My goal is to build machine learning models capable of approaching the performance of biological brains in terms of data-efficiency and robustness to perturbations and changes in the environment. Drawing inspiration from adaptation behaviour of biological systems, I am currently studying methods for domain adaptation using optimal transportation theory, adversarial learning and generative models.
Besides, I am interested in applications to medical image processing and neuroscience. Other research interests include semi-supervised learning in domains with little labeled data, the cross-section between machine learning and computational neuroscience and philosophical questions in AI research.
In the past, I worked on the open-source microscope NeuBtracker with Gil Westmeyer and Panagiotis Symvoulidis at the Helmholtz Zentrum Munich. Previously, I worked with Daniel Bug and Dorit Merhof at RWTH Aachen University on a novel approach for domain adaptation in histopathological images.
Also see my full list of talks and publications below.
MSc in Neuroengineering
Technical University of Munich (2016 - present)
BSc in Electrical Engineering, Information Technology and Computer Engineering
RWTH Aachen University (2013 - 2016)
For my Master’s thesis, I am investigating mechanisms for robust signal processing in neural circuits and research possible applications for domain adaptation in deep learning models.
HelmholtzZentrum München 05/2017 - 05/2018
Working in Gil Westmeyer’s group on novel image processing techniques. I work on real-time deep learning based image processing techniques for temporal 2D and 3D imaging data, with the goal of enabling closed-loop cell-circuit-control with spatiotemporal precision and deep tissue penetration in zebrafish.
School of Computing, University of Kent 03/2017 - 05/2017
I am currently on a research visit in the labs of Caroline Li and Prof. Yi-Ke Guo and work on unsupervised learning methods for analysis of time-series data such as EEG.Implementation of our approaches is realized in TensorFlow and TensorLayer.
Institute of Imaging and Computer Vision, Aachen 05/2016 - 02/2018
Within the ILUMINATE project, I am working on deep learning algorithms for semi-supervised dense classification of histopathological images used in cancer research. Apart from deployment of networks in our software system, I worked on a novel approaches to apply deep learning in contexts with little available labeled training data. Used software packages are mainly Theano and TensorFlow.
RWTH Aachen University 09/2014 - 06/2015
Winter Term 2014: Mathematical Methods in Electrical Engineering, Prof. Merhof, Institute of Imaging and Computer Vision, Aachen, Summer Term 2015: Fundamentals of Electrical Engineering II, Prof. DeDoncker, ISEA, Aachen
Development of a software system for automated calibration of 3D camera systems as a preparation for sensor fusion algorithms, using C++, the Point Cloud Library and ROS.
TUfast e. V. Driverless Racing Team 2016 - 2017
At TUfast, we are developing an autonomous version of a Formula Student Racecar to participate in the Formula Student Driverless competition in Hockenheim. I work in the Sensors and Perception group on deep learning approaches for processing of sensor inputs.
Formula Student Team RWTH Aachen e. V. 2014 - 2016
I worked on the hardware and software design of data acquisition devices and the battery management system in the Formula Student racecars eace04 and eace05.
IT4Kids, Enactus Aachen e. V. since 2013
To provide children in primary school with courses in computer science, I founded IT4Kids in 2013 and build up the student initiative that is still active in Aachen as of now. With our classes, we have reached hundreds of pupils. The project was awarded the 3rd place at the Enactus National Competition 2015 and a winning project in the Google Impact Challenge (awarded 10.000 €). We also started the development of a flexible programming environment for pupils, combining advantages of Scratch and Python.
Development of a fully autonomous racecar for the FSG Driverless competition in Hockenheim.
M.Sc. Neuroengineering Student Blog with latest information about our study program and events.
ILUMINATE develops a novel platform for integrated analysis of in-vivo models for preclinical evaluation of new compounds in oncology, including innovative therapeutic approaches in oncoimmunology.