Computer Science Student · Tübingen
Armin Burkhardt
I work on quantitative finance, machine learning and interdisciplinary ML applications.
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About
I study computer science in Tübingen. What interests me most is what happens when modern machine learning models reach complex domains where mistakes are expensive - trading, law, medicine, etc. Alongside that I am building the Tübingen Quant Society with a fellow student, a student initiative that closes exactly that gap between theory and practice.
- Studies
- B.Sc. Computer Science, University of Tübingen, 2024 - 2027
- Role
- Machine Learning Engineer, DenkBox GmbH
- Focus
- Quant Finance · Machine Learning · Interdisciplinary ML
- Initiative
- Tübingen Quant Society, Executive Board
Projects
Co-founded in Tübingen to give students hands-on access to quantitative finance and organize events. For the initiative I co-developed the bilingual website: Next.js App Router, statically served, with a SAML 2.0 service provider for university login.
- Next.js
- TypeScript
- SAML 2.0
- Vercel
Built at the HackTheLaw Hackathon at the University of Cambridge for Legora's "The Sparring Room" challenge. Rather than answering questions, the system attacks the user's reasoning and forces them to defend their position - legal sparring instead of a research assistant. Python backend on FastAPI, frontend in React and Vite.
- Python
- FastAPI
- React
- Vite
- LLMs
An experimental model on a dense Gemma4-style backbone: a single MoE block is applied for several iterations (LoopLM-style), rerouting tokens on every pass. Heterogeneous experts - self-attention, cross-attention, retrieval and MLP - share one router. Multi-token prediction as an auxiliary objective, trained in FP8 and NVFP4 among others.
- PyTorch
- Transformer Engine
- CUDA
- MoE
- MTP
Currently training - the repository goes public once the run completes.
Everything that would normally need a glance at the screen - weather, directions, calendar, messages, alarms, device settings, the camera as a description of your surroundings - happens through a Voice to Voice Interface. An LLM (optionally local) decides which of the on-device tools to call and acts on the user's behalf; replies are shaped for German text-to-speech and tailored to the target audience.
- Android
- Kotlin
- LLM Tool-Use
- TTS
- ASR
- RAG
University team project - the repository is not public at the moment.
CV
Education
B.Sc. Computer Science
University of Tübingen
1.08Current grade averageGerman grading scale, where 1.0 is the top grade
Experience
Machine Learning Engineer
DenkBox GmbH, Cyber Valley
Inference optimization, RAG and agent features, fine-tuning and evaluation for a privacy-first, on-premise LLM assistant.
Student Teaching Assistant
Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen
Teaching assistant for C++ projects at the Faculty of Applied Bioinformatics.
Student Teaching Assistant
Chair of Embedded Systems, University of Tübingen
Tutor for "Computer Engineering 3: Microcomputer Lab": supervising students in the lab, grading lab assignments, low-level programming.
Mathematics Tutor
Self-employed
Software Development Intern
Novatec Consulting GmbH
Software development internship, working in Java.
Engagement
Executive Board Member
Tübingen Quant Society
Co-founder; responsible for the concept, university registration and technical infrastructure.
Awards
Deutschlandstipendium
University of Tübingen
Merit-based national scholarship.