Focus Areas

Work centered on making AI actually useful in everyday life. No buzzwords — just disciplined engineering and clear results.

Model Debugging & Evals

  • Define failure modes and success metrics
  • Curate adversarial datasets and red-team prompts
  • Benchmark reliably and close the loop

Training & Fine‑Tuning

  • From-scratch training for small, task-specific models
  • LoRA/QLoRA and instruction-tuning for targeted gains
  • Data recipes and token budgets that fit reality

Applied Research & Prototyping

  • Explore ideas quickly; validate with minimal systems
  • Ablations that separate signal from noise
  • Clear docs, reproducible results

Productization & Deployment

  • Reliability, cost, and latency tradeoffs by design
  • Edge, web, or local-first deployments
  • Observability, safety rails, and rollback plans