I am interested in designing high-performance machine learning methods that make sense to humans.
Quanta magazine described well why I am doing what I am doing. Thank you John Pavlus for writing this piece!
My focus is building interpretability method for already-trained models or building inherently interpretable models . In particular, I believe
the language of explanations should include higher-level, human-friendly concepts so that it can make sense to everyone .
I gave a talk at the G20 meeting in Argentina in 2019. One of my work TCAV received UNESCO Netexplo award 2019, was featured at Google I/O 19' and in Brian Christian's book on The Alignment Problem. I gave keynote at ECML 2020.
Stuff I help with:
Workshop Chair at ICLR 2018
Area chair NIPS 2017, NeurIPS 2018, 2019, 2020, ICML 2019, 2020, ICLR 2020, AISTATS 2020
Senior program committee at AISTATS 2019
Steering committee and area chair at FAT* conference
Program committee at ICML 2017/2018, AAAI 2017, IJCAI 2016 (and many other conference before that...)
Executive board member of Women in Machine Learning.
Co-organizer 3rd ICML 2018 Worshop on Human Interpretability in Machine Learning (WHI), 1st ICML 2016 Worshop on Human Interpretability in Machine Learning (WHI),
2nd ICML 2017 Worshop on Human Interpretability in Machine Learning (WHI).
and NIPS 2016 Worshop on Interpretable Machine Learning for Complex Systems.
I gave a couple of tutorials on interpretability:
Deep Learning Summer school at University of Toronto, Vector institute in 2018 (slides, video)
CVPR 2018 (slides and videos)
Tutorial on Interpretable machine learning at ICML 2017 (slides, video).
Google Scholar
