Systems Architect | Rust Engineer | Ph.D. Mathematician

I specialise in High-Performance Computing (HPC) and ML Infrastructure. My focus is replacing GIL-bound Python bottlenecks with highly optimised Rust and C++ kernels, leveraging SIMD, zero-copy data transfer (Apache Arrow), and in-process OLAP engines (DuckDB).

LinkedIn • Crates.io

• Advanced Forecasting: Large-scale Hierarchical Time Series (HTS), Probabilistic Forecasting, and Handling Intermittent Demand (Sparse Data) for Supply Chains.
• Rigorous Statistics: Bayesian Inference (Stan), Design of Experiments (DoE), Functional Data Analysis (FDA).
• Industrial AI: Anomaly Detection in manufacturing processes, Predictive Quality, and Root Cause Analysis using Causal Inference.

• Systems Programming: Porting interpretability-heavy Python logic to Rust/C++ (WASM/Native).
• GenAI Infrastructure: Building Model Context Protocol (MCP) servers and dependency-free inference engines for Foundation Models.
• Data Engineering: Designing zero-copy ETL pipelines using DuckDB, Polars, and Apache Arrow.

• Architecture: Full re-implementation of the Chronos-2 time-series foundation model in pure Rust.
• Objective: Remove heavy PyTorch/Python dependencies for edge and high-throughput environments.
• Tech: Candle / Burn, WASM, Tokio.

• Performance: Achieved 2,900x speedup vs. statsmodels/pandas loops by moving logic to C++.
• Design: Hybrid architecture using DuckDB for parallelized data shuffling and Rust for vectorized statistical kernels.
• Tech: Rust, DuckDB C-API, OpenMP.

• Implementation: Custom Rust-based servers implementing the MCP standard to inject dynamic context (DB schemas, API specs) into AI coding agents.
• Tech: axum, serde, async-trait.

Core:

Data & ML:

Infrastructure: