Teaching / Materials
Operations Research Tutorial
Tutorials and supporting material for an undergraduate Operations Research course.
Exercitatio artem parat
Tacitus XXIV.
Konstantin Kuchenmeister
Generative AI · Quantitative Finance · Mathematics
New York City
I apply mathematical rigor to AI systems that operate under real constraints: risk, capital, and time.
High-stakes systems, built with rigor.
I build mathematically grounded systems at the intersection of markets and machine intelligence. The objective is simple: create durable advantage through rigor, velocity, and disciplined execution.
Generative AI
Machine Learning
Mathematics of Finance
Decision Systems
Optimization
Operating model
From theory to production — I translate research into reliable systems.
Style
Clear, rigorous, and outcome-driven.
Location
Manhattan, New York.
I’m intentional about trajectory: measurable outcomes, increasing scope, and work that stands up to scrutiny.
Bio
My dream once was to walk the Magnolia Lane in Augusta to play the great golf Masters. In 12,000 hours I worked the handicap out of my game.
Each day I examined another aspect in myself and became more industrious. I owe a lot to the game as I know today, not only self-control.
My teenage years with ancient Greek philosophy had remarkable impact on my sport. Plato was right: 'The first and greatest victory is to conquer oneself'
Overview
I work at the intersection of quantitative finance and generative AI. My background combines mathematics, software engineering, and academic teaching. I focus on translating research into systems that are reliable in production and useful in practice.
Selected credentials
- Teaching: Teaching graduate-level class on "A Mathematical Approach to Generative AI" at Columbia University in New York and the London School of Economics.
- Patent: “Generating responses to structured queries using relevant data extracted from databases” (US 12189638, issued 2025).
- Languages: English (full professional), German (native).
- Membership: Union League Club of New York.
Experience
Dec 2023 – Present
Associate, Goldman Sachs (New York)
CF&O AI Team Lead. Fellow of the Goldman Sachs Global Institute.
Aug 2022 – Dec 2023
Analyst, Goldman Sachs (New York)
Quantitative and AI-focused work in a finance operating context.
Jan 2025 – May 2025
Adjunct Professor, Columbia University
Department of Mathematics · “A Mathematical Approach to Generative AI”.
Nov 2025 – Present
Visiting Fellow, London School of Economics
Department of Mathematics · “A Mathematical Approach to Generative AI”.
Education
Sep 2021 – May 2022
Columbia University — M.A., Mathematics (Mathematics of Finance)
Focus on mathematical foundations for finance: probability, optimization, and machine learning.
Oct 2018 – May 2021
Technical University of Munich — B.Sc., Information Systems
Software engineering and quantitative decision-making, combining technical depth with business process intelligence.
Note: This is my personal website. Any references to organizations reflect my own experience and are not statements made on behalf of those institutions.
Momentum, with discipline
That moment captures what I value: momentum earned through discipline - and the confidence to commit when the work is done.
Execution
Bias toward shipping high-quality work in constrained time.
Rigor
Mathematical foundations before implementation details.
Taste
Signal-first: clarity, restraint, and disciplined judgment.
Selected work
A selection of teaching materials, research notes, and applied work. Links are provided for reproducibility and context.
Research / Markets
A Mathematical Approach to Generative AI
A quantitative perspective on generative models, with emphasis on robustness and applications in finance.
Code / Reproducibility
Repository
Selected implementations and supporting code for research and production prototypes.
Passion: Golf
Golf is where I train discipline under pressure—precision, patience, and repeatable execution.
Focus
Commitment to process: one shot, one decision, full attention.
Performance
Competitive mindset shaped through structured practice and feedback.
Carryover
The same principles apply to work: risk management, tempo, and judgment.
Augusta No. 1 — Tea Olive
“The slight dogleg right is not the easiest tee shot golfers will face.”
Carrying the fairway bunker demands elite distance, while shorter hitters are left with an uphill approach
into an undulating green. (Context paraphrased; source: The Augusta Chronicle, 2020.)
The Augusta Chronicle © 2020. All Rights Reserved.
Contact
For collaboration, speaking, or applied AI work in finance, reach me through the channels below. I respond fastest via LinkedIn.