Jesse Bettencourt

Graduate Student in Machine Learning

University of Toronto & Vector Institute

jessebett [at] cs [dot] toronto [dot] edu

Biography

I am a graduate student in Machine Learning at the University of Toronto and the Vector Institute. I am currently pursuing follow-up research to my work on Neural Ordinary Differential Equations, and am generally interested in approximate inference for latent variable models. I have recently completed an M.Sc. supervised by Drs. David Duvenaud and Roger Grosse, and am continuing as a Ph.D. student under David Duvenaud.

From January 2025 to January 2026 I completed an internship in NVIDIA's Spatial Intelligence Lab (SIL).

My teaching at the University of Toronto includes instructing CSC412/2506: Probabilistic Learning and Reasoning and STA414: Statistical Methods for Machine Learning II.

Interests

Neural ODEs
Approximate Inference
Automatic Differentiation

Education

PhD in Computer Science — University of Toronto, 2019–
MSc in Computer Science — University of Toronto, 2017–2019
MSc in Mathematics — University of Toronto, 2015–2016
BSc in Integrated Science and Mathematics — McMaster University, 2011–2015

Publications

Learning differential equations that are easy to solve

J. Kelly, J. Bettencourt, M. J. Johnson, D. K. Duvenaud

Advances in Neural Information Processing Systems, 2020.

PDF Code

Taylor-mode automatic differentiation for higher-order derivatives in JAX

J. Bettencourt, M. J. Johnson, D. Duvenaud

Program Transformations for ML Workshop, NeurIPS, 2019.

PDF Docs Code

DiffEqFlux.jl — A Julia library for neural differential equations

C. Rackauckas, M. Innes, Y. Ma, J. Bettencourt, L. White, V. Dixit

arXiv preprint, 2019.

PDF Blog Code

Neural Ordinary Differential Equations

A kind of continuous-depth Neural Network.

Ricky T. Q. Chen*, Yulia Rubanova*, Jesse Bettencourt*, David Duvenaud

Neural Information Processing Systems, 2018.
Oral. Best Paper Award.

PDF Code Poster Slides Video

FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Scaling up continuous normalizing flows by estimating the trace.

Will Grathwohl*, Ricky T. Q. Chen*, Jesse Bettencourt, Ilya Sutskever, David Duvenaud

International Conference on Learning Representations, 2019.
Oral. Best Student Paper @ AABI 2018.

PDF Code Video

* indicates equal contribution

Teaching

I have taught the following courses at University of Toronto:

CSC412/2506: Probabilistic Learning and Reasoning (Winter 2020)
STA414: Statistical Methods for Machine Learning II (Winter 2020)
CSC412/2506: Probabilistic Learning and Reasoning (Winter 2019)
CSC412/2506: Probabilistic Learning and Reasoning (Winter 2018)

In the past I have been a teaching assistant for the following courses:

CSC2541: Generative AI for Images (Fall 2024)
CSC411/2515: Introduction to Machine Learning (Fall 2018)
CSC411/2515: Introduction to Machine Learning (Fall 2017)
MAT136: Single Variable Calculus I for Science (Winter 2016)
MAT186: Single Variable Calculus I for Engineering (Fall 2016)
MAT235: Multivariable and Vector Calculus II for Science (Summer 2016)
MAT235: Multivariable and Vector Calculus II for Science (Year 2016)
ISCI2A18: Multivariable and Vector Calculus II for Integrated Science (Year 2014)