mahmohajer

Refactored the core numerical execution layer of the Orion Framework (ONNX equivalent in Cairo), replacing custom fixed-point signed integers with native Cairo signed integers to optimize math representation. Implemented the ReduceProd ONNX mathematical operator, enabling provable multi-dimensional tensor reductions inside zero-knowledge proof (ZKP) systems. Contributed 84 commits, editing over 120,000 lines of code (+60,473 / -62,115) to enhance model compilation speed and numerical precision correctness. Authored comprehensive test suites in Python and Cairo to verify operator equivalence against PyTorch and ONNX reference implementations. Built a Giza-Agents SDK Movie Recommendation System, demonstrating verifiable agent-based ML inference workflows.

Designed privacy-preserving Lasso and Penalized Linear Regression pipelines executing inference over homomorphically encrypted datasets via Fully Homomorphic Encryption (FHE). Implemented integer-based quantization pipelines (4-bit and 8-bit configurations) using Concrete-ML to compile floating-point model weights into FHE-compatible tables. Developed an end-to-end developer tutorial demonstrating encrypted ML workflows, validating FHE model accuracy against Scikit-Learn baselines.

Designed unsupervised clustering pipelines (K-Means) to identify Sybil (fake) accounts attempting to exploit the Gitcoin Grants quadratic funding protocol. Engineered behavioral features based on voting distributions and total USDT funding amounts, segmenting user contributions into poor, middle-class, and monopoly profiles to flag anomalous coordinated fraud campaigns. Performed exploratory data analysis (EDA) on transaction graphs, mapping anomalous donor groups to prevent Sybil attacks.