4 citations · 7 across the 5 of their papers we have counts for
5 papers
Science sandboxes measure the scientific capability of AI agents
Arya S. Rao, Rodrigo I. Castro, Sager J. Gosai +8
Scientific progress depends not only on finding solutions, but on learning the rules that explain why they work and using that understanding to design better experiments. We introd…
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse folding
Yasha Ektefaie, Olivia Viessmann, Siddharth Narayanan +3
Protein inverse folding-that is, predicting an amino acid sequence that will fold into the desired 3D structure-is an important problem for structure-based protein design. Machine…
Empowering Biomedical Discovery with AI Agents
Shanghua Gao, Ada Fang, Yepeng Huang +6
We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomed…
Is Ignorance Bliss? The Role of Post Hoc Explanation Faithfulness and Alignment in Model Trust in Laypeople and Domain Experts
Tessa Han, Yasha Ektefaie, Maha Farhat +2
Post hoc explanations have emerged as a way to improve user trust in machine learning models by providing insight into model decision-making. However, explanations tend to be evalu…
Multimodal learning with graphs
Yasha Ektefaie, George Dasoulas, Ayush Noori +2
Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics.…