Seven disciplines. One practice of careful work.

A new paradigm for neural architecture — Reverse Synthetic Neural Networks that construct models directly from data patterns, without gradient descent, backpropagation, or GPU training. From zero-training synthesis to interpretable, efficient intelligence.

Hardware and algorithms in which randomness is not noise to suppress but the substrate itself — for ultra-low-power inference and neuromorphic systems.

Market microstructure, microdynamics, and the theory of financial markets as programmable computational systems — for asset management, risk inference, and the science of economic computation.

Cryptographic provenance for intellectual property of any kind. On-chain timestamping today only proves that one exact file existed at a moment in time — it breaks the instant a work is paraphrased, reformatted, translated, or remixed by a model, and it forces you to reveal the work in order to register it. We work on the unsolved version: provenance that is invariant to semantic-preserving transformation, and that can be proven in zero knowledge — letting a creator register a work and later establish priority or trace derivation without ever revealing the work itself. It is the research substrate beneath ठप्पा (Ṭhappā).

Statistical inference over sparse, noisy, high-dimensional observations of the universe — and, at the lab's leading edge, a single standard of evidence for exoplanet atmospheres. We build machine-accelerated Bayesian retrieval that turns the public JWST archive into reproducible, preregistered verdicts on contested biosignature claims. It is the research substrate beneath व्योम (Vyom).

Modeling the machinery of living systems at scale — protein dynamics, regulatory networks, and the interpretable biology of disease.

Algorithms and error-mitigation techniques for the NISQ era, and the theoretical frontier of what quantum advantage actually means outside cryptography.