About
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.
Curriculum Vitae
Research
My current research interests span programming languages, Bayesian nonparametric models, Markov Chain Monte Carlo methods, statistical learning theory 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.
News and Travel
December 2019
- Presented a poster at the Program Transformations for Machine Learning workshop in the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019).
| poster | extended abstract
January 2018
- 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
November 2017
- I am pleased to announce that I’ve recieved the Department of Computer Science 50th Anniversary Graduate Scholarship
Preprints and Research Articles
Approximations in Probabilistic Programs
Ekansh Sharma and Daniel M. Roy
| arXiv:1912.06791 | poster
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
| arXiv:1712.01745
Modeling trajectories of mental health: challenges and opportunities
(with Lauren Erdman, Anna Goldenberg et. al)
Appeared in NIPS Workshop Machine Learning for Health 2016
| arXiv:1612.01055
Teaching
Fall 2019: Teaching Assistant for the graduate course CSC2515H: Machine Learning
Winter 2019, Winter 2017, Fall 2016: Teaching Assistant for CSC236H1: Introduction to the Theory of Computation
Fall 2018: Teaching Assistant for CSC263H1: Data Structures and Analysis
Winter 2018: Teaching Assistant for CSC240H1: Enriched Introduction to the Theory of Computation
Contact
Email: ekansh -at- cs -dot- toronto -dot- edu
Office:
Vector Institute
MaRS Centre, West Tower
661 University Ave., Suite 710