All participants were asked to vote for the most valuable mentor. The vote gave a tie.
Jeffrey Kelling (HZDR) tutored the DeepFEI team trying to classify full events at Belle2.
Sebastian Starke (HZDR) tutored the Lung-Radgen team trying to classify lung tumor tissue from CT images and mRNA datasets thereof.
Walter de Back (TU Dresden, Universitätsklinikum Dresden)
Walter de Back has a MSc in Artificial Intelligence from Utrecht University (NL) and worked as a Junior Fellow at the the Collegium Budapest-Institute for Advanced Study. He studied pattern formation in tissues using multi-scale agent-based simulations, for which he obtained a PhD in Computational Biology from TU Dresden. Currently, Walter works as a postdoc data scientist at the Faculty of Medicine (TU Dresden) where he uses deep learning for biomedical image analysis. Recent projects include cell segmentation in live cell microscopy, dental age estimation from panoramic radiographs, and tumor tissue classification based on mass spectrometry imaging data.
Emilio Dorigatti (LMU Munich, Helmholtz-Zentrum München)
Emilio is a Ph.D. student at LMU Munich, where he is applying machine learning methods to the design of personalized vaccines for HIV and cancer, in collaboration with the Helmholtz Zentrum in Munich. Emilio has a Bachelor's degree in Computer Science and a double Master's degree in Data Science, with a minor in Innovation and Entrepreneurship. He also has several years of working experience as a Software Developer, Data Engineer and Data Scientist. He is mostly interested in Bayesian inference, uncertainty quantification, and Bayesian Deep Learning.
Alexandr Dibrov (Max Planck Institute of Molecular Cell Biology and Genetics, Center for Systems Biology Dresden)
Appearing Soon.Alex has a MSc in biophysics from TU Dresden and is currently a PhD student at Max Planck Institute of Molecular Cell Biology and Genetics. He is interested in the development and application of advanced life imaging techniques. He is particularly enthusiastic about applying computer vision and deep learning for real-time image analysis as well as the generation of synthetic training data in bioimaging.
Oliver Guhr (HTW Dresden)
Oliver received a bachelor degree in computer science and business studies in 2014 (HfT Leipzig) and holds a master degree in computer science since 2018 (HTW Dresden). From 2007 to 2018 he was working as a software engineer in the sector of information and communication technology. In 2018 he became a research fellow at the HTW Dresden in the department of artificial intelligence. His research focuses on Spoken Dialogue Systems, Machine Learning, and Natural Language Processing. He also teaches the Natural Language Processing part of the Deep Learning course at HTW Dresden.
Jenia Jitsev (Juelich Supercomputing Center, Helmholtz Research Center Juelich)
Jenia is leading the Cross-Sectional Team Deep Learning (CST-DL) at the Jülich Supercomputing Center (JSC) - a research group with focus on large-scale continual unsupervised and reinforcement learning, built in frame of Helmholtz Artificial Intelligence Cooperation Unit (HAICU). His work takes place in the overlap of computational neuroscience and machine learning, focusing on plasticity and learning in deep artificial and biological neural networks. From a high performance computing (HPC) perspective, his interest is in scaling up learning in deep neural networks across multiple GPUs or other accelerators and enabling robust maintenance of continual learning systems running on HPC facilities over long periods (months-years) of time. Before joining JSC, Jenia studied computer science and psychology at University of Bonn and University of Bochum, and obtained his PhD in Computer Science from University of Frankfurt working on models of synaptic plasticity und unsupervised learning in the visual cortex. Later, he was with Max Planck Institute for Neurological Research in Cologne and at Institute for Neuroscience and Medicine at Forschungszentrum Jülich, working on reinforcement learning and models of reward-based learning in cortico- basal ganglia circuits in the brain. Long-term goal of his research is on enabling large-scale continual, multi-task, active self-supervised learning that is capable of growing generic models from incoming streams of data, extracting knowledge and skills transferable across different domains.
Jeffrey Kelling (Helmholtz-Zentrum Dresden-Rossendorf)
Jeffrey Kelling obtained his diploma in statistical physics and his Ph.D. in physics on massively parallel lattice Monte-Carlo simulations on GPUs. He is a scientist in the computational science group at the Helmholtz-Zentrum Dresden-Rossendorf, concerned with high performance computing and deep learning applications in science as well as giving courses to teach related skills to fellow scientists.
Jonny Hancox (Nvidia)
Jonny is a Deep Learning Senior Solutions Architect at NVIDIA and works in the Health & Life Sciences team in EMEA. His work is focussed on enabling biologists, researchers and clinicians to get the most value from their data using the latest hardware and software tools. Jonny's current focus areas are computational pathology, radiology and genomics. Before joining NVIDIA in 2018, Jonny spent four years working in a similar role at Intel and was based at Imperial College London so that the team could work closely with the medical and computer science groups there. Before that, Jonny was Technical Director for a software company creating automated data capture applications for the UK National Health Service and public sector. Although originally trained as a product design engineer, most of Jonny's career has been spent writing code - something he strives to maintain.
Nico Hoffmann (Helmholtz-Zentrum Dresden-Rossendorf)
Nico Hoffmann earned his PhD (Dr. rer. nat.) in December 2016 from Technische Universität Dresden (Dresden, Germany) in medical image analysis. He developed statistical machine learning methods that compensate motion artefacts and advanced semiparametric regression models as well as neural networks to recognize evoked neuronal activity of the human brain. Additionally, he and his students developed novel approaches for multimodal 2D-3D image registration and -fusion. Nico visited the Laboratory of Mathematics in Imaging of Harvard University from 2018 to 2019. During that time, he advanced the reconstruction of the human brain's nerve fibre bundles using recurrent convolutional neural network. Recently, Nico joined the Computational Radiation Physics group of Helmholtz-Zentrum Dresden-Rosssendorf. His work mainly focusses on the design of deep neural networks that approximate forward simulations as well as parameter estimation (inverse problems) of complex physical systems.
Thomas Neumann (freelance R&D software developer & contractor)
Thomas Neumann is specialized in 3D scan data processing where he employs techniques from machine learning, nonrigid registration, statistical modeling, visualization and nonlinear optimization. He also teaches about convolutional neural networks at HTW Dresden. There, he previously was a PostDoc working reconstruction and analysis of human motion in the junior research group "TISRA". He defended his PhD in 2016 at the Institute for Computer Graphics at TU Braunschweig on "Reconstruction, Analysis, and Editing of dynamically deforming 3D-Surfaces".
Mangal Prakash (Max Planck Institute of Molecular Cell Biology and Genetics, Center for Systems Biology Dresden)
Mangal Prakash received the BTech degree in Electrical Engineering from NIT Durgapur, India and MS degree in Electrical Engineering from University of Minnesota, USA. He is currently pursuing a PhD in Computer Science at MPI-CBG/CSBD in the lab of Dr. Florian Jug working on developing computer vision techniques and algorithms for cell segmentation and tracking. His research interests include machine learning and computer vision.
Clemens Reinhardt (TU BAF Freiberg, TU Dresden, Helmholtz-Zentrum Dresden-Rossendorf)
Clemens holds a diploma in mechanical engineering from TU Dresden since 2017 and started in the same year the master program “Computational Science and Engineering” at TU Dresden and TU BAF Freiberg with the goal to apply Machine Learning techniques to classical mechanical engineering challenges. Currently, he is working on his master thesis “Improvement of the reconstruction of coherent diffraction images through learning of proximal operators” at the HZDR.
Uwe Schmidt (Max Planck Institute of Molecular Cell Biology and Genetics, Center for Systems Biology Dresden)
Uwe Schmidt received the MSc and PhD degrees in computer science from TU Darmstadt, Germany. He has been a visiting graduate student at the University of British Columbia in Vancouver, Canada. Uwe is currently a postdoc at MPI-CBG in Dresden, Germany. His research interests include machine learning and computer vision.
Steffen Seitz (TU Dresden, Sonotec GmbH)
Steffen holds a diploma in electrical engineering from Technische Universität Dresden (TUD) since April 2016. Since then he is working towards his PhD at the intersection of machine learning and predictive maintenance at the fundamentals of electronics chair at TUD. He develops unsupervised machine learning algorithms like RNN based autoencoders for representation learning to disentangle wear in machinery elements for industrial applications. He also is co-founder of the Machine Learning Community Dresden (MLC), to connect regional scientists working on artificial intelligence topics.
Sebastian Starke (Helmholtz-Zentrum Dresden-Rossendorf)
Sebastian received his bachelor degree in mathematics in 2013 and his masters degree in statistics in 2015 from the Otto-von-Guericke University in Magdeburg. Afterwards he worked as an algorithm engineer in the field of speech recognition before joining the computational science group at HZDR in October 2016. At the moment he is working together with OncoRay scientists to apply deep learning methods to CT images of cancer patients to improve personalized treatment.
Patrick Stiller (Helmholtz-Zentrum Dresden-Rossendorf)
Patrick Stiller is a student assistant at the Computational Radiation Physics Group of the HZDR. In a student thesis, he worked on a deep learning parameter reconstruction on small-angle x-ray scattering images. Patrick is a co-founder of the machine learning community Dresden and lectured on the visualization tool Tensorboard. Currently, he is working on deep learning based solvers for partial differential equations.
Kira Vinogradova (Max Planck Institute of Molecular Cell Biology and Genetics, Center for Systems Biology Dresden)
Kira obtained her bachelor’s degree in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology. She has been a summer intern at the IST Austria and the University of Heidelberg, she has also worked part-time at Kurchatov Institute and at Samsung Research Institute Russia. Currently, Kira is working on interpretation of convolutional neural networks in the group of Prof. Gene Myers as a PhD student. Her research is mainly focused on explainable AI, image classification and segmentation.
Martin Weigert (Max Planck Institute of Molecular Cell Biology and Genetics, Center for Systems Biology Dresden)
Martin Weigert holds a Diploma in Physics from Technical University Dresden. He is currently wrapping up his PhD in the group of Gene Myers at MPI-CBG in Dresden, where he investigates computational methods for advanced fluorescence microscopy. Among his interests are computational optics, physical simulations and visualizations, and machine learning methods for image reconstruction.