PhD Student at UT Austin | @NSF Graduate Research Fellow
Building deep learning methods to interpret medical images
https://www.gholste.me/
We develop AI methods for science, particularly deep learning methods based on data geometry, topology and dynamics systems.
Human-centered AI #HCAI, NLP & ML. Director TRAILS (Trustworthy AI in Law & Society) and AIM (AI Interdisciplinary Institute at Maryland). Formerly Microsoft Research NYC. Fun: 🧗🧑🍳🧘⛷️🏕️. he/him.
Machine Learning researcher, physicist, ex-Flagship Pioneering, ex-Broad Institute.
Associate Professor of Computer Science @Purdue, interested in graphs, invariances, causality, and OOD robustness in ML
Apple ML Research in Barcelona, prev OxCSML InfAtEd, part of MLinPL & polonium_org 🇵🇱, sometimes funny
machine learning asst prof at @cs.ubc.ca and amii
statistical testing, kernels, learning theory, graphs, active learning…
she/her, 🏳️⚧️, @queerinai.com
Asst prof at Duke University. Co-founder at LayerHealth.
ML and NLP for healthcare.
PhD MIT, BS/MS Stanford. She/her.
Assistant Professor at Stanford Statistics and Stanford Data Science | Previously postdoc at UW Institute for Protein Design and Columbia. PhD from MIT.
CS PhD Student at NYU, previously @MetaAI. Trying to make ML more reliable, predictable, and representative.
International Conference on Learning Representations https://iclr.cc/
Principal Scientist (Director) at Google DeepMind in Japan. 波瀬小⇒一志中⇒鈴鹿高専⇒名工大 (IBM T.J. Watson Research intern)⇒東芝欧州研究所⇒Google (Speech🇬🇧⇒Brain🇯🇵) ⇒Google DeepMind. 3rd generation Korean in Japan.
Unofficial bot by @vele.bsky.social w/ http://github.com/so-okada/bXiv https://arxiv.org/list/cs.LG/new
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Senior Energy and Climate Data Analyst @emberenergy.bsky.social
Finding, curating and explaining the data behind the global energy⚡️transition.
Anti-cynic. Towards a weirder future. Reinforcement Learning, Autonomous Vehicles, transportation systems, the works. Asst. Prof at NYU
https://emerge-lab.github.io
https://www.admonymous.co/eugenevinitsky
Conversational AI | NLP | Headed by Dr. Dilek Hakkani-Tur and Dr. Gokhan Tur | UIUC | IllinoisCDS
Applied Mathematician learning how to make things learn. Tell me about your Probabilistic Model Based Reinforcement Learning! I have two amazing labradoodles and an even more amazing human family!
Statistics, machine learning, numerics, and simulation at EURECOM.
CS @ Stanford | abhinavchinta.com