Ph.D. Students

For students seeking PhD admission to my group: 

Given the huge number of enquiries, I accept a small proportion of applicants with very strong academic credentials. Because of the sheer volume of emails, please do not be offended if I do not reply.

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 either in Electrical & Computer Engineering, or the Center for Applied Math or Mechanical & Aerospace Engineering. My current areas of interest include:

  • 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 Computational Game Theory
  • Social Network Analytics and Sensing – Network Science with Reinforcement Learning: How to use social networks as a real time adaptive sensor? How to  model & control the dynamics of information flow in social networks?
  • 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 

  • Kunal Pattanayak (undergraduate/masters at IIT Kharagpur): Inverse Reinforcement Learning
  • Rui Luo  (did undergraduate at Tsinghua Univ): Statistical Signal Processing of social networks
  • Anurag Gupta (did undergraduate/masters at IIT Mumbai)
  • Shashwat Jain (did undergraduate/masters at IIT Kharagpur)
  • Luke Snow (did undergraduate at Clemson Univ)


I have been fortunate to have worked with outstanding PhD students. You can download many of the PhD theses below.

Andrew Logothetis (1998) EM Algorithms for State and Parameter Estimation Chief Engineer, Airspan, U.K.
Jonathan Manton (1998) Optimal Estimation and Identification of Linear Systems — Stochastic and Algebraic Approaches Future Generation Professor, University of Melbourne
Kenneth Tan (1997) Nonlinear Signal Processing Techniques based on the EM algorithm
Jamie Evans (1998) Studies in Nonlinear Filtering Theory – Random Parameter Linear Systems, Target Tracking and Communication Constrained Estimation Professor and vice-Dean, University of Melbourne
Leigh Johnston (2000) Iterative Algorithms for Estimation of Nonlinear Stochastic Dynamical Systems Professor, University of Melbourne.
S. Singh (2003) Optimization Issues in DS/CDMA Wirless Networks Associate Professor, Cambridge Univ
Sam McLaughlin (2006) Data Incest in Decentralized Estimation Systems Research Engineer ADI Thales Limited, France
Arsalan Farrokh(2007) Stochastic Resource Allocation in Wireless Networks Mathematical Finance, Phillips, Hager & North Investment Management Ltd.
Minh Ngo (2007) Cross Layer Adaptive Transmission Scheduling in Wireless Networks Mathematical Finance, Phillips, Hager & North Investment Management Ltd
Michael Maskery (2007) Game Theoretic Methods for Networked Sensors and Dynamic Spectrum Access Patent Engineer, MBM Intellectual Property Law
Laxminarayana Pillutla (2008) Resource Management in Wireless Networks Research Engineer, Apple , USA
Hassan Mansour (2009) Modeling of Scalable Video Content for Multi-user Wireless Transmission Mitsubishi Electric Research Labs
Farhad Ghassemi (2009) Sensor Management with Applications in Localization and Tracking Data Scientist, Microsoft, USA
Alex Wang (2009) Meta-level Tracking with Stochastic Grammar Research engineer, Amazon, Canada
Jane Huang (2011) Application of Game Theory in Wireless Communication Networks Senior Engineer in Machine Learning, Google
Kevin Topley (2012) Average Consensus in Two-time Scale Markovian Systems
Maryam Abolfath-Beygi (2013) Biosensor Arrays for Molecular Source Identification in Mass-Transport Systems Data Scientist, Nike, Portland.
Omid Namvar (2015) Stochastic Approximation Methods for Decision Making in Non-stationary Uncertain Environments Quantitative Finance,  CPP Investment Board, Toronto
Mustafa Fanswala (2015) Meta-level Pattern Analysis for Target Tracking Principal Engineer Lead, Microsoft Hololens
William Hoiles (2015) Biosensing and Electrophysiological Response Principal AI Scientist, Katerra, Toronto
Maziyar Hamdi (2015) Statistical Signal Processing on Dynamic Graphs with Applications in Social Networks Data Scientist, Qudos Inc, Vancouver
Mohammad Ghasemi (2016) Planning and Operation of Active Smart Grids  Risk Management at TD Bank
Anup Aprem (2017) Detection, Estimation and Control in Online Social Media National Institute of Technology, Calicut
Tanzil Shahrear (2018) Mobile Edge Cloud: Computation and Caching Ericsson, Sweden
Sujay Bhatt (2019) @Cornell Controlled Social Sensing: A POMDP Approach AI Research lead, Morgan Stanley, New York.
Buddhika Nettasinghe (2021) @Cornell Statistical Modeling and Inference in Social Networks Assistant Professor, U Iowa, Tippie College of Business