London, UK
Javal Vyas
ML / Optimization Engineer building toward Quant Research
I build reliable, research-driven AI systems—agentic decision workflows, optimization pipelines, and evaluation methods. I'm especially interested in risk-aware deployment and how reliability techniques translate into quantitative decision-making.
Core
Reliable AI + Optimization
Evaluation discipline, robustness, constraints
Work
Agentic fault handling
From information → actions, with monitoring in mind
Track
Quant trajectory
Risk-aware modeling + decision-making
Selected Work
Engineering-forward projects with reliability and evaluation discipline.
GraphRAG-powered agentic fault handling for controlled operations. Structured context injection and safer action selection (paper under review).
Focus
Fault recovery
Mode
Graph + tools
- Context routing for decision-time retrieval
- Action-oriented agent design (not just Q&A)
- Research-grade framing with practical engineering
Study on passing operational information to LLM agents for correct, controllable actions.
Goal
Action correctness
Lens
Control + safety
- Information → action pipelines
- Failure modes captured for iteration
- Practical framing for operations
An open-source scheduling package focused on reproducible process scheduling workflows (first author).
Type
OSS package
Domain
Scheduling
- Clean interfaces for experiments and reuse
- Reproducible scheduling workflows
Optimization of scheduling problems with ML surrogate integration to improve efficiency and data use.
Angle
Surrogate modeling
Scope
Large OSS
- Neural surrogate integration for optimization
- Engineering contributions on a large open-source project
Signals and evaluation discipline framed as an engineering-first bridge into quant research.
Theme
Signals + eval
Goal
Quant bridge
- Focus on evaluation hygiene and avoiding false discoveries
- Clear experiment structure for iteration
Health metrics for LLM policies using validity/consistency checks and invalid transition suppression. Available on request.
- Metrics that map to actionable intervention
- Designed to reason about policy health, not just accuracy
Publications / Research
Selected work + what I’m currently building.
Selected publications
Full list on Google Scholar .
Work in progress
- When to use LLMs (and how to choose them)Reliability-guided framework for selecting LLMs when capabilities vary and failure costs differ.
- Risk controllability of LLMsHow to define/control risk in agentic LLM systems (evaluation → interventions → safer actions).
- Look forward, act nowDecision-time methods to combat latency by planning ahead and acting with partial future context.
Full publication list: Google Scholar .
Writing / Research
Public writing + research signals. Short, pragmatic, and measurable.
Writing on reliability, evaluation, and risk-aware deployment for AI systems.
GraphRAG + agentic fault handling for safer action selection in operations.
Skills
A compact stack tuned for shipping research-quality systems.
Core
AI/ML
Optimization
Quant direction
Contact
If you're building something technical in markets/data, I’m happy to chat.
Best way to reach me: [email protected]