Science pythonSee in schedule: Thu, Jul 29, 10:00-10:30 CEST (30 min) Download/View Slides
Your parents haven’t talked to each other for years. Your uncle Sam went to the same university as Mr. Finkelstein, while the Trautsohns share a common interest for photography with the Krazinskis. The N’Guyens had Covid, while the Lefebvres aren’t even vaccinated. You would like to achieve a reasonable mix of people knowing each other and of new acquaintances. How are you supposed to arrange the seating of the guests?
Besides wedding planning, mathematical optimization has a wide range of applications including logistics, chemistry, energy system operation, genetics… In this talk, we’ll present how mathematical optimization problems can be formulated, fed with data and solved in Python using the open-source Pyomo package (http://www.pyomo.org/) and open-source optimization solvers such as the COIN-OR Branch-and-Cut Solver.
This talk is aimed at a general audience familiar with Python and requires no mathematical optimization background, nor actual wedding experience.
The repository to this talk: https://gitlab.com/Rikerl/optimalwedding
Type: Talk (30 mins); Python level: Beginner; Domain level: Beginner
I'm a data scientist specialized on energy markets. In my current position at Trayport, I am using machine-learning models to predict energy prices and mathematical optimization methods for energy trading. Before that, I worked as a quantitative analyst for an energy company and developed optimization and forecast models for energy systems. I have a PhD in quantum physics and enjoy presenting analytical results to support effective evidence-based decision taking.