Scientific Computing & HPC
Massively parallel workflows for multi-terabyte datasets. SLURM orchestration, C++ optimization, HDF5/Parquet pipelines.
Vishal Sood
Senior Research Engineer
Building computational systems that accelerate scientific discovery
15+ years | PhD Statistical Physics | 11 publications | 5 major platforms
About
I am a physicist turned engineer. After a PhD in Statistical Physics at Boston University — studying voter models, random walks, and evolutionary dynamics on complex networks — I spent 15 years building computational systems that accelerate scientific discovery.
At the Blue Brain Project (EPFL), I designed frameworks for brain circuit validation, scientific data management, and HPC workflow orchestration — serving a team of 100+ neuroscientists working with 4.2 million neurons and 14 billion synapses. At Saphetor SA, I built high-performance C++ backends for clinical-grade genomic variant annotation. At Citiviz, I developed geospatial analytics pipelines for urban planning.
The thread connecting these domains is the transferability of computational methods: the same algorithmic thinking that models opinion dynamics on networks also designs validation frameworks for brain circuits, variant classification systems for cancer diagnostics, and data pipelines for satellite imagery. Each transition was driven not by restlessness but by the recognition that first-principles methods travel well.
Most recently, I have been building with LLMs — not as a consumer of AI tools, but as an architect of systems that use them. I designed a framework for human-AI collaboration grounded in statistical physics, built a personal research environment where AI agents help reconstruct digital twins of natural systems, and deployed conversational AI interfaces (including the one on this site) with streaming, session management, and cost control. The physicist's instinct — model the system, understand its dynamics, then engineer it — turns out to apply directly to LLMs.
I am based in Bussigny, Switzerland (Permit C) and looking for my next role in scientific computing, AI/ML infrastructure, quantitative engineering, or computational biology.
Areas of Expertise
Algorithms & Statistical Modeling
Novel algorithms for pattern detection in noisy high-dimensional data. Monte Carlo, Bayesian methods, network analysis.
Bioinformatics & Genomics
Clinical-grade genomic pipelines, variant annotation, ACMG classification. Integration of ClinVar, gnomAD databases.
Software Architecture & APIs
Plugin-based extensible frameworks, advanced Python metaprogramming, modern C++ with functional paradigms.
AI & Collaborative Intelligence
LLM-powered applications with streaming APIs, system prompt engineering grounded in statistical physics, multi-agent architectures, and human-AI co-ownership methodology.