Research
Programs spanning core ML, instrument engineering and clinical translation.
Medical imaging foundation models
Self-supervised pretraining on multi-modal clinical imaging (MRI, CT, pathology) and downstream models for triage, segmentation and report drafting.
Autonomous experimentation
Reinforcement-learning controllers for microscopy and spectroscopy that plan acquisition, adapt to samples and curate results.
Edge inference on instruments
Compact models that run inside microscopes, sequencers and bedside devices — low-latency, GDPR/PDPO-friendly.
Signal & sensor models
Time-series and spectral models for electrophysiology, mass spectrometry and Raman spectroscopy.
Instrument QA & drift
Statistical and learned monitors that flag calibration drift before it contaminates downstream analyses.