Teaching

• Advanced Topics in Discrete Mathematics: Local Search - Spring 2023.
  Co-lecturer together with Stefan Hougardy.
  Advanced course aimed at MSc students in Mathematics with previous exposure to discrete mathematics and optimization. The technique of local search is discussed from various points of view, including its use in approximation algorithms for hard combinatorial optimization problems, variants of local search that are used in practical applications, and complexity theoretic perspectives.

  
  

Student supervision

I was involved in co-supervising the following projects.

• Computational Approaches to Vehicle Routing Problems
  Maëlys Lerat, Master Thesis. ETH Zurich, Fall 2020. Co-supervision with Federica Cecchetto and Rico Zenklusen.
  Implementing and testing a column generation algorithm to solve linear relaxations of a vehicle routing problem in a framework of package deliveries with time window constraints. Part of a collaboration with notime AG and the Swiss Post.

• Large-Scale Stochastic Programming for Hydropower Valuation and Operation
  Jan Wey, Master Thesis. ETH Zurich, Fall 2018. Co-supervision with Simon Bruggmann and Rico Zenklusen.
  Collaboration with the Swiss energy supplier Axpo on valuation models for hydroelectric power plants with the goal of improving current approaches to achieve faster running times.

• A Case Study on the Generation of Efficient Surgery Schedules
  Edwin L. Hernandez, Master Thesis. ETH Zurich, Spring 2018. Co-supervision with Rico Zenklusen.
  Collaboration with the University Hospital of Zurich to analyze current operation room schedules and develop mathematical scheduling models with the aim of finding scheduling strategies resulting in more efficient schedules.

• Optimisation of Engine Manoeuvres
  Patrick Gantner, Master Thesis. ETH Zurich, Spring 2018. Co-supervision with Simon Bruggmann and Rico Zenklusen.
  Collaboration with BLS Cargo, a Swiss rail freight transport company, for optimizing manoeuvres of train engines on storage tracks, including development of a decision support software based on mathematical optimization techniques.

Teaching assistance

During my time as a PhD student, I was involved in the following courses at ETH Zurich as a teaching and/or head assistant (i.e., teaching and/or organizing exercise classes, preparing problem sets, helping in the preparation of lecture material, etc.):

• Linear and Combinatorial Optimization (before fall 2021: “Mathematical Optimization”) (401-3901-00L) - Fall 2019, Fall 2020, Fall 2021. Lecturer: Rico Zenklusen.
  Introduction course to mathematical optimization, setting as a goal to get a thorough understanding of various classical mathematical optimization techniques with an emphasis on polyhedral approaches. In particular, the course covers linear programming and polyhedra, flows and cuts, combinatorial optimization techniques, ellipsoid method, and an introduction to integer programming. It is aimed ad third year bachelor or master students in mathematics.

• Network & Integer Optimization: From Theory to Application (401-3902-21L) - Spring 2021. Lecturer: Rico Zenklusen.
  This course covers various topics in Network and (Mixed-)Integer Optimization. It starts with a rigorous study of algorithmic techniques for some network optimization problems (with a focus on matching problems) and moves to key aspects of how to attack various optimization settings through well-designed (Mixed-)Integer Programming formulations.

• Combinatorial Optimization (401-4904-00L) - Spring 2019, Spring 2018, Spring 2017, Spring 2016. Lecturer: Rico Zenklusen.
  Advanced course on various modern combinatorial optimization techniques with an emphasis on polyhedral approaches (polyhedral descriptions for various combinatorial optimization problems, combinatorial uncrossing, the ellipsoid method), aimed third year bachelor and master students in mathematics.

• Discrete Mathematics (401-0053-00L) - Fall 2018, Fall 2017, Fall 2016. Lecturer: Rico Zenklusen.
  Introductory course on foundations of discrete mathematics aimed at second year students in electrical engineering, including combinatorics (elementary counting), graph theory and graph algorithms (breadth first search, spanning trees, flow problems), basic algebra, and applications thereof.

• Numerical Analysis I (401-1652-10L) - Spring 2015, Spring 2014. Lecturers: Christoph Schwab (Spring 2015), Ralf Hiptmair (Spring 2014).
  Introduction to numerical methods aimed at second year students in mathematics covering numerical linear algebra, quadrature, interpolation and approximation methods as well as their error analysis, and implementation in MATLAB.

• Geometry (401-1511-00L) - Fall 2014. Lecturer: Mark Burger.
  Minor course aimed at first year students in mathematics, introducing the concept of groups and dicussing their importance in euclidean and hyperbolic geometry.