Optical computing
Analog optical computer for AI inference
A compute architecture that combines optics and analog computation to improve efficiency for AI inference and certain optimization problems, alongside model designs that fit the machine.
Language models
Implicit language models are RNNs
Work on implicit state-space models that recover non-linear state transitions with more parallelizable training, scaled to reasoning tasks and large language-model pretraining.
Stochastic physics
Faster-than-free-diffusion reaction rates
Experimental work on tuned barrier shapes showing regimes where reaction rates can increase rather than decrease with barrier height, against the usual Arrhenius intuition.
More projects
Holographic optical storage for the cloud
Exploring rewritable holographic storage as an alternative to hard disk drives, combining material experiments, workload-aware system design, and machine learning for data recovery.
Inverse digital holography using conditional generative models
Conditional generative modeling for phase retrieval in digital holography, aimed at finding holograms that produce small laser patterns with high accuracy.
Measuring stochastic path probabilities
A protocol for extracting relative path probabilities from measured particle trajectories, making stochastic actions and the most probable paths across a barrier experimentally accessible.
Detecting hidden molecular intermediates
Using first-passage-time statistics to reveal intermediate states in molecular processes and connect measured transition dynamics to the underlying energy landscape.
Symmetry breaking of transition-path times
Experimental and theoretical work on when fundamental inversion symmetries in first-passage and transition-path dynamics hold, and how they break down on mesoscopic and molecular scales.
Broken detailed balance in living and active systems
A line of work on non-equilibrium dynamics in biological and active-matter systems, including broken detailed balance in living systems and active filament networks.