math + software

SELECTED RESEARCH

Latest in Google Scholar

Shortcuts Come in Multiples Where Mitigating One Amplifies Others

a method for and study of how deep learning techniques cope with multiple shortcuts, CVPR 2023.

> paper + code

Zhiheng Li, Ivan Evtimov*, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, Mark Ibrahim*


ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations

we find surprisingly similar strengths and vulnerabilities across more than 2,200 deep learning models, ICLR Spotlight 2023.

> paper + website

Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim


The Robustness Limits of SoTa Vision Models to Natural Variation

even today's best models are brittle to common changes in pose, background, etc. TMLR 2023

> paper

Mark Ibrahim, Quentin Garrido, Ari Morcos, Diane Bouchacourt


Grounding Inductive Biases in Natural Images

invariance in deep learning stems from variations in data, NeurIPS 2021.

> paper + code

Diane Bouchacourt*, Mark Ibrahim*, Ari S. Morcos



Global Explanations for Neural Networks

Mapping the Landscape of Predictions, ACM AAAI 2019

> paper + open source library + blog post

Mark Ibrahim, Melissa Louie, Ceena Modarres, John Paisley (Columbia University)


A Cookbook of Self-Supervised Learning

co-author along with Randall Balestriero , Yann LeCun, and many others

Knowledge Network

a graph-based knowledge search engine powered by Wikipedia

connect ideas

Talks


Georgia Tech's Deep Learning Course Instructor (2022) (10k+ online students)
Lecture on "Feed Forward Neural Networks"

PyCon US 2020 (Python Conference)
Talk on "Machine Learning on Encrypted Data with CrypTen"

NeurIPS 2018 FEAP Workshop Spotlight Talk (Dec 2018)
"Towards Explainable Deep Learning for Credit Lending"

New York Python Meetup (Dec 2018)
Data Science Talk: " Explaining Deep Learning Models"

Applied Machine Learning Tom Tom Conference (April 2018)
"Explainable AI: Key Techniques and Societal Implications"

George Washington University, Data Driven Conference (Dec 2017)
"Understanding the Predictions of Deep Neural Networks"

NYC Data Wranglers Meetup (Aug 2016)
Data Science in Practice: "Building a Graph-Based Search Engine"


Courses Taught at the University of Vermont


Calculus I 71 eager minds,

Calculus II 38 étudiants, and

College Algebra 42 estudiantes.