About me

Tobias_Brosch__1_von_2_LowResI am a Machine Learning Development Specialist for Autonomous Driving and C++ development at BMW Car IT GmbH in Ulm. This page maintains my former university web page. Please feel free to contact me via Linkedin.

Education/Experience

  • 2016 (July): Start at BMW Car IT Ulm as development specialist for machine learning in autonomous driving
  • 2016 (Jan-June): Postdoctoral researcher at Ulm University
  • 2015: Research intern IBM – Almaden (cognitive computing group)
  • 2010-2015: Phd student at Ulm University (studentship of “Graduate School Mathematical Analysis of Evolution, Information and Complexity”)
  • 2006-2010: Diploma Mathematician
  • 2006: Community Service
  • 2005: Abitur (Simpert Kraemer Gymnasium Krumbach)

Research Interests

  • Autonomous Driving
  • Simulation
  • Driving Strategy
  • GNSS Positioning
  • Training simultaneous recurrent neural networks
  • Neuromorphic computing
  • Visual Routines
  • Neural Dynamics
  • Deep Learning
  • Reinforcement Learning
  • Machine Learning
  • Asynchronous event based sensing
  • Computer vision

Work Experiences/Teaching/Certifications

  • 2017: Certified LeSS Practitioner
  • 2017: ISO 26262 based BMW Group Standard GS 95014 Training
  • 2017: Professional Scrum Master I (scrum.org)
  • 2016: ADTF Training
  • 2013: Work collaboration with Prof. Dr. Pieter Roelfsema at the Netherlands Institute of Neuroscience (1 month)
  • 2012-present: Sports instructor at  “Hochschulsport Ulm University
  • 2011-2014: Teaching assistant of “Natural Computation”, “Computer Vision 2”, “Information Processing in the Brain”, “Introduction to Computer Science” (Prof. Dr. H. Neumann, Prof. Dr. G. Palm)
  • 2010/11: Teaching assistant of “Object Oriented Programming with C++” (Dr. A. Borchert)
  • 2007–2009: Working as Tutor and typesetting two mathematical lecture notes of Prof. Dr. Spodarev in LaTeX.
  • 2008/2009: ”Studentische Hilfskraft” at the ”Institut für Angewandte Informationsverarbeitung” University Ulm. Creating web-services to access a java-application.
  • 30. Aug 04 to 3. Sep 04: Voluntary internship at AAI-Development-Services section MS2—elementary bioanalytical methods for quantitative determination of active pharmaceutical ingredients in biology matrices by using LC-MS respectively GC-MS.

Publications

2018

  • Brosch, T., Grünwedel, S. and Connette, C. (2018). Facets of the Usage of high-precision digital Maps for Driver Assistance and Autonomous Driving. VDI 2018.

2017

2016

  • Brosch, T. (2016). Kognitiver sequentieller Parallelismus: Von kanonischen neuronalen Schaltkreisen und dem Training rekurrenter neuronaler Netze für perzeptuelle Entscheidungsfindungen. In Hölldobler, S., et al. editors, Ausgezeichnete Informatikdissertationen 2015, pages 51–60. Lecture Notes in Informatics (LNI) – Dissertations, Volume D-16. ISBN: 978-3885799757
  • Jimeno Yepes, A., Tang, J., Saxena, S., Brosch, T. and Amir, A. Weighted Population Code for Low Power Neuromorphic Image Classification. Accepted for publication in Proc. IJCNN (IEEE), 2016.
  • C. Jarvers, T. Brosch, A. Brechmann, M. L. Woldeit, A. L. Schulz, F. W. Ohl, M. Lommerzheim, and H. Neumann. Reversal Learning in Humans and Gerbils: Dynamic Control Network Facilitates Learning. Frontiers in Neuroscience, 10(535):1–17, 2016
  • T. Brosch, S. Tschechne, and H. Neumann. Visual Processing in Cortical Architecture from Neuroscience to Neuromorphic Computing. In Brain–Inspired Computing 2015, volume 10087 of LNCS, pages 86–100. Springer, 2016.

2015

  • Brosch, T., Tschechne, S. and Neumann, H. (2015). On Event-Based Optical Flow Detection. Frontiers in Neuroscience, 9(137):1–15.
  • T. Brosch. Cognitive Sequential Parallelism: From Canonical Neural Circuits to Training Recurrent Neural Networks in Perceptual Decision–Making. PhD thesis, Ulm University, 2015.
  • Brosch, T., Neumann, H. and Roelfsema, P. R. (2015). Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks. PLoS Computational Biology, 11(10):e1004489.
  • Brosch, T. and Neumann, H. (2015). Event-Based Optical Flow on Neuromorphic Hardware. In 9th EAI International Conference on Bio-inspired Information and Communications Technologies, pages 551-558. ICST.
  • Layher, G., Brosch, T. and Neumann, H. (2015). Towards a Mesoscopic-Level Canonical Circuit Definition for Visual Cortical Processing. In CMVC 2015, pages 543-550 .

2014

  • Brosch, T. and Neumann, H. (2014). Interaction of Feedforward and Feedback Streams in Visual Cortex in a Firing–Rate Model of Columnar Computations. Neural Networks, 54:11–6.
  • Brosch, T., Roelfsema, P. and Neumann, H. (2014). Learning of New Perceptual Groupings – A Biologically Plausible Recurrent Neural Network Model that Learns Contour Integration. Journal of Vision (Abstract, VSS), 14(10):941
  • Brosch, T. and Neumann, H. (2014). Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback. Neural Computation, 26:12, pages 2735-89.
  • Tschechne, S. and Brosch, T. and Sailer, R. and von Egloffstein, N. and Abdul-Kreem, L. I. and Neumann, H. (2014). On Event-Based Motion Detection and Integration. In 8th International Conference on Bio-inspired Information and Communications Technologies, pages 298-305. ICST.

2013

  • Brosch, T., Schwenker, F., and Neumann, H. (2013). Attention–Gated Reinforcement Learning in Neural Networks–A Unified View. In ICANN, volume 8131 of LNCS, pages 272–9. Springer.
  • Schels, M., Glodek, M., Meudt, S., Scherer, S., Schmidt, M., Layher, G., Tschechne, S., Brosch, T., Hrabal, D., Walter, S., Palm, G., Neumann, H., Traue, H., and Schwenker, F. (2013). Multi–Modal Classifier–Fusion for the Recognition of Emotions. In Rojc, M. and Campbell, N., editors, Coverbal Synchrony in Human–Machine Interaction, chapter 4, pages 73–98. CRC Press.

2012

  • Brosch, T., Neumann, H. (2012). The Brain’s Sequential Parallelism: Perceptual Decision-Making and Early Sensory Responses. In ICONIP (Part II), volume 7664 of LNCS (pp. 41-50).
  • Brosch, T., Neumann, H. (2012). Perceptual Crowding in a Neural Model of Feedforward-Feedback Interactions. Journal of Vision (Abstract, VSS), 12(9):329.
  • Brosch, T., Neumann, H. (2012). The Combination of HMAX and HOGs in an Attention Guided Framework for Object Localization. ICPRAM (pp. 281-288).
  • Scherer, S., Glodek, M., Layher, G., Schels, M., Schmidt, M., Brosch, T., Tschechne, S., Schwenker, F., Neumann, H., and Palm, G. (2012). A Generic Framework for the Inference of User States in Human Computer Interaction. Journal of Multimodal User Interface, 6(3–4):117–41.

2011

  • Layher, G., Tschechne, S., Scherer, S., Brosch, T., Curio, C., Neumann, H. (2011). Social Signal Processing in Companion Systems – Challenges Ahead. Proc. 41th Conference of the Gesellschaft für Informatik (Informatik’11). GI-Edition LNI, 239.
  • Glodek, M., Tschechne, S., Layher, G., Schels, M., Brosch, T., Scherer, S., Kächele, M., Schmidt, M., Neumann, H., Palm, G., Schwenker, S. (2011). Multiple Classifier Systems for the Classification of Audio-Visual Emotional States. Proc. 4th International Conference on Affective Computing and Intelligent Interaction (ACII’11). Springer LNCS 6975, 359-368.

Participations/Awards

  • May 2017: Digital Hackathon BMW (big data analysis)
  • July 2016: Promotionspreis of the Ulmer Universitätsgesellschaft
  • 2015: Nomination for GI-Dissertationspreis of the Gesellschaft für Informatik
  • November 2014: Invitation to IBM’s Cognitive Systems Colloquium in Almaden (on Truenorth’s architecture developed in DARPA funded SYNAPSE project)
  • June 2010: Achieved studentship of “Graduate School Mathematical Analysis of Evolution, Information and Complexity” (University of Ulm).
  • 2006: Participation at “Jugend forscht”—area mathematics/informatics— attained exceptional award of fundamental research.
  • 2005: Participation at the Regionalwettbewerb “Jugend forscht”— area physics.
  • Jan. 2005: Participation at the TU-Munich AbiTUMath-program (exponential functions on time-scales).
  • Sep. 2003 and 2004: Reached 2. round in Federal Republic of Germany selection process of 35. and 36. international physics olympiade.