Co-organizing the 'Machine Learning for Visual Computing' lecture and leading the exercise sessions, which covers topics such as linear models, deep neural networks, optimization methods, model selection, kernel methods, Bayesian approaches, Gaussian processes, clustering, and recent advances in computer vision.

Evaluating machine learning to accelerate mixed-integer programming solvers for the mapping and scheduling of automotive applications on advanced driver assistance system platforms as well as designing software solutions that enable experts to interactively guide the scheduling process and refine model predictions.

Pioneered a pipeline to automatically segment engravings from surface normals (photometric stereo scans) of ancient Etruscan mirrors which is on-par with a human annotator, overcoming challenges such as deterioration and limited data (59 mirrors). Additionally, developed a methodology for efficient human-in-the-loop refinement of predictions.

Developed a neural network based on graph convolutions and the attention mechanism to detect critical transmission lines in power grids. When employing its predictions, solving for a cost-efficient schedule is 36% faster than the current industry standard, enabling cost savings on the scale of $100 million.

For further training, I gave a presentation on neural networks to about 35 participants, accompanied by an exercise.

With a focus on usability and performance (optimizing database queries, Java/MongoDB), enhanced a React web application to improve the company's hiring process, facilitating the exchange of files and messages.

• PhD Thesis on "Meta-Learning for Rapid Domain Adaptation of Vision-Language Models to Specialized Data-Scarce Domains"
• Supervised by Prof. R. Sablatnig

• Major in Machine Intelligence with a Minor in Data Management
• Thesis: "Learning to Predict Security Constraints for Large-Scale Unit Commitment Problems", Supervised by Prof. A. Krause

• Thesis: "Weighting Intrinsic and Extrinsic Rewards in Reinforcement Learning"
• Qualified for the Bachelor with Honors program (Top 5%) & received multiple Scholarships of Excellence