Teaching

Material

• I am the lead author of the CP-SAT Primer.
• I am co-organizer and technical lead of the CG:SHOP Challenges, a yearly competition on optimizing hard problems in computational geometry.

Teaching Experience

Since my postdoctoral tenure, I have had the privilege of independently lecturing and co-designing courses that bridge the gap between theoretical concepts and real-world applications.

• Algorithm Engineering (Co-Lecturer with Phillip Keldenich): This course, which we co-designed, focuses on the efficient implementation of algorithms, emphasizing CPU and memory architecture. It also delves into solving NP-hard optimization problems using various techniques like MIP, CP, SAT, and Heuristics. First offered in the summer term of 2022, it received positive feedback from the participants.
• Algorithms Lab: This practical lab course allows students to tackle a number of optimization problems and finally apply their newly gained skills to a project of their choice. Its popularity and high demand have led to a selective admission process.

As a teaching assistant, I contributed to several courses and supervised various student-led projects. My supervised teams have repeatedly won university-wide competitions.

• Winter 23/24: Supervised and designed Algorithms Lab
• Summer 23: Supervised and designed Algorithms Lab
• Summer 22: Co-Lecturer and Co-Designer of Algorithm Engineering (with Phillip Keldenich)
• Summer 22: Supervised Practical Software Development Training (“Software Entwicklungs Praktikum”)
• Winter 21/22: Supervised and redesigned Algorithms Lab
• Winter 21/22: Tutorials and Exercises for Mathematical Optimization (Linda Kleist)
• Summer 21: Supervised Algorithms Lab
• Summer 21: Tutorials and Exercises for Approximation Algorithms (Sándor Fekete)
• Summer 21: Supervised Practical Software Development Training (“Software Entwicklungs Praktikum”)
• Winter 20/21: Supervised Algorithms Lab
• Winter 20/21: Tutorials and Exercises for Mathematical Optimization (Linda Kleist)
• Summer 20: Supervised Practical Software Development Training (“Software Entwicklungs Praktikum”)
• Winter 19/20: Tutorials and Exercises for Approximation Algorithms (Sándor Fekete)
• Winter 19/20: Tutorials and Exercises for Mathematical Optimization (Linda Kleist)
• Summer 19: Supervised Practical Software Development Training (“Software Entwicklungs Praktikum”)
• Winter 18/19: Tutorials and Exercises for Mathematical Optimization (Sándor Fekete)
• Summer 18: Supervised Practical Software Development Training (“Software Entwicklungs Praktikum”)
• Winter 17/18: Tutorials and Exercises for Mathematical Optimization (Sándor Fekete)
• Summer 17: Tutorials and Exercises for Approximation Algorithms (Sándor Fekete)
• Winter 16/17: Tutorials and Exercises for Introduction to Algorithm Engineering (Victor Alvarez)

Thesis Supervision

Throughout my academic career, I have guided numerous students in their bachelor’s, master’s, and project theses, providing them with insights and methodologies in various areas of computational research.

• 2024 Computing Coverage Paths with Turn Costs using Dense Meshes
• 2024 Computing Point Sets with Maximal Dispersion in Polygons
• 2023 Computing Optimal Solutions for the Traveling Salesman Problem with Dynamic Travel Costs
• 2023 The Close-Enough Traveling Salesman Problem with Turncost
• 2023 A Branch-and-Bound Algorithm for the Traveling Salesman Problem with Neighborhoods
• 2023 Large Neighborhood Search for Pairwise Interaction Sampling of Configurable Systems
• 2022 Computing feasible tilt assembly sequences with minimal auxiliary tiles
• 2022 Computing exact solutions to the Close-Enough TSP
• 2022 Optimierung der Kommunikation in Multi-Hop-Satellitennetzwerken
• 2022 Aggregation of particles in 3d using global inputs
• 2021 Theoretical and practical approaches for optimizing lawn mowing and milling
• 2021 Generating and Classifying Geometric Graphs with high Diversity
• 2021 Minimum Scan Cover in 3D mit Erdumlaufbahnen
• 2021 Algorithmic Methods for Navigating Particle Swarms by Global Force
• 2021 Instance Sparsification for Hard Geometric Optimization Problems
• 2021 Berechnung multikriterieller Lösungen für überdeckende Touren mit Scankosten
• 2021 Berechnung von Farbverläufen mit Multi-Objective Integer Programming
• 2020 Reinforcement Learning for Navigating Particle Swarms by Global Force
• 2020 Algorithmic Problems in Inter-Satellite Communication
• 2019 Evolving Instances for the Experimental Analysis of Algorithms
• 2019 Lösungsansätze für das Budget Travling Salesman Problem mit GPGPUs
• 2018 Automated Analysis for Algorithm Engineering with Machine Learning
• 2017 Algorithmic Methods For Capturing Maximum Weight Geometric Subsets
• … (this list is incomplete)