ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning
Development of ActiveUltraFeedback, a RLHF pipeline for Preference Data Generation utilizing Active Learning 
Projects
Selected research and engineering work across machine learning theory, large-scale model training, and uncertainty-aware modeling. The projects below combine mathematical analysis, empirical evaluation, and systems-oriented implementation.
Bachelor Thesis: Exploring the Hidden Structures of Attention Layers in Transformer Models through the Lens of Gaussian Distributions (July 01, 2024)
Analyzing the mathematics of attention layers through random matrix theory and a finite-dimensional Gaussian approximation 
ETH Large-Scale AI Engineering 2026: Mamba, DeltaNet and Torch DDP
Custom implementation of the DeltaNet and Mamba SSM, comparing performance/throughput against a Transformer baseline, and testing distributed data parallel (DDP) schemes 
ETH Computational Intelligence Lab 2025: Uncertainty-Aware Ensemble for Monocular Depth Estimation
Fine-tuning a mixture-of-experts meta-model for monocular depth estimation using epistemic uncertainty estimates 