IST Austria

Computer Vision and
Machine Learning Group


Christoph H. Lampert - Curriculum Vitae

  • ...
  • Christoph Lampert
    Name: Christoph Lampert
    Date of Birth: 23. April 1974
    Place of Birth: Konstanz, Germany
    Nationality: German
    Academic Positions
  • 04-2017-09/2017 Visiting Faculty, Google Reseach, Zurich, CH.
  • since  04/2015    Professor, Institute of Science and Techology Austria, Klosterneuburg.
  • 04/2010-03/2015 Assistant Professor, Institute of Science and Techology Austria, Klosterneuburg.
  • 02/2007-03/2010 (Senior) Research Scientist, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
  • 10/2006-12/2006 Research Intern, Google Inc, Mountain View, USA.
  • 02/2004-01/2007 Senior Researcher, German Research Center for Artificial Intelligence (DFKI),Kaiserslautern, Germany.
  • 10/2001-09/2003 Research Assistant, University of Bonn, Germany.
  • Education
  • 03/2003 Dr. rer. nat. Mathematics, University of Bonn, Germany (summa cum laude), Advisor: Prof. Ingo Lieb.
                  Thesis title: "The Neumann Operator in strictly pseudoconvex domains with a weighted Bergman metric"
                  (in German).
  • 10/2000-09/2001 Research Stay, Chalmers University, Gothenburg, Sweden. Host: Prof. Mats Andersson.
  • 03/2000 Diplom Mathematics, University of Bonn, Germany. Advisor: Prof. Ingo Lieb.
                  Thesis title: "Canonical Solution Operators for δu=f in strictly pseudoconvex domains with weighted
                  Bergman norm" (in German).
  • 06/1993 Abitur, Internatsgymnasium Schloß Plön, Germany.
  • Grants & Awards
  • 01/2013-12/2017 ERC Starting Grant "Life-long learning of Visual Scene Understanding"
  • 06/2011 Best Reviewer Award, IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
                Colorado Springs, CO, USA.
  • since 2013 Member of the Young Academy of the Austrian Academy of Science
  • 10/2008 Best Student Paper Award, European Conference on Computer Vision (ECCV), France,
                for the paper "Learning to Localize Objects with Structured Output Regression" with M. Blaschko.
  • 06/2008 Best Paper Award, Computer Vision and Pattern Recognition (CVPR) Conference, USA, for the paper "Beyond
                Sliding Windows: Object Localization by Efficient Subwindow Search" with M. Blaschko and T. Hofmann.
  • 06/2008 DAGM Main Prize, German Society for Pattern Recognition (DAGM), Germany,
                for the paper "A multiple kernel learning approach to joint multi-class object detection" with M. Blaschko.
  • Scholarships
  • 2002         Fellowship, Bonn International Graduate School in Mathematics, Physics and Astronomy
  • 2000-2002 PhD scholarship, Studienstiftung des Deutsches Volkes.
  • 2001         Foreign exchange scholarship, Deutscher Akademischer Austausch Dienst.
  • Scientific Talks and Presentations Invited Talks at Conference and Workshops
  • 03/2017 Invited talk: IIT-IST Workshop: Incremental Classifier and Representation Learning, Genoa, IT.
  • 10/2016 Invited talk: TASK-CV Workshop at ECCV: Towards Principled Transfer Learning, Amsterdam, NL.
  • 08/2016 Keynote talk: VS3 Workshop: Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, Prague, CZ.
  • 06/2016 Keynote talk: CHIST-ERA Conference: Towards Lifelong Machine Learning, Vienna, AT.
  • 05/2016 Invited talk: AC Workshop of the DAGM: Lifelong Learning for Visual Scene Understanding, Hannover, DE.
  • 02/2016 Invited talk: High Visual Computing 2016 (HiViSComp)
  • 09/2015 Keynote talk: Netherlands Conference on Computer Vision (NCCV)
  • 07/2015 Keynote talk: Symposium on Intelligent Systems in Science and Industry: Towards Lifelong Learning
  • 04/2015 Keynote talk: Dagstuhl Seminar on Machine Learning with Interdependent and Non-identically Distributed Data, Dagstuhl, DE.
  • 02/2015 Invited talk: Weizmann Workshop on Computational Challenges in Large Scale Image Analysis, Weizman Institute, Rehovot, IL.
  • 09/2014 Invited talk: ECML/PKDD Workshop on Multi-Target Prediction, Predicting multiple structured outputs, Nancy, FR.
  • 09/2014 Invited talk: ECCV Workshop on Transferring and Adapting Source Knowledge in Computer Vision, Learning with a time-evolving data distribution , Zurich, CH.
  • 06/2014 Invited talk: CVPR Workshop on Long Term Detection and Tracking, Learning with a time-evolving data distribution , Columbus, OH, USA.
  • 06/2014 Invited talk: ECCV Area Chair Workshop, Closed-form training of conditional random fields for large scale image segmentation , Zurich, CH.
  • 02/2014 Invited talk: Workshop on Recent Trends in Computer Vision, Learning with asymmetric data distributions, University of Maryland , College Park, MD, USA.
  • 12/2013 Invited talk: ICCV Workshop on Visual Domain Adaptation and Dataset Bias, Towards lifelong visual learning: From practice to theory and back , Sydney, AU.
  • 05/2013 Featured talk: Workshop of the Austrian Association for Pattern Recognition, Visual scene understanding , Innsbruck, AT.
  • 11/2011 Invited talk: Dagstuhl Seminar on Efficient Algorithms for Global Optimisation Methods in Computer Vision, Efficiently enforcing topological constraints in random field image segmentation, Dagstuhl, DE.
  • 06/2011 Invited talk: CVPR Workshop on Fine-Grained Visual Categorization, Attribute-based classification for fine-grained categorization, Colorado Springs, CO, USA.
  • 02/2011 Invited talk: Computer Vision Winter Workshop, Structured learning and prediction in computer vision, Mitterberg, AT.
  • ...

  • Scientific Presentations
  • 09/2016 Microsoft Research: Multi-task and lifelong learning with unlabeled tasks, Cambridge, UK.
  • 09/2016 University of Oxford: Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, Oxford, UK.
  • 09/2016 Yandex: Classifier Adaptation at Prediction Time, Moscow, RU.
  • 09/2016 Skoltech: Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, Moscow, RU.
  • 09/2016 Higher School of Economics: Multi-task learning with unlabeled tasks, Moscow, RU.
  • 01/2016 University of Brno: Classifier adaptation as prediction time, Brno, CZ.
  • 01/2015 University of Heidelberg, Image Representations and Learning, Heidelberg, DE.
  • 01/2015 Technical University of Munich, Efficient Training of Structured Prediction Models, Munich, DE.
  • 12/2014 University of Lübeck, Towards lifelong visual learning, Lübeck, DE.
  • 06/2014 University of Marburg, Machine learning for visual scene understanding, Marburg, DE.
  • 05/2014 MPI for Intelligent Systems, Towards lifelong visual learning, Tübingen, DE.
  • 02/2014 Rutgers University, Learning with asymmetric information, New Brunswick, NJ, USA.
  • 02/2014 New York University, Learning with asymmetric information, New York, NY, USA.
  • 02/2014 Memorial Sloan Kettering Cancer Center, Learning with asymmetric information, New York, NY, USA.
  • 02/2014 Massachusetts Institute of Technology, Learning with asymmetric information, Boston, MA, USA.
  • 01/2014 INRIA Rhone-Alpes, Learning with asymmetric information, Grenoble, FR.
  • 10/2013 Schiele Group Retreat, Lifelong learning for visual scene understanding - from practice to theory and back, Schloss Ringberg, DE.
  • 05/2013 University of Illinois at Urbana-Champaign, Attribute-based classification and the dream of lifelong learning for visual scene understanding, Champaign, IL, USA.
  • 05/2013 Carnegie Mellon University, Attribute-based classification and the dream of lifelong learning for visual scene understanding, Pittsburgh, PA, USA.
  • 03/2013 University of California, Berkeley, Attribute-based classification and the dream of lifelong learning for visual scene understanding, Berkeley, CA, USA.
  • 03/2013 University of California, Berkeley, Dynamic pruning of factor graphs and classification without training examples, Berkeley, CA, USA.
  • 03/2013 Stanford University, Attribute-based classification and the dream of lifelong learning for visual scene understanding, Palo Alto, CA, USA.
  • 01/2013 University of Leuven, Attribute-based classification and the dream of lifelong learning for visual scene understanding, Leuven, BE.
  • 10/2012 Xerox Research Center Europe, Semantic attributes for object categorization, Grenoble, FR.
  • 10/2012 INRIA Rhône-Alpes, Predicting binary features for attribute-based and multi-Label classification, Grenoble, FR.
  • 09/2012 University College London, Gatsby Unit, Semantic attributes and classification without training examples, London, UK.
  • 04/2011 University of Oxford, Enforcing topological constraints in random field image segmentation, Oxford, UK.
  • 03/2011 Microsoft Research, Enforcing topological constraints in random field image segmentation, Cambridge, UK.
  • Teaching Activities Scientific Events
  • 09/2016 Lecture series "Probabilistic Graphical Models", Higher School of Economics, Moscow, RU
  • 08/2016 Invited Lectures "Machine Learning for Computer Vision", Vision and Sport Summer School, Prague, CZ.
  • 08-09/2015 Summer Academy of the German National Academic Foundation, Greifswald, DE
  • 08/2015 Invited Lectures "Learning with Structured Inputs and Outputs", Vision and Sports Summer School, Prague, CZ
  • 08/2015 Invited Lectures "Learning with Structured Inputs and Outputs", Microsoft Machine Learning and Intelligence Summer School, Saint Petersburg, RU
  • 08/2014 Invited Lectures "Learning with Structured Inputs and Outputs", Vision and Sports Summer School, Prague, CZ
  • 07/2013 Invited Lectures "Supervised Learning" and "Learning with Structured Inputs and Outputs" INRIA CVML Summer School, Paris, FR.
  • 08/2012 Invited Lecture "Kernel Method in Computer Vision", Vision and Sports Summer School, Prague, CZ.
  • 07/2012 Invited Lecture "Learning with Structured Inputs and Outputs" INRIA CVML Summer School, Grenoble, FR.
  • 06/2012 Short course "Structured Prediction and Learning" with S. Nowozin, CVPR Conference, Providence, RI, USA.
  • 08/2011 Invited Lecture "Learning with Structured Inputs and Outputs" INRIA CVML Summer School, Paris, FR.
  • 07/2011 Invited Lecture "Maximum Margin Learning in Computer Vision", Microsoft Summer School on Computer Vision, Moscow, RU.
  • 06/2011 Short course "Structured Prediction and Learning" with S. Nowozin, CVPR Conference, Colorado Springs, USA.
  • 07/2010 Invited Lecture "Learning with Structured Inputs and Outputs", Visual Recognition and Machine Learning Summer School, Grenoble, FR.
  • 06/2010 Invited Lecture "Learning with Structured Inputs and Outputs", Pattern Recognition and Learning in Multimedia Systems Summer School, Benicassim, ES.
  • 08/2009 Invited Lecture "Kernel Method in Computer Vision", Vision and Sports Summer School, Zurich, CH.
  • 06/2009 Short course, "Kernel Methods in Computer Vision" with M. Blaschko, CVPR Conference, Miami, FL, USA.
  • 07/2008 Tutorial "Kernel Methods", University of Oxford, UK.
  • 06/2008 Tutorial "Kernel Methods for Object Recognition", DAGM Conference, Munich, DE.

    Institute of Science and Technology Austria
  • 02/2017-05/2017: "Data Science and Scientific Computing - Predictive Models"
  • 10/2016-11/2016: "Probabilistic Graphical Models"
  • 02/2016-05/2016: "Data Science and Scientific Computing - Predictive Models"
  • 02/2016-04/2016 "Statistical Machine Learning"
  • 10/2015-02/2016 Project course "Computer Vision and Machine Learning"
  • 02/2014-04/2014 Seminar "Machine Learning and Applications"
  • 02/2014-04/2014 "Image Processing and Analysis"
  • 11/2013-01/2014 "Statistical Machine Learning"
  • 09/2013-11/2013 "Linear Algebra" (with Uli Wagner)
  • 02/2013 Block course "Statistical Machine Learning"
  • 10/2012-12/2012 "Linear Algebra (from a data analysis point of view)
  • 12/2011-02/2012 "Linear Algebra (from a data analysis point of view)
  • 10/2011 Lecture "Computer Vision and Machine Learning" in lecture series "Science at IST Austria"
  • 02/2011 Block course "Statistical Machine Learning"
  • 10/2010 Lecture "Computer Vision and Machine Learning" in lecture series "Science at IST Austria"

    Technical University of Kaiserslautern
  • 04/2006-09/2006 Master Course "Image and Video Processing" with D. Keysers.
  • 10/2005-03/2006 Seminar "Computer Vision and Pattern Recognition".
  • 04/2005-09/2005 Seminar "Computer Gaming".
  • Scientific Services and Peer Reviewing Memberships in Editorial Boards
  • since 09/2015 Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • since 02/2014 Editor for International Journal of Computer Vision (IJCV)
  • since 03/2013 Action Editor for Journal of Machine Learning Research (JMLR)
  • 01/2011-08/2015 Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • 10/2009-09/2012 Guest Editor for International Journal for Computer Vision (IJCV), Special Issue on "Structured Prediction and Inference", with M. Blaschko

    Chair Positions
  • 2016 Area Chair, Symposium on Neural Information Processing Systems (NIPS)
  • 2015 Area Chair, Symposium on Neural Information Processing Systems (NIPS)
  • 2015 Co-Chair, Doctoral Consortium, IEEE Computer Vision and Pattern Recognition (CVPR)
  • 2014 Co-Chair, Tutorials, European Conference on Computer Vision (ECCV)
  • 2014 Area Chair, European Conference on Computer Vision (ECCV)
  • 2014 Area Chair, IEEE Computer Vision and Pattern Recognition (CVPR)
  • 2013 Area Chair, International Conference on Computer Vision (ICCV)
  • 2012 Area Chair, European Conference on Computer Vision (ECCV)


    Workshop Organization
  • 07/2017 Co-organizer Workshop "Continuous and Open-Set Learning" at CVPR 2017 with A. Freytag, T. Boult, J. Denzler
  • 12/2015 Co-organizer (advisory role) Workshop "Transfer and Multi-Task Learning: Trends and New Perspectives" at NIPS 2015
  • 09/2014 Co-organizer "Third International Workshop on Parts and Attributes" at ECCV 2014, with R. Feris and D. Parikh
  • 05/2014 Co-chair "Annual Workshop of the Austrian Society for Pattern Recognition (OAGM)" , with V. Kolmogorov
  • 10/2013 Organizer "Fourth IST Symposium on Computer Vision and Machine Learning", at IST Austria.
  • 10/2012 Co-organizer "Second International Workshop on Parts and Attributes" at ECCV 2012, with R. Feris
  • 10/2012 Organizer "Third IST Symposium on Computer Vision and Machine Learning", at IST Austria.
  • 11/2011 Co-organizer "Workshop on Kernels and Distances in Computer Vision", at ICCV 2011, with B. Kulis and P. Gehler
  • 10/2011 Organizer "Second IST Symposium on Computer Vision and Machine Learning", at IST Austria.
  • 10/2010 Organizer "First IST Symposium on Computer Vision and Machine Learning", at IST Austria.
  • 09/2010 Co-organizer "First International Workshop on Parts and Attributes" at ECCV 2010, with T. Caetano, R. Feris and D. Forsyth at ECCV 2010.
  • 06/2010 Co-organizer "Structured Models in Computer Vision" with P. Gehler and V. Ferrari at CVPR 2010.

    Major Reviewing
  • European Research Council (ERC),
  • German-Israeli Foundation (GIF),
  • scientific journals: IJCV, TPAMI, JMLR, ML, PRL, AURO,
  • scientific conferences: CVPR, ICCV, ECCV, NIPS, ICML, AISTATS, DAGM/GCPR, OAGM, IBPRIA, CVWW.
  • Useless tidbits
    Mathematical Geneology Thanks to the Mathematical Genealogy project I can track my mathematical ancestors back quite a bit: PhD advisor tree
    Erdős Number My Erdős-Number is at most 4. Here's three disjoint paths:
    1. Erdős, Paul; Pach, Janos: On a problem of L. Fejes Toth.. Discrete Math. 30 (1980), no. 2, 103-109.
    2. Edelsbrunner, Herbert; Guibas, Leonidas; Pach, Janos; Pollack, Richard; Seidel, Raimund; Sharir, Micha: Arrangements of curves in the plane - topology, combinatorics, and algorithms. Automata, languages and programming (Tampere, 1988), 214-229, Lecture Notes in Computer Science, 317, Springer, Berlin, 1988.
    3. Chao Chen; Herbert Edelsbrunner: Diffusion runs low on persistence fast, 13th IEEE International Conference on Computer Vision, 2011.
    4. Chao Chen; Daniel Freedman; Christoph H. Lampert: Enforcing topological constraints in random field image segmentation IEEE Computer Vision and Pattern Recognition, 2011.

    1. Erdős, Paul; Ivic, Aleksandar: Estimates for sums involving the largest prime factor of an integer and certain related additive functions. Studia Sci. Math. Hungar. 15 (1980), no. 1-3, 183-199
    2. Hafner, James Lee; Ivic, Aleksandar: On the mean-square of the Riemann zeta-function on the critical line. J. Number Theory 32 (1989), no. 2, 151-191
    3. Niblack, Wayne; Zhu, Xiaoming; Hafner, James L.; Breuel, Tom; Ponceleon, Dulce B.; Petkovic, Dragutin; Flickner, Myron D.; Upfal, Eli; Nin, Sigfredo I.; Sull, Sanghoon; Dom, Byron E.; Yeo, Boon-Lock; Srinivasan, Savitha; Zivkovic, Dan; Penner, Mike: Updates to the QBIC system. Proc. SPIE Vol. 3312, p. 150-161, Storage and Retrieval for Image and Video Databases VI, 1997
    4. anything I published together with Thomas Breuel, e.g.
      Adrian Ulges, Christoph H. Lampert, Thomas M. Breuel: "Document Image Dewarping using Robust Estimation of Curled Text Lines", International Conference on Document Analysis and Recognition (ICDAR), pages 1001-1005, 2005.

    1. Erdős, Paul; Pálfy, Péter Pál; Szedegy, Mario: a(mod p)≤b(mod p) for all primes p implies a=b. . Amer. Math. Monthly 94 (1987), no. 2, 169–170.
    2. Kiltz, Eike; Pietrzak, Krzysztof; Szegedy, Mario: Digital signatures with minimal overhead from indifferentiable random invertible functions. Advances in Cryptology (2013), 571-588
    3. Stefan Dziembowski; Sebastian Faust; Vladimir Kolmogorov; Krzysztof Pietrzak: Proofs of Space. Advances in Cryptology (2015), 585-605
    4. anything I published together with Vladimir Kolmogorov, e.g.
      Shah, Neel; Kolmogorov, Vladimir; Lampert, Christoph H.: A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle. IEEE Conference on Computer Vision and Pattern Recognition (2015)