measurement modes based on their Bayesian estimates? How to calibrate your adversary’s sensors capabilities and intent? How to detect cognition? We use micro-economics and inverse re-inforcement learning

Current PhD Students. Check out our new lab website 

• Shashwat Jain (did undergraduate/masters at IIT Kharagpur)
• Luke Snow (did undergraduate at Clemson Univ, NSF Fellowship)
• Adit Jain (did undergraduate at IIT Guwahati)
• Yiming Zhang (did undergraduate at Shanghai Jiao Tong and MEng at Cornell)

Alumni.

As a professor at Univ of Melbourne, then UBC, and now Cornell, I have been fortunate to have supervised amazing PhD students who have gone on to outstanding careers in academia and industry. You can download many of the PhD theses below.