I am currently a graduate student pursuing my PhD in the Electrical Engineering department at Princeton University. I am advised by Miklós Rácz and H. Vincent Poor. Before starting grad school, I completed my Bachelor’s degree in Electrical and Computer Engineering (ECE) at Carnegie Mellon University, where I worked with Jeremiah Blocki and Soummya Kar.
Broadly, my research interests are in network science, applied probability and statistics. My current work focuses on inference in random graphs and network cascades.
For more details, check out my CV.
- (Sep 2021) My paper on correlated stochastic block models was accepted for a spotlight presentation at NeurIPS 2021.
- (Summer 2021) I was an intern with the Machine Learning University team at Amazon, where I taught a course on Natural Language Processing to Amazon employees (mainly software developers) and also designed a course on Graphical Models.
- (July 2021) I presented my work on modeling mutations and mask-wearing in viral spread on networks at the Networks 2021 conference.
- (June 2021) Two of my papers were presented at ICASSP. One was on the effect of viral mutations in network epidemics, with implications for the COVID-19 pandemic. The other derived optimal algorithms for tracking a cascade under noisy observations.
- (May 2021) My paper on quantifying the effects of mask-wearing in network epidemics was presented at the American Control Conference.
- (March 2021) My paper on modeling viral spread with applications to COVID-19 will appear in the Harvard Data Science Review. Especially exciting as this is my first journal publication!
Awards and Honors:
- Interdisciplinary Fellowship from Princeton’s ECE Department, Spring 2021.
- Finalist for the INFORMS-APS Best Student Paper Award, 2020.
- M. Z. Rácz, A. Sridhar. Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities. To appear in NeurIPS 2021 (spotlight presentation), July 2021. [arXiv link]
- O. Yagan, A. Sridhar, R. Eletreby, S. A. Levin, J. B. Plotkin, H. V. Poor. Modeling and Analysis of the Spread of COVID-19 Under a Multiple-Strain Model with Mutations. Harvard Data Science Review, April 2021. [Paper link]
- M. Z. Rácz, A. Sridhar. Correlated Randomly Growing Graphs. To appear in the Annals of Applied Probability, April 2020. [arXiv link]