About Me

I work on designing and understanding machine learning algorithms that can be applied to novel problems. I am currently a Machine Learning Scientist at Pendulum Systems. Before, I worked as a Machine Learning Researcher at Morgan Stanley.

I received a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania, and an M.A. in Statistics from the Wharton School. I had postdoctoral stints in the Electrical Engineering and Computer Sciences department at the University of California, Berkeley, and the Electrical and Computer Engineering department at Rice University. I have also been a research intern at Facebook Artificial Intelligence Research in Montréal, and a visiting scholar at TU Delft in the Netherlands.

The core of my published research is related to machine learning and signal processing for graph-structured data. I have devised novel graph neural network (GNNs) architectures, developed theoretical insights that help explain and understand their strengths and limitations, and pioneered their use across diverse applications including power grids, sensor networks, multi-agent robotics, and distributed control. My background in statistics, time series analysis, and signal processing has allowed me to bring a fresh perspective to machine learning problems, leveraging concepts from frequency analysis, filtering, ergodicity, and stochastic processes.