helix-ai
Curating bioinformatics and life-science coding projects.
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Curating bioinformatics and life-science coding projects.
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Shared-pool Mahjong: second-player win supported by a Lean-kernel implication and complete native checks of 778.5B SAFE orbits. Three English reports and bounded component proofs; latest paper is a working draft. End-to-end opening kernel proof and independent-machine replication remain open.
Interactive recipes for vector mathematics, statistics, and ML models in Swift
A Machine-Verified Constructive Proof of Lemoine's Conjecture.
Certificate, proof and two independent Arb checkers for C82 < 0.25360615 (essential minimum of the Zhang-Zagier height, Tao's optimization-constants table 82a)
High-scale Federated Learning (Goal**10M nodes) with formal BFT verification. Features 10ms zk-SNARKs and a 2026 PQC Overhaul: enforcing x25519-mlkem768 hybrid KEX, XMSS TPM-attestation, and epoch-based quantum-resistant ledger migration. Includes Go node agent + Python SDK v2.
SBML differential equation and chemical reaction model (Gillespie simulations) for Julia's SciML ModelingToolkit
Gradescope Lean autograder for discrete math classes
My journey through competitive programming with solutions to problems from platforms like CodeWars, LeetCode, CodeChef, and CodeForces, highlighting problem-solving and algorithmic skills.
Grand Challenge Labs mathematics governance, standards, schemas, and domain registry
BitBIRCH-Lean, a memory-efficient implementation of BitBIRCH designed for high-throughput clustering of huge molecular libraries
An AI-first formal mathematics library for Lean 4 agentic theorem provers.
Reproducible refinement proof for unmodified PQClean ML-DSA reference C (reduce.c + forward NTT): fixed-width C -> Cryptol -> Isabelle -> FIPS 204, via a SAW + Cryptol + Isabelle pipeline.
An open-source, clean-room reimplementation of Adobe Acrobat built in pure Rust
Source for the community blog
A computational experiment in probability, randomness, and convergence
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
Lean 4 formalization of identity regimes for Structural Explainability, defining six canonical regimes, nine derived profiles, and transformation-based identity classification.
Research Direction Selector: automated assistance to human research through evidence-driven experiment decisions, execution checks, Advisor, memory and Lean4 collaboration.