Laure Delisle

I am a PhD student in Computing and Mathematical Sciences at Caltech, advised by Pietro Perona and funded by a Kortschak scholarship.

My research interests lie at the intersection of Machine Learning and Computer Vision, with humanitarian and ecology applications. I am particularly interested in developing new techniques and methods to enable non-profits and NGOs and further their efforts.

Previously, I was a research engineer at Element AI, where I helped NGOs to tackle human rights violations with Deep Learning (pro-bono). There, I've worked on quantifying the scale and intersectionality of verbal abuse against women and detecting human presence in Darfur and document the Darfur Genocide with Amnesty International. I've also helped Hope Not Hate analyzing fascist groups social media activity during the latest European election. From a research perspective, I collaborated closely with Thomas Boquet to study more closely reproducibility in Machine Learning.

I graduated with a Master of Data Science from Illinois Institute of Technology where I was advised by Shlomo Argamon. I also graduated with an EECS MSc and Bachelors at Ecole Superieure d'Informatique, Electronique, Automatique in Paris, advised by Robert Erra and Vincent Guyot.

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I'm interested in computer vision, machine learning, scarce labeled data regime, and AI for Good.
I also care about ML related issues: reproducibility and stability, datasets used for benchmarking. See my slides on ImpactNet!

✔ Experimental design methodology, based on linear mixed models, to study and separate the effects of multiple factors of variation in ML experiments

✔ Methodology for testing for statistical differences in model performances under several replications (MAML, Prototypical Networks, TADAM).

✔ First large-scale study on online abuse against women, in partnership with Amnesty International.