David Rolnick, McGill University

Research

machine learning deep learning theory expressivity generalization neural networks artificial intelligence biodiversity change climate change

Latest papers

Benchmarking the geographic generalization of deep learning models for precipitation downscaling.
Scientific reports · 2026 · senior author
A landmark environmental law looks ahead.
Science (New York, N.Y.) · 2023
Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks.
Entropy (Basel, Switzerland) · 2021 · senior author

All 3 publications

Latest funding

AI and Biodiversity Change (ABC)
NSERC · 2023 · Principal investigator
$1,312,350
ReCONstruct: Building Energy Retrofit Solutions for Canada / ReCONstruire: Solutions de rénovation énergétique des bâtiments pour le Canada
NSERC · 2021 · Co-investigator
$2,136,225
Mathematical Principles for Neural Network Design
NSERC · 2021 · Principal investigator
$12,500

All 5 grants and awards

3 publications.

01

Benchmarking the geographic generalization of deep learning models for precipitation downscaling.

Harder P, Schmidt L, Pelletier F, Ludwig N, Chantry M, Lessig C, Hernandez-Garcia A, Rolnick D

Scientific reports2026Senior author

DOI: 10.1038/s41598-025-34557-4 PMID: 41593162

02

A landmark environmental law looks ahead.

Fischman RL, Ruhl JB, Forester BR, Lama TM, Kardos M, Rojas GA, Robinson NA, Shirey PD, Lamberti GA, Ando AW, Palumbi S, Wara M, Schwartz MW, Williamson MA, Berger-Wolf T, Beery S, Rolnick D, Kitzes J, Thau D, Tuia D, Rubenstein D, Hickman CR, Thorstenson J, Kaebnick GE, Collins JP, Jayaram A, Deleuil T, Zhao Y

Science (New York, N.Y.)2023

DOI: 10.1126/science.adn3245 PMID: 38127744

03

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks.

Hillar C, Chan T, Taubman R, Rolnick D

Entropy (Basel, Switzerland)2021Senior author

DOI: 10.3390/e23111494 PMID: 34828192

AI and Biodiversity Change (ABC)

Principal investigatorNSERC2023Alliance Grants

Principal investigators: Rolnick, David DS

Keywords: artificial intelligence; biodiversity change; climate change; ecology; machine learning

$1,312,350

ReCONstruct: Building Energy Retrofit Solutions for Canada / ReCONstruire: Solutions de rénovation énergétique des bâtiments pour le Canada

Co-investigatorNSERC2021Alliance Grants

Principal investigators: Jemtrud, Michael M

Keywords: building energy retrofit; climate change adaptation; community resilience; decarbonization; design for manufacturing assembly; housing; life cycle analysis; low energy buildings; off-site prefabrication; sustainable design

$2,136,225

Mathematical Principles for Neural Network Design

Principal investigatorNSERC2021Discovery Launch Supplement

Principal investigators: Rolnick, David

$12,500

Mathematical Principles for Neural Network Design

Principal investigatorNSERC2021Discovery Grants Program - Individual

Principal investigators: Rolnick, David

Keywords: machine learning; neural networks; deep learning theory; expressivity; generalization

$116,000

Meeting 30 x 30 and the Paris Agreement: An inclusive framework leveraging digital solutions for effective nature-based solutions in Canada

Co-investigatorNSERC2021Alliance Grants

Principal investigators: Matthews, Damon HD

Keywords: biodiversity; carbon accounting; climate change; governance; Indigenous knowledge; machine learning; nature-based solutions; science policy; sustainability; water

$256,623

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Write to this professor

A short, specific email works best. This draft uses one of their recent papers; replace the parts in brackets with your own details before sending.

Applying for

Paper to mentionMention their NSERC grant from 2023

Subject: Prospective PhD student: machine learning Dear Professor Rolnick, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Benchmarking the geographic generalization of deep learning models for precipitation downscaling." (2026), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "AI and Biodiversity Change (ABC)" (2023), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]