Prof. Dr. Vincent Heuveline

Group Leader Data Mining and Uncertainty Quantification

Contact

Phone: +49 6221 – 533 – 312

Fax: +49 6221 – 533 – 298

At HITS gGmbH Prof. Dr. Vincent Heuveline is the leader of the research group Data Mining and Uncertainty Quantification (DMQ). The research group maintains a close relationship with the Heidelberg University. Besides his professorship, Vincent Heuveline is also Director of the Computing Center of Heidelberg University. He also heads a research group at the university, the “Engineering Mathematics and Computing Lab” (EMCL) at the Interdisciplinary Center for Scientific Computing.

Research interests

Short CV

Vincent Heuveline (born 1968) studied Mathematics, Physics and Computer Science at the Universities of Caen (France) and Würzburg (Germany). He received his PhD in Computer Science in 1997 from the Université de Rennes and habilitated in Mathematics at the University of Heidelberg in 2002. Since 2004, he was a professor at Karlsruhe University (KIT) until he moved to Heidelberg in May 2013.

2023

2022

  • Sirazitdinov A, Buchwald M, Hesser J, Heuveline V (2022). Review of Deep Learning Methods for Individual Treatment Effect Estimation with Automatic Hyperparameter Optimization, [Preprint] 1615
  • Møller P, Seppälä T, Dowty JG, Haupt S, Dominguez-Valentin M, Sunde L, Bernstein I, Engel C, Aretz S, Nielsen M, Capella G, Evans DG, Burn J, Holinski-Feder E, Bertario L, Bonanni B, Lindblom A, Levi Z, Macrae F, Winship I, Plazzer J, Sijmons R, Laghi L, Valle AD, Heinimann K, Half E, Lopez-Koestner F, Alvarez-Valenzuela K, Scott RJ, Katz L, Laish I, Vainer E, Vaccaro CA, Carraro DM, Gluck N, Abu-Freha N, Stakelum A, Kennelly R, Winter D, Rossi BM, Greenblatt M, Bohorquez M, Sheth H, Tibiletti MG, Lino-Silva LS, Horisberger K, Portenkirchner C, Nascimento I, Rossi NT, Silva LAd, Thomas H, Zaránd A, Mecklin J, Pylvänäinen K, Renkonen-Sinisalo L, Lepisto A, Peltomäki P, Therkildsen C, Lindberg LJ, Thorlacius-Ussing O, Doeberitz MvK, Loeffler M, Rahner N, Steinke-Lange V, Schmiegel W, Vangala D, Perne C, Hüneburg R, Vargas AFd, Latchford A, Gerdes A, Backman A, Guillén-Ponce C, Snyder C, Lautrup CK, Amor D, Palmero E, Stoffel E, Duijkers F, Hall MJ, Hampel H, Williams H, Okkels H, Lubiński J, Reece J, Ngeow J, Guillem JG, Arnold J, Wadt K, Monahan K, Senter L, Rasmussen LJ, Hest LPv, Ricciardiello L, Kohonen-Corish MRJ, Ligtenberg MJL, Southey M, Aronson M, Zahary MN, Samadder NJ, Poplawski N, Hoogerbrugge N, Morrison PJ, James P, Lee G, Chen-Shtoyerman R, Ankathil R, Pai R, Ward R, Parry S, Dębniak T, John T, Hansen TvO, Caldés T, Yamaguchi T, Barca-Tierno V, Garre P, Cavestro GM, Weitz J, Redler S, Büttner R, Heuveline V, Hopper JL, Win AK, Lindor N, Gallinger S, Marchand LL, Newcomb PA, Figueiredo J, Buchanan DD, Thibodeau SN, Broeke SWt, Hovig E, Nakken S, Pineda M, Dueñas N, Brunet J, Green K, Lalloo F, Newton K, Crosbie EJ, Mints M, Tjandra D, Neffa F, Esperon P, Kariv R, Rosner G, Pavicic WH, Kalfayan P, Torrezan GT, Bassaneze T, Martin C, Moslein G, Ahadova A, Kloor M, Sampson JR, Jenkins MA (2022). Colorectal cancer incidences in Lynch syndrome: a comparison of results from the prospective lynch syndrome database and the international mismatch repair consortium, Hered Cancer Clin Pract 20(1),36 1616
  • Lösel PD, Monchanin C, Lebrun R, Jayme A, Relle J, Devaud J, Heuveline V, Lihoreau M (2022). Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning, biorxiv;2022.10.12.511944v3,[Preprint] 1617
  • Ahadova A, Witt J, Haupt S, Gallon R, Hüneburg R, Nattermann J, Broeke St, Bohaumilitzky L, Hernandez‐Sanchez A, Santibanez‐Koref M, Jackson MS, Ahtiainen M, Pylvänäinen K, Andini K, Grolmusz VK, Möslein G, Dominguez‐Valentin M, Møller P, Fürst D, Sijmons R, Borthwick GM, Burn J, Mecklin J, Heuveline V, Doeberitz MvK, Seppälä T, Kloor M (2022). Is HLA type a possible cancer risk modifier in Lynch syndrome?, Intl Journal of Cancer,ijc.34312 1619
  • Zaunseder E, Haupt S, Mütze U, Garbade SF, Kölker S, Heuveline V (2022). Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review, JIMD Reports 63(3):250-261 1620

2021

2020

2019

2018

  • Kamp Tvd, Schwermann AH, Rolo TdS, Lösel PD, Engler T, Etter W, Faragó T, Göttlicher J, Heuveline V, Kopmann A, Mähler B, Mörs T, Odar J, Rust J, Jerome NT, Vogelgesang M, Baumbach T, Krogmann L (2018). Parasitoid biology preserved in mineralized fossils, Nat Commun 9(1) 353
  • Mexner W, Bonn M, Kopmann A, Mauch V, Ressmann D, Chilingaryan SA, Jerome NT, Kamp TVD, Heuveline V, Lösel P, al. e (2018). OpenGL® API-Based Analysis of Large Datasets in a Cloud Environment, Design and Use of Virtualization Technology in Cloud Computing,pp.161-181,IGI Global 242
  • Seelig T, Meyer A, Gerstner P, Meier M, Jongmanns M, Baumann M, Heuveline V, Egbers C (2018). Dielectrophoretic force-driven convection in annular geometry under Earth’s gravity, arXiv preprint arXiv:1812.05460 348
  • Mexner W, Bonn M, Kopmann A, Mauch V, Ressmann D, Chilingaryan SA, Jerome NT, Kamp Tvd, Heuveline V, Lösel P (2018). OpenGL@API-Based Analysis of Large Datasets in a Cloud Environment, In Design and Use of Virtualization Technology in Cloud Computing, pp. 161–181, IGI Global 349
  • Gawlok S, Heuveline V (2018). Nested Schur-Complement Solver for a Low-Mach Number Model: Application to a Cyclone-Cyclone Interaction, Preprint Series of the Engineering Mathematics and Computing Lab, vol. 0(02) 350
  • John D, Schick M, Heuveline V (2018). Learning model discrepancy of an electric motor with Bayesian inference, Preprint Series of the Engineering Mathematics and Computing Lab, vol. 0(01) 351
  • John D, Schick M, Heuveline V (2018). Bayesian inference for estimating model discrepancy of an electric motor, PAMM, 18(1):1–2 352

2017

2016

  • Schoch N, Kißler F, Stoll M, Engelhardt S, Simone Rd, Wolf I, Bendl R, Heuveline V (2016). Comprehensive patient-specific information preprocessing for cardiac surgery simulations, Int J CARS 11(6):1051-1059 147
  • Bromberger M, Bastian P, Bergeest J, Conrad C, Heuveline V, Rohr K, Karl W (2016). FPGA-accelerated Richardson-Lucy Deconvolution for 3D Image Data, In Proceedings International Symposium on Biomedical Imaging (ISBI), pp. Prague, Czech Republic, Apr 2016 68
  • Bromberger M, Karl W, Heuveline V (2016). Reducing Energy Consumption of Data Transfers using Runtime Data Type Conversion, In Proceedings Conference on Architecture of Computing Systems – ARCS, Nuremberg, Germany, Apr 2016 69
  • Fetzer A, Metzger J, Katic D, März K, Wagner M, Philipp P, Engelhardt S, Weller T, Zelzer S, Franz AM, Schoch N, Heuveline V, Maleshkova M, Rettinger A, Speidel S, Wolf I, Kenngott H, Mehrabi A, Müller-Stich BP, Maier-Hein L, Meinzer H, Nolden M (2016). Towards an open-source semantic data infrastructure for integrating clinical and scientific data in cognition-guided surgery, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations,p.97890O,SPIE 144
  • Kratzke J, Rengier F, Weis C, Beller CJ, Heuveline V (2016). In vitro flow assessment: from PC-MRI to computational fluid dynamics including fluid-structure interaction, Medical Imaging 2016: Physics of Medical Imaging,p.97835C,SPIE 145
  • Lösel P, Heuveline V (2016). Enhancing a diffusion algorithm for 4D image segmentation using local information, Medical Imaging 2016: Image Processing,p.97842L,SPIE 146
  • Schoch N, Philipp P, Weller T, Engelhardt S, Volovyk M, Fetzer A, Nolden M, Simone RD, Wolf I, Maleshkova M, Rettinger A, Studer R, Heuveline V (2016). Cognitive tools pipeline for assistance of mitral valve surgery, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling,p.978603,SPIE 148
  • Schoch N, Speidel S, Sure-Vetter Y, Heuveline V (2016). Towards Semantic Simulation for Patient-Specific Surgery Assistance, Proc. First International Workshop on Surgical Data Science, 1:58-63 149
  • Wlotzka M, Heuveline V, Klatt S, Kraus D, Haas E, Kiese K, Butterbach-Bahl K, Kraft P, Breuer L (2016). Parallel multiphysics simulations using OpenPALM with application to hydro-biogeochemistry coupling, Proc. 6th International Conference on High-Performance Scientific Computing 2015 (LNCS Springer), pp. [in press] 150

2015

  • Hoecker M, Polsterer KL, Kugler SD, Heuveline V (2015). Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties, 2015 IEEE 18th International Conference on Computational Science and Engineering,pp.82-91,IEEE 36
  • Gerstner P, Schick M, Heuveline V (2015). A Multilevel Domain Decomposition approach for solving time constrained Optimal Power Flow problems, Preprint Series of the Engineering Mathematics and Computing Lab, 4:1-32 61
  • Hoecker M, Polsterer KL, Kugler SD, Heuveline V (2015). Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties, In Computational Science and Engineering (CSE), 2015 IEEE 18th International Conference on, pp. 82-91 62
  • Meyer-Hübner N, Suriyah M, Leibfried T, Slednev V, Bertsch V, Fichtner W, Gerstner P, Schick M, Heuveline V (2015). Time Constrained Optimal Power Flow Calculations on the German Power Grid, In Proceedings of International ETG Congress, Bonn 63
  • Song C, Schick M, Heuveline V (2015). A Polynomial Chaos Method for Uncertainty Quantification in Blood Pump Simulation, In Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP) 64
  • Kratzke J, Schick M, Heuveline V (2015). Fluid-Structure Interaction Simulation of an Aortic Phantom with Uncertain Young’s Modulus Using the Polynomial Chaos Expansion, Applied Mechanics and Materials, 807:34-44 65
  • Kratzke J, Schoch N, Weis C, Mueller-Eschner M, Speidel S, Farag M, Beller C, Heuveline V (2015). Enhancing 4D PC-MRI in an aortic phantom considering numerical simulations, In Proceedings of SPIE Medical Imaging 2015: Physics of Medical Imaging 66
  • Schoch N, Engelhardt S, Zimmermann N, Speidel S, Simone RD, Wolf I, Heuveline V (2015). Integration of a biomechanical simulation for mitral valve reconstruction into a knowledge-based surgery assistance system, In Proceedings of SPIE Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling 67
  • Bromberger M, Karl W, Heuveline V (2015). Exploiting approximate computing methods in FPGAs to accelerate stereo correspondence algorithms, In HiPEAC Conference, Amsterdam, Netherlands, Jan 2015 70

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