Skip to Content

Our research focuses on creating and commercializing high-precision, affordable and scalable machine learning technologies and solutions across two key areas.

image description
Clinical AI
image description
Drug Discovery

Research Projects

Faster Machine Unlearning via Natural Gradient Descent
2024
MicrobioRaman: an open-access web repository for microbiological Raman spectroscopy data
2024
Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics
2024
Prediction-powered Generalization of Causal Inferences
2024
Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
2024
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
2024
Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization
2024
Measuring Stochastic Data Complexity with Boltzmann Influence Functions
2024
Learning Optimal Projection for Forecast Reconciliation of Hierarchical Time Series
2024
Causal Discovery with Fewer Conditional Independence Tests
2024
Overcoming the Optimizer’s Curse: Obtaining Realistic Prescriptions from Neural Networks
2024
Implicit Representations via Operator Learning
2024
Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design
2024
Position: Application-Driven Innovation in Machine Learning
2024
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
2024
CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes
2024
Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design”
2024
Improving protein optimization with smoothed fitness landscapes
2024
Load More
image description