Now
Last updated: February 2026 · Mumbai, India
Research
Training Spekron, a spectral foundation model that learns transferable molecular representations from IR and Raman spectra. Currently pretraining on QM9S (130K computed spectra) using a CNN + D-LinOSS hybrid architecture on 2× RTX 5060 Ti. Writing up results for the companion paper.
Finalizing the spectral identifiability paper — formal proofs that combined IR + Raman uniquely determines molecular structure for 99.9% of organic molecules.
Building
- SpectraKit — stable at v1.8.0, 699 tests passing. Exploring wavenumber-aware Whittaker smoothing for the next release.
- ReactorTwin — hybrid neural ODE digital twin for chemical reactors. Building the online adaptation loop.
- VibeScope — 3D molecular vibration visualizer. Next.js + React Three Fiber + QM9S data.
Learning
- State space models — D-LinOSS, Mamba-2, and how selective state spaces handle sharp spectral features differently from attention.
- Optimal transport for domain adaptation — Sinkhorn divergences for calibration transfer between spectrometers.
- Conformal prediction — uncertainty quantification for spectral retrieval without distributional assumptions.
Reading
- Gu & Dao (2024) — Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- Peyré & Cuturi (2019) — Computational Optimal Transport
- Angelopoulos & Bates (2023) — Conformal Prediction: A Gentle Introduction
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