Production Deployments
Safety-Critical ML Workflows
Self-Driving Company- Shipped safety-critical vehicle behavior improvements across architecture, data pipelines, and eval loops
- Hardened release workflows with automated regression testing and pre-merge checks
- Standardized test suites and metric gates cut release cycle time by more than 5×
5× faster iteration and release analysis
Agentic Document Processing
AI Startup- Memory-empowered AI agent achieved significantly higher accuracy vs. context-window-only baseline
- Line-by-line editing agent dramatically reduced token costs in production
- High-speed PDF-to-HTML pipeline via intelligent parallel processing
Dramatically lower costs, significantly higher accuracy
Semantic Search for Investment Research
Investment Firm- Built RAG-based vector database identifying relevant alternative datasets by investment thesis
- Intuitive natural language-based interface let non-technical analysts self-serve research queries
- Migrated legacy data into modern, searchable infrastructure
Analysts retrieve insights in seconds instead of hours
AI Model Training Platform
AI Startup- Kubernetes-orchestrated fine-tuning platform with on-demand AWS GPU provisioning
- Auto-scaling infrastructure reduced compute costs while maintaining throughput
- Live inference engine enabled real-time model evaluation during development
Cost-efficient, on-demand GPU training at scale
AI-Powered Chip Design
Chip Startup- AI-powered chip design and verification tool accelerated the development cycle
- Microfluidic cooling system design for high-performance chips
- Comprehensive semiconductor supply chain and unit economics analysis
Accelerated design cycle with AI verification tooling
Research
Economic Evaluation of LLMs
When the real-world cost of an AI mistake exceeds pennies, it's almost always more economical to deploy the most powerful model.
Available as arXiv Preprint
Rational Tuning of LLM Cascades via Probabilistic Modeling
When chaining multiple AI models together, modeling how their errors interact yields better cost-accuracy trade-offs than empirical tuning—especially when training data is scarce.
Published in Transactions on Machine Learning Research
Natural-Language Based Synthetic Data Generation for Cluster Analysis
Summarizing a dataset's overall geometry as a few high-level parameters makes it possible to define evaluation scenarios in natural language, making clustering benchmarks easier to set up, more interpretable, and more reproducible.
Published in Journal of Classification
Advisory
Enterprise AI Enablement
Education Company- Designed and delivered a structured AI curriculum taking cross-functional teams from fundamentals to hands-on deployment in 6 weeks
- Project-based sprints enabled teams to build and validate AI prototypes against real business workflows
- Curriculum tailored to domain-specific use cases including document analysis, research workflows, and decision support
125+ professionals trained, measurable acceleration in AI adoption
AI Hardware Due Diligence
Venture Capital Firm- Deep evaluation of AI accelerator specifications and performance for investment due diligence
- Infrastructure assessment for GPU provisioning and high-performance computing workloads
- Technical review of ASIC and specialized accelerator development cycles
Identified and analyzed key technical risks for VC investments