🚀 David Schmotz – Portfolio

👨‍💻 About Me

• 📧 davidschmotz@gmail.com

I am a researcher with a background in mathematics, computer science, and machine learning. My current interests lie in AI Safety, with a particular focus on the Robustness of Large Language Models (LLMs). I am passionate about developing methods that make AI systems safer, more reliable, and interpretable.

📚 Education

University of Cambridge — Part III MASt Mathematics (2023–2024)

• Grade: Honours
• Courses: Modern Statistical Methods, Concentration Inequalities, Topics in Convex Optimisation, Information Theory, Statistical Learning in Practice, Advanced Probability
• Thesis: Differentially Private Synthetic Data, supervised by Prof. Dr. Po-Ling Loh

University of Göttingen — BSc Mathematics (2020–2023)

• Grade: 1.5
• Courses: Real Analysis, Analytic Geometry & Linear Algebra, Analysis on Manifolds, Numerical Analysis, Measure & Probability Theory, Stochastics, Statistical Foundations of Data Science, Optimization
• Thesis: Conjectures on Empirical Optimal Transport and their Numerical Assessment

ETH Zürich — Visiting Student in Mathematics (2022)

• Courses: Graph Theory, Mathematics of Machine Learning, Topology

University of Göttingen — BSc Applied Computer Science (2018–2023)

• Grade: 1.5
• Courses: Advanced Algorithms & Data Structures, Theoretical Computer Science, Scientific Computing, ODEs, Programming, Statistical Data Science, Deep Learning with PyTorch

🧑‍🔬 Experience

Student Researcher (May 2025–now)
Chair for Scientific Information Analytics, University of Göttingen

• Teaching assistant for “Deep Learning for NLP”
• Researching robustness of LLMs

Stephan Hell Fellowship (Oct 2024–March 2025)
Max-Planck-Institute for Multidisciplinary Sciences

• Developed genome assembler for metagenomic data using Bayesian statistics, Linclust clustering, and repeat detection algorithms in Python & C++

Teaching Assistant — University of Göttingen (2019–2020, 2022–2023)

• Led sessions and graded homework in “Computer Science 1” and “Discrete Mathematics”

Working Student — German Aerospace Center Göttingen (2020–2021)

• GPU-accelerated flutter analysis using Python (PyTorch), C, Matlab

Intern/Working Student — ESE GmbH, Hannover (2018, 2019)

• Client projects in PHP and C#, React-based accounting tool

Volunteer — SNNTG e.V., Hannover (2019)

• Developed iOS/Android app, CMS, and REST API (Swift, Java, React, Node.js)

Co-Founder — Fahrpreiserstattung.de (2017–2018)

• Automated train refund applications (PHP, Angular.js)

🧠 Interest: AI Safety & LLM Robustness

I am deeply interested in AI Safety, with a special focus on the robustness and reliability of Large Language Models (LLMs). My current research explores how LLMs can be made more robust to adversarial prompts, distributional shifts, and unexpected behaviors, ensuring they are trustworthy and safe for real-world deployment.

🎓 Scholarships

• Stephan Hell Fellowship 2024–2025
• Deutschlandstipendium 2020
• Lower Saxony scholarship 2020, 2021

📝 Curriculum Vitae

You can view or download my full CV here:

• View My CV

🌐 Website

Visit my live site: davidsmts.github.io

📬 Contact

For professional inquiries, collaborations, or academic references, please use the contact form or email provided above.

Academic References

• Prof. Dr. Florin Manea — florin.manea@informatik.uni-goettingen.de
• Dr. Johannes Söding — soeding@mpinat.mpg.de
• Dr. Jan Philip Wahle — wahle@uni-goettingen.de
• Prof. Dr. Po-Ling Loh — pll28@cam.ac.uk

Thank you for visiting!