London, UK / tech + quantitative research
Javal Vyas
ML / Optimization Researcher - Reliable Decision Systems
I build research-grade software for constrained decision-making: optimization models, ML systems, knowledge-grounded agents, and validation loops. My work sits between tech and quantitative research: turning complex system data into reliable, testable decisions.
Build
Research systems that run
Python packages, reproducible experiments, and deployable evaluation workflows
Model
Optimization + ML under constraints
Scheduling, unit commitment, surrogate models, and process-system decisions
Validate
Reliable actions before deployment
Simulation checks, deterministic validators, and failure-mode analysis
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Selected Work
A curated set of research systems and open-source projects across ML, optimization, validation, and process intelligence.
Agentic decision framework that turns fault signals into constraint-aware recovery plans, then checks candidate actions through simulation and deterministic validators.
- Multi-agent monitoring, planning, action synthesis, simulation, and reprompting
- Benchmarks action reliability under process constraints before execution
Multimodal language-model workflow for extracting equipment tags and reconstructing process topology from P&ID drawings.
- Separates visual extraction from topology reasoning
- Targets scalable, semantically reliable P&ID digitization
Python package for solving resource-task-network scheduling problems with Pyomo, including experiment and visualization utilities.
- Resource-task-network inputs
- Gantt, resource-level, and network visualizations
Optimization models and decomposition algorithms for meeting electricity demand at minimum cost under combinatorial commitment constraints.
- Decomposition method paired with EGRET unit commitment models
- Benchmarked across four power-system cases
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Selected Research
Key papers and preprints. The full list remains available on Google Scholar.
- 2026 Automating Cause-Effect Specification with Knowledge Graphs and Large Language ModelspreprintarXiv / preprint
- 2026 preprint
- 2026 From P&ID Drawings to Process Graphs: A Multimodal Language Model Approachjournal articleSystems and Control Transactions / journal article
- 2025 Optimization models and algorithms for the Unit Commitment problemjournal articleSystems and Control Transactions / journal article
- 2024 Integration of Plant Scheduling Feasibility with Supply Chain Networks Under Disruptions Using Machine Learning SurrogatesconferenceESCAPE / conference
current positions
- Reliable agentic controlDecision agents that move from fault detection to safe action through context retrieval, validators, and simulation.
- Optimization-aware MLSurrogate models and structured features that improve feasibility, scheduling, and operational decision support.
- Structured process intelligenceMultimodal extraction of equipment, topology, and operating semantics from engineering artifacts.
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Notes
Public writing and maintained profiles for research context.
Writing on reliability, validation, and decision-time controls for AI systems.
Complete publication list, including papers not shown in this curated site view.
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Stack
A compact toolchain for quantitative modelling and reliable decision systems.
Research Engineering
Optimization
ML Systems
Agents / Knowledge
Systems Domain
LANG
Python, TypeScript
OPT
Pyomo, MILP/MINLP, decomposition
ML
Evaluation, uncertainty, surrogates
SYS
Git, Docker, CI/CD, Linux
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Contact
For quantitative ML, optimization, controls, and decision-system conversations.
contact route
javalvyas2000@gmail.com