Technical Skills
Python, C++
PyTorch, TensorFlow, Keras
machine reading comprehension, semantic clustering, multi-agent LLM workflows, retrieval-augmented generation
depth estimation, object detection, image segmentation, 3D shapes optimization, 3D reconstruction, implicit neural representations, hypernetworks, inpainting
Iterative Closest Point (ICP), Principal Component Analysis (PCA)
Unix systems, Git/Github, ssh, Streamlit, Docker

Experience

  1. Intern

    Apple
    LLM-driven context-generation system turning raw databases and source code into a queryable context layer

  2. Intern

    BMW
    Computer Vision: diffusion-based pipelines to generate out-of-distribution pedestrians in road-scene images

  3. Research Engineer

    Mars Auto
    Computer Vision: obstacle detection and driver monitoring for self-driving trucks

  4. Machine Learning Engineer

    Skelter Labs
    Natural Language Processing: machine reading comprehension and semantic clustering

Teaching

  1. Teaching Assistant / Tutor

    Technical University of Munich
    Introduction to Deep Learning: graduate course.
    Managed the course for over 1000 students for three semesters.
    Conducted Q&A sessions and provided online support to students.

Education

  1. M.Sc. Mathematics in Data Science

    Technical University of Munich
    Master’s thesis on Neural Network Inductive Biases for High-fidelity 3D Reconstruction.
    Thesis

  2. B.Sc. Mathematics

    KAIST
    Bachelor’s thesis about minimal geometric intersections of non-homotopic simple closed curves.