Reyhane Askari Hemmat

I'm a PostDoc researcher at FAIR labs, working with Adriana Romero Soriano and Michal Drodzal. I received my PhD in computer science from Mila at University of Montreal. I worked under the supervision of Ioannis Mitliagkas and Nicolas Le Roux. Prior to my PhD, I received my Masters in Computer Science and Bachelor in Computer Engineering from Université de Montréal and Amirkabir University of Technology (Tehran Polytechnic), respectively.

My research interests are in the intersection of Generative Models and Synthetic Data.

I am the winner of Borealis AI Graduate Fellowship, the FRQNT three years 'Bourse de doctorat en recherche' and the NSERC's three years Postgraduate PhD Scholarship (PGS-D).

I co-organized the Bridging Game Theory and Deep Learning workshop at NeurIPS 2019, Mila's Deep Learning Theory Group and MTL MLOpt.

Twitter / GitHub / Google Scholar

Projects

	Improving the Scaling Laws of Synthetic Data with Deliberate Practice Reyhane Askari-Hemmat, Mohammad Pezeshki, Elvis Dohmatob, Florian Bordes, Pietro Astolfi, Melissa Hall, Jakob Verbeek, Michal Drozdzal, Adriana Romero-Soriano
	EvalGIM: A library for Evaluating Generative Image Models Melissa Hall, Oscar Mañas, Reyhane Askari-Hemmat, Mark Ibrahim, Candace Ross, Pietro Astolfi, Tariq Berrada Ifriqi, Marton Havasi, Yohann Benchetrit, Matthew Muckley, Karen Ullrich, Mike Rabbat, Brian Karrer, Michal Drozdzal, Jakob Verbeek, Adriana Romero-Soriano
	Multi-Modal Language Models as Text-to-Image Model Evaluators Jiahui Chen, Candace Ross, Reyhane Askari-Hemmat, Koustuv Sinha, Melissa Hall, Michal Drozdzal, Adriana Romero-Soriano[under review]
	On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models Tariq Berrada, Pietro Astolfi, Marton Havasi, Matthew J. Muckley, Melissa Hall, Reyhane Askari-Hemmat, Yohann Benchetrit, Karteek Alahari, Michal Drozdzal, Adriana Romero-Soriano, Jakob Verbeek, Neurips 2024
	Diffusion-Based In-painting of Corrupted Spectrogram Mahsa Massoud, Reyhane Askari-Hemmat, Adrian Liu, Siamak Ravanbakhsh, Neurips 2024 workshop on ML for Physical Sciences
	An Introduction to Vision-Language Modeling F. Bordes, R. Pang, A. Ajay, A. Li, A. Bardes, S. Petryk, O. Mañas, Z. Lin, A. Mahmoud, B. Jayaraman, M. Ibrahim, M. Hall, Y. Xiong, J. Lebensold, C. Ross, S. Jayakumar, C. Guo, D. Bouchacourt, H. Al-Tahan, K. Padthe, V. Sharma, H. Xu, X. Ellen Tan, M. Richards, S. Lavoie, P. Astolfi, R. Askari-Hemmat, J. Chen, K. Tirumala, R. Assouel, M. Moayeri, A. Talattof, K. Chaudhuri, Z. Liu, X. Chen, Q. Garrido, K. Ullrich, A. Agrawal, K. Saenko, A. Celikyilmaz, V. Chandra
	Improving Geo-diversity of Generated Images with Contextualized Vendi Score Reyhane Askari Hemmat, Melissa Hall, Alicia Sun, Candace Ross, Michal Drozdzal, Adriana Romero Soriano ECCV 2024
	Feedback-guided Data Synthesis for Imbalanced Classification Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, Adriana Romero-Soriano TMLR 2024
	LEAD: Least-Action Dynamics for Min-Max Optimization Reyhane Askari Hemmat, Amartya Mitra, Guillaume Lajoie, Ioannis Mitliagkas, TMLR 2023 featured paper (oral equivalent - Presented at ICLR 2024)
	Negative Momentum for Improved Game Dynamics Gauthier Gidel, Reyhane Askari Hemmat, Mohammad Pezeshki, Gabriel Huang, Remi Lepriol, Simon Lacoste-Julien, Ioannis Mitliagkas
	Oríon: Experiment Version Control for Efficient Hyperparameter Optimization Christos Tsirigotis, Xavier Bouthillier, François Corneau-Tremblay, Peter Henderson, Reyhane Askari Hemmat, Samuel Lavoie-Marchildon, Tristan Deleu, Dendi Suhubdy, Michael Noukhovitch, Frédéric Bastien, Pascal Lamblin, AutoML workshop at ICML
	Auto Encoders in PyTorch A quick implementation of Auto Encoder, Denoising Auto Encoders and Variational Auto Encoders in PyTorch.

This was cool :)