An ever-expanding collection of papers and synthesized notes at the intersection of AI, Biology, and Mathematics. This repository uses an automated sync system to pull high-signal resources directly from my personal Notion database, so create an issue for any problems or to request a resource to be added.
The core "how" of both biological and artificial reasoning.
information-theoryThe mathematical basis for neural networks and genetic coding.
stochastic-processesModeling randomness in Markov chains, protein dynamics, and cellular trajectories.
optimization-theoryThe engine behind AI training and evolutionary biological selection.
causalityCausal graphs, AI robustness, and gene regulatory networks.
Turning complex, messy data into computable "maps" of the world.
embeddingsRepresenting words, LLMs, and chemical compounds as vectors in latent space.
geometric-dlGraph neural networks for protein structures and medical knowledge graphs.
multimodal-integrationCombining disparate data types like genomic data and clinical EHR notes.
interpretabilityMechanistic understanding of deep learning models and cellular pathways.
Modeling change over time, from reasoning paths to life cycles.
sequential-decision-makingReinforcement learning for AI agents and organoid protocol optimization.
generative-designAI-driven "hallucinations" of protein structures or synthetic gene circuits.
trajectory-inferenceMapping the "life story" of a cell or the reasoning path of a model.
perturbation-predictionActive learning to predict how systems react to external triggers.
The implementation of theory into clinical and laboratory practice.
knowledge-synthesisUsing EHRs and knowledge graphs to bridge bench science and clinical care.
structural-omicsSpatial transcriptomics and the physical architecture of protein design.
automated-discoveryUsing AI agents to design and run experiments (Active Learning).
robustness-safetyPredictable cellular reprogramming and counterfactual-proof medical AI.
- Publication & news feed
- Favorite genes (and why!)