Roles

Reseach Assistant

Institute of Imaging and Computer Vision, Aachen, since 2016

  • I am currently working on the ILUMINATE project and implement 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 work on new approaches to apply deep learning in contexts with little available labeled training data. Used software package are mainly Theano and TensorFlow.

Teaching Assistant

Software Engineering Intern

Institute for Real-Time Learning Systems, University Siegen, Summer 2013

  • Development of a software system for automated calibration of 3D camera systems as a preparation for sensor fusion algorithms
  • Used software: C++/Point Cloud Library/ROS

Extracurriculars

Student Engineer, Perception and Sensor Group

TUfast e. V. Driverless Racing Team, 2016 - present

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.

Student Engineer, Control Systems

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.

Founder and Project Manager

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.

Publications

Compressed sensing has proven to be an important technique in signal acquisition, especially in contexts in which sensor quality or the maximum possible duration of the measurement is limited. In this report, deep learning techniques are used to improve compressive sensing in the context of image acquisition. In a previous approach, stacked denoising autoencoders capable of reconstructing images considerably faster than conventional iterative methods were deployed. Apart from reviewing this approach, a possible extension using convolutional autoencoders inspired by the popular VGGnet architecture is discussed. Instead of learning models from scratch, a simple yet effective way for adapting available filters used in ImageNet classification is presented. By reformulation of the autoencoder structure in terms of a fully convolutional network, the previous approach can be adapted to arbritrarly large images for efficient learning of the measurement matrix and sparsity basis. Suggestions on the real implementation of such as system conclude the report.

In this thesis, various methods for the design of deep architectures for tissue classification are presented. By using transfer learning and unsupervised feature learning, it is shown that powerful state of the art models with millions of parameters can be finetuned to outperform previous approaches despite the lack of sufficient labeled training examples. Several models such as the 16-layer VGGnet, the GoogLeNet model with some exten- sion, convolutional restrict boltzman machines and convolutional denoising autoencoders were trained on the ILUMINATE-9 dataset. Along with evidence provided on how the training policy for the networks should be designed, a whole model zoo, trained on the ILUMINATE-9 dataset, is provided along with this thesis.

Projects

Ecurie Aix eace04/05

Contributions include the design of electric control units

TUFast Driverless nb017

Development of a fully autonomous racecar for the FSG Driverless competition in Hockenheim.

ILUMINATE

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.

IT4Kids

IT4Kids provides computer science classes to elementary school pupils - Providing software, teaching materials and easy communication between schools and teaching students