I am a Ph.D. student in the Department of Computer Science at the University of Toronto. I am also affiliated to the newly founded Vector Institute. My research adviser is Prof. Daniel M. Roy.
I received my bachelor’s degree in Electrical Engineering also from the University of Toronto. During my undergrad, I worked with Prof. Anna Goldenberg on problems related to predicting growth trajectories and discovering causal genes.
My current research interests span programming languages, Bayesian nonparametric models, probabilistic inference, and (more broadly) machine learning.
I enjoy working on sophisticated models that represent underlying probabilistic symmetries in data. My research objective is to devise tractable inference algorithms for such models.
- I transitioned from the Master of Science program to the Doctor of Philosophy program.
- I gave a talk at the Probabilistic Programming Languages, Semantics, and Systems (PPS 2018) workshop in the 45th ACM SIGPLAN Symposium on Principles of Programming Languages (POPL 2018).
| slides | extended abstract
- I am pleased to announce that I’ve recieved the Department of Computer Science 50th Anniversary Graduate Scholarship
Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization
Victor Veitch, Ekansh Sharma, Zacharie Naulet, and Daniel M. Roy
| arXiv:1712.02311 | slides
An estimator for the tail-index of graphex processes
Zacharie Naulet, Ekansh Sharma, Victor Veitch, and Daniel M. Roy
Modeling trajectories of mental health: challenges and opportunities
(with Lauren Erdman, Anna Goldenberg et. al)
Appeared in NIPS Workshop Machine Learning for Health 2016
Winter 2018: Teaching Assistant for CSC240H1: Enriched Introduction to the Theory of Computation
Winter 2017, Fall 2016: Teaching Assistant for CSC236H1: Introduction to the Theory of Computation
Email: ekansh -at- cs -dot- toronto -dot- edu
Office: Vector Institute
MaRS Centre, West Tower
661 University Ave., Suite 710