Jasmine Collins

Currently I am a Research Scientist at Databricks.

Before that, I had the pleasure of doing my PhD student at UC Berkeley with Jitendra Malik, funded by the NSF GRFP. I have done insternships at Meta and Amazon Science, and was part of the inaugural group of AI Residents at Google Brain.

email / google scholar / twitter / github

Research

My research has primarily focused on computer vision and cognitive science. In general, I’m interested in making machine learning model outputs more aligned with human preferences and behavior. I'm also interested in problems such as learning with fewer human-labeled annotations, improving sample efficiency, and building more robust models.

Computer Vision & Deep Learning

	CA²T-Net: Category-Agnostic 3D Articulation Transfer from Single Image Jasmine Collins, Anqi Liang, Jitendra Malik, Hao Zhang, Frederic Devernay arXiv, 2023 paper
	GANmouflage: 3D Object Nondetection with Texture Fields Rui Guo, Jasmine Collins, Oscar de Lima, Andrew Owens Computer Vision and Pattern Recognition (CVPR), 2023 paper / project page
	ABO: Dataset and Benchmarks for Real-World 3D Object Understanding Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon Xu, Erhan Gundogdu, Xi Zhang, Thomas F. Yago Vicente, Thomas Dideriksen, Himanshu Arora, Matthieu Guillaumin, Jitendra Malik Computer Vision and Pattern Recognition (CVPR), 2022 paper / project page
	Accelerating Training of Deep Neural Networks with a Standardization Loss Jasmine Collins, Johannes Balle, Jonathon Shlens Women in Machine Learning Workshop, 2019 paper
	Capacity and Trainability in Recurrent Neural Networks Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo International Conference on Learning Representations (ICLR), 2017 paper / project page

Cognitive Science & Computational Neuroscience

	Three-Dimensional Object Completion in Humans and Computational Models Eunice Yiu, Jasmine Collins, Alison Gopnik Cognitive Science Society (CogSci), 2022 (Oral Presentation) paper
	Learning Causal Overhypotheses through Exploration in Children and Computational Models Eliza Kosoy, Adrian Liu, Jasmine Collins, David M Chan, Jessica B. Hamrick, Nan Rosemary Ke, Sandy Huang, Bryanna Kaufmann, John Canny, Alison Gopnik Conference on Causal Learning and Reasoning (CLeaR), 2022 (Oral Presentation) paper
	Exploring Exploration: Comparing Children with RL Agents in Unified Environments Eliza Kosoy, Jasmine Collins, David M Chan, Sandy Huang, Deepak Pathak, Pulkit Agarwal, John Canny, Alison Gopnik, Jessica B. Hamrick International Conference on Learning Representations (ICLR) Workshop, 2020 (Oral Presentation) paper / blog post
	Automatically Inferring Task Context for Continual Learning Jasmine Collins, Kelvin Xu, Bruno Olshausen, Brian Cheung Cognitive Computational Neuroscience (CCN), 2019 (Oral Presentation) paper / talk (recording)
	Inferring Single-trial Neural Population Dynamics Using Sequential Auto-encoders Chethan Pandarinath, Daniel J O'Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D. Stavisky, Jonathan C. Kao, Eric M. Trautmann, Matthew T. Kaufman, Stephen I. Ryu, Leigh R. Hochberg, Jaimie M. Henderson, Krishna V. Shenoy, Larry F. Abbott, David Sussillo Nature Methods, 2018 paper / code

Teaching

Graduate Student Instructor, CS 188 Spring 2022

Graduate Student Instructor, CS 188 Spring 2020

Thanks to Jon Barron for this iconic website template.