Nico Strasdat

Teaching

You can find information about my teaching activities here.

Scientific Interests

• Methods for machine learning, in particular Support Vector Machines and Deep Learning
• Application of convex optimization and duality
• Newton-type methods for the solution of optimization problems
• Integer-linear programming
• Treatment of application-related optimization problems
• Effective and efficient implementation of algorithms

Short CV

• Doctor rerum naturalium in Mathematics (10/2014 to 10/2023), Dissertation: "Duality, Derivative-Based Training Methods and Hyperparameter Optimization for Support Vector Machines"
• Master of Science (Mathematik) at Technische Universität Dresden (10/2012 to 09/2014), Master's Thesis: "Reduced-Set Support Vector Machines"
• Bachelor of Science (Mathematik) at Technische Universität Dresden (10/2009 to 08/2012), Bachelor's Thesis: "Random Forests und ihre Anwendung zur Regression"

Publications

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Recent and current projects

• Datengetriebene Generierung von Modellen und Sensitivitätsanalyse für Produktionsprozesse (BMBF, 04/2020 to 11/2023) im Verbundprojekt OptProDat
• Anspruchsvolle Freiformbeschichtung flächiger und 3-dimensionaler Substrate durch Inline-Sputtertechnick (SAB, 08/2019 to 12/2020)
• Newton-type Methods for Nonsmooth Equations with Nonisolated Solutions (DFG, 01/2017 to 12/2019)
• Optimierungstechniken für Klassifikation und Regression, Teil II (Industry, 01/2017 to 12/2017)
• Optimierungstechniken für Klassifikation und Regression (Industry, 10/2014 to 12/2016)
• Support-Vector-Methoden und multivariate Regression (Industry, 04/2013 to 09/2014)
• Support Vector Methoden (Industry, 09/2011 to 08/2013)