// About
A practitioner in AI and quantitative finance.
Two careers, one discipline.
For most of two decades I have worked where financial markets meet technology, across multi-asset and hedge funds, fintech, and now frontier AI. The thread through all of it is the same: turning quantitative ideas into systems that actually run.
On the buy side I ran institutional capital under real accountability: factor and risk models, derivatives, and portfolio construction informed by roughly $1.6 billion in assets. That work taught me how far a model can be trusted before it breaks, and what it costs when it does.
Today I build the AI systems that do that work at scale, the automation, the reasoning, and the evaluation a desk once ground out by hand. Most enterprise AI dies in the slide deck. The part that matters is the unglamorous engineering, data contracts, validation, monitoring, and governance, that makes a model trustworthy in production.
Teaching keeps the work grounded; it forces clean explanations and honest assumptions. As a professor and advisor in California, I have brought quantitative finance, machine learning, and applied AI to several thousand students and working professionals.
My book, Modern Analytics Engineering, is where that methodology lives. It argues that durable systems start from the underlying concepts as a foundation, then layer modern machine learning and AI on top to train, validate, and deploy. The chapter I care most about is on the coder mindset, which I believe is the deciding skill in today’s agentic coding workplaces.
// Expertise
What I work on
// The intersection
Where frontier AI meets institutional capital.
Most people pick a side: industry or academia, quant or AI. I work the seams between them and ship the systems that prove it.
Quantitative Finance
Factor models, derivatives, portfolio construction, and risk, run with real institutional capital.
Frontier AI & Agents
Deep learning, LLMs, RAG, and agentic systems, plus the tooling and evaluation that make them trustworthy.
Production & Governance
Pipelines, monitoring, validation, and audit trails that keep models reliable at scale.
Stack
Python · PyTorch · LLMs · RAG · Agents · Evals · MLOps · Cloud
The framework
Production-Grade AI
Foundations → Pipelines → Deployment → Governance
In the book →// Credentials
Background
Designations
- MBA
- MSc, Artificial Intelligence
- CFA Charterholder
- FRM (Financial Risk Manager)
Education
- Stanford UniversityExecutive / leadership & data science studies
- The University of Texas at AustinData science studies
- DHBW (Germany)Quantitative finance program
Roles
- Agentic Tooling & Quant Finance, xAI
- Author, Modern Analytics Engineering (2025)
- Director, data-science consultancy
- Professor & Advisor, university in California
- Managing Director, quant-finance & fintech research firm
- Lead Portfolio Manager, multi-strategy hedge fund
- Equity Analyst, WestLB BNY Mellon Asset Management
- Investment-banking analyst, a German top-tier bank
// Tooling
Tech stack
How I build, the modern AI engineer and researcher toolkit behind the work.
Languages
ML & deep learning
LLMs & agents
Data & infra
MLOps
// Speaking & Media
Talks that travel
I speak to technical and leadership audiences on production AI and quantitative finance.
From Slideware to Systems
Why most enterprise AI stalls after the demo, and the engineering discipline that gets models into trustworthy production.
AI in Quantitative Finance
Where machine learning genuinely moves the needle in risk, factor investing, and systematic strategies, and where it doesn’t.
The Practitioner-Academic Bridge
Teaching several thousand practitioners taught me what actually transfers from research to the desk. A field guide for technical leaders.
// Get in touch
Let’s talk shop.
Roles, research, a workshop, or just comparing notes, I’m always glad to connect.