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Jo2234/README.md

Johan Vaz

I build AI systems for financial research and risk — the unglamorous machinery that decides whether an AI tool is trustworthy or just fluent: eval harnesses, cited retrieval over SEC filings, and risk analytics that explain themselves.

Computer Science + Quantitative Finance at NUS. Based in Singapore.

Site: johan-vaz-site.vercel.app — several projects below are deployed and clickable, not just readable.

Currently: looking for internships or junior roles in AI agents, LLM evaluation, fintech, or quant research tooling — ideally on a small team that ships.

Selected work

Project What it is
AI Equity Research Copilot Document-grounded research over SEC filings: EDGAR ingestion, chunk-level cited retrieval, structured memos — plus a finance QA eval set so citation precision, refusals, and hallucination risk are measured, not assumed.
Financial LLM Eval Harness 50-case evaluation suite for financial QA systems: factual extraction, multi-document synthesis, refusal behavior, adversarial prompts, scoring, and regression reports.
Curio Learning-by-teaching, instrumented: teach an AI novice by voice, reasoning agents map your claims against a curriculum, then the novice teaches it back using only what it learned from you.
FluentAI Agentic language tutor with adaptive lessons, evaluator/memory agents, real-time speech, and spaced repetition driven by actual conversation mistakes.
finance-labs A toolkit of small, offline, test-covered risk and market-structure diagnostics: margin cascades, ETF liquidity stress, option skew, factor crowding, covenant headroom, and more. Live results gallery →
US Market Regime Dashboard Macro/risk context dashboard with transparent regime rules, data-freshness checks, and exports. FastAPI + React. Live →
Portfolio Risk Copilot API-first portfolio risk: VaR, expected shortfall, correlations, concentration flags, stress tests, plain-English commentary. Live →

How I work

  • Grounded, or it doesn't ship. Generated claims trace to source chunks; when evidence is weak, the system refuses — and that behavior is tested like a feature.
  • Evaluated, not vibe-checked. I build the eval harness before trusting the output: golden sets, regression scoring, adversarial cases.
  • Honest about failure modes. Finance punishes overconfidence, so I document where tools break and what they must not do.

Contact

Pinned Loading

  1. ai-equity-research-copilot ai-equity-research-copilot Public

    Document-grounded equity research copilot with SEC filing ingestion, cited RAG, memos, comparisons, and evals

    Python

  2. financial-llm-eval-harness financial-llm-eval-harness Public

    Evaluation harness for financial QA systems with scoring, citations, refusal checks, and reports

    Python

  3. curio curio Public

    Voice-based learning-by-teaching lab: teach an AI novice, reasoning agents map your claims against a curriculum, then the novice teaches it back

    TypeScript

  4. FluentAI FluentAI Public

    Agentic language tutor with adaptive lessons, evaluator/memory agents, and desktop/web demos

    Python

  5. finance-labs finance-labs Public

    Systematic toolkit of small, offline, test-covered risk and market-structure diagnostics — margin cascades, ETF liquidity stress, option skew, factor crowding, covenant headroom, and more

    Python

  6. us-market-regime-dashboard us-market-regime-dashboard Public

    US market regime dashboard with market data, volatility, yield curve signals, freshness checks, and exports

    Python