Championing Open-source DEvelopment in Machine Learning

Sara Hooker is a renowned ML researcher and leader in AI fairness and interpretability, and currently the VP of research at Cohere. She previously was a research scientist at Google Brain, focusing on training models that are not only accurate, but also interpretable, fair, and robust. Sara is the founder and a current advisor of Delta Analytics, a nonprofit organization dedicated to bringing data science expertise to underserved communities. She uses her expertise and outreach to advocate for trustworthy, accessible and equitable ML practices and to promote open research and collaboration.

Tri Dao is a chief scientist at Together.AI and an incoming assistant professor at Princeton University. He recently completed his Ph.D. at Stanford University working with Christopher Re and Stefano Ermon. Tri is a leading expert in machine learning and systems, with a focus on efficient training and long-range context. He has made significant contributions to the development of open-source tools and frameworks, including Mamba and FlashAttention.

Stella Biderman is a researcher at Booz Allen Hamilton & executive director at EleutherAI who specializes in natural language processing, ML interpretability, and AI ethics. She has contributed to the release of several open-source generative models such as GPT-NeoX, BLOOM, VQGAN-CLIP, and OpenFold. Her current research focuses on mechanistic interpretability research and the learning dynamics of large language models. Stella is currently a lead contributor to the Pythia project for transformer interpretability.

Matt Johnson is a senior research scientist at Google, where he works on the development of open-source tools and frameworks for machine learning. He has made numerous contributions to numerical libraries used in machine learning. Matt was a founder and lead contributor of Autograd, a precursor to the widely popular JAX library, and now is a key contributor to JAX.

Evan Shelhamer is an incoming assistant professor of computer science at UBC, a faculty member at the Vector Institute, and a senior research scientist at Google DeepMind. He has over ten years' experience in research and development for computer vision and machine learning and is an advocate for DIY science and open-source. Most notably, Evan served as the lead developer of the Caffe deep learning framework from version 0.1 to 1.0.

Chris Rackauckas is the Director of Modeling and Simulation at Julia Computing, the lead developer of the SciML Open Source Software Organization, Co-PI of the Julia Lab at MIT and Director of Scientific Research at Pumas-AI. He is the lead developer of several major open-source packages within the Julia ecosystem, perhaps most notably DifferentialEquations.jl. Chris's research focuses on scientific machine learning, which aims to integrate domain-specific scientific models with data-driven approaches from machine learning to accelerate simulations.