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Thomas Unterthinerphone: +43-732-2468-4532 e-mail: unterthiner@bioinf... Room: S3 327 (Computer Science building/Science Park 3) |
Research Interests
- Understanding the inner working of deep learning
- Generative Adversarial Networks
- Deep Learning and Neural Networks
- Autonomous Driving
- Drug Target Prediction
- Machine Learning in Bioinformatics
- Machine Learning and Artificial Intelligence
Selected Publications
- Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter: First Order Generative Adversarial Networks, International Conference on Machine Learning (ICML), 2018, link
- Andreas Mayr*, Günter Klambauer*, Thomas Unterthiner*, Marvin Steijaert, Jörg K. Wegner, Hugo Ceulemans, Djork-Arne Clevert, Sepp Hochreiter: Large-scale comparison of machine learning methods for drug target prediction on ChEMBL, Chemical Science, 2018, link
- Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter: Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields, International Conference on Learning Representations (ICLR), 2018, link, code
- Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Sepp Hochreiter: GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium, Advances in Neural Information Processing Systems 30 (NIPS), 2017, link
- Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter: Self-Normalizing Neural Networks, Advances in Neural Information Processing Systems 30 (NIPS), 2017, link
- Djork-Arne Clevert, Thomas Unterthiner, Gundula Povysil, Sepp Hochreiter: Rectified factor networks for biclustering of omics data, Bioinformatics, 2017, link
- Michael Treml*, José Arjona-Medina*, Thomas Unterthiner*, Rupesh Durgesh, Felix Friedmann, Peter Schuberth, Andreas Mayr, Martin Heusel, Markus Hofmarcher, Michael Widrich, Bernhard Nessler, Sepp Hochreiter Speeding up Semantic Segmentation for Autonomous Driving, Machine Learning for Intelligent Transportation Systems, in conjunction with Neural Information Processing Systems (NIPS), 2016, link
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Thomas Unterthiner*, Andreas Mayr*, Günter Klambauer*, Sepp Hochreiter:
Deep Learning for Drug Target Prediction,
GPU Technology Conference Europe (GTC Europe), 2016,
Best Poster Award - Djork-Arne Clevert, Thomas Unterthiner, Sepp Hochreiter: Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs), International Conference on Learning Representations (ICLR), 2016, link
- Andreas Mayr*, Günter Klambauer*, Thomas Unterthiner*, Sepp Hochreiter: DeepTox: Toxicity Prediction using Deep Learning, Frontiers in Environmental Science, 2016, link
- Djork-Arne Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter: Rectified Factor Networks, Advances in Neural Information Processing Systems 28 (NIPS), 2015, link
- Günter Klambauer, Martin Wischenbart, Michael Mahr, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter: Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map, Bioinformatics, 2015, link
- Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Sepp Hochreiter: Toxicity Prediction using Deep Learning, arXiv pre-print, 2015, link
- Günter Klambauer, Bie Verbist, Liesbet Vervoort, Willem Talloen, The QSTAR Consortium, Ziv Shkedy, Olivier Thas, Andreas Bender, Hinrich W.H. Göhlmann, Sepp Hochreiter: Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project, Drug Discovery Today, 2015, link
- Lev Givon, Thomas Unterthiner, N. Benjamin Erichson, David Wei Chiang, Eric Larson, Luke A. Pfister, Sander Dieleman, Gregory R. Lee, Stefan van der Walt, Teodor Mihai Moldovan, Frederic Bastien, Xing Shi, Jan Schlüter, Brian Thomas, Chris Capdevila, Alex Rubinsteyn: scikit-cuda 0.5.1: a Python interface to GPU-powered libraries, 2015, link
- Aakash Chavan Ravindranath, Nolen Perualila-Tan, Adetayo Kasim, Georgios Drakakis, Sonia Liggi, Suzanne C. Brewerton, Daniel Mason, Michael J. Bodkin, David A. Evans, Aditya Bhagwat, Willem Talloen, Hinrich W. H. Göhlmann, QSTAR Consortium, Ziv Shkedy, Andreas Bender: Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis, Molecular BioSystems, 2014, link
- Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Marvin Steijaert, Jörg Wegner, Hugo Ceulemans, Sepp Hochreiter: Deep Learning as an Opportunity in Virtual Screening, Deep Learning and Representation Learning Workshop, in conjunction with Neural Information Processing Systems (NIPS), 2014, PDF
- Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter: DEXUS: Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions, Nucleic Acids Research 41 (21), 2013, link
- Thomas Unterthiner: Evaluating RNA-seq Methods for Differential Gene Expression, Master thesis, 2012
- Thomas Unterthiner, Anne-Kathrin Schultz, Jan Bulla, Burkhard Morgenstern, Mario Stanke, Ingo Bulla: Detection of viral sequence fragments of HIV-1 subfamilies yet unknown. BMC Bioinformatics, 12(1):93, 2011, link
Teaching
- 2018 summer term: Deep Learning and Neural Networks
- 2017 summer term: Deep Learning and Neural Networks
- 2016 summer term: Deep Learning and Neural Networks
- 2015 summer term: Deep Learning and Neural Networks
- 2014 winter term: Exercises in Sequence Analysis and Phylogenetics
- 2014 summer term: Exercises in Theoretical Concepts of Machine Learning
- 2014 summer term: Exercises in Machine Learning: Unsupervised Techniques
- 2013 summer term: Exercises in Theoretical Bioinformatics and Machine Learning
Software
- binet: A Deep Learning framework in Python with GPU support
- librfn: high-performance implementation of Rectified Factor Networks, runs on CPU and GPU
- libfabia: High performance parallel implementation of the FABIA biclustering algorithm
- pyfabia: Cython implementation of FABIA
- Regular contributor to various open source projects such as scikit-learn, PyCUDA, scikit-cuda and others
Education
- 2012 - present: PhD in Computer Science, Johannes Kepler University, Linz
- MSc Computer Science (Diplom-Ingeneur), Johannes Kepler University, Linz, Austria (passed with distinction)
- MSc Bioinformatics, Johannes Kepler University, Linz, Austria (passed with distinction)
- Erasmus Study Abroad, University of Reading, United Kingdom
- BSc Computer Science, Johannes Kepler University, Linz, Austria (passed with distinction)
- High School: Deutschsprachige Handelsoberschule, Bolzano, Italy, Grade: 100/100
Employment Record
- 2012 - present: Research Assistant, Institute of Bioinformatics, Johannes Kepler University, Linz, Austria
- 2010 - 2011: Research Assistant, Department of Mathematics Education, Johannes Kepler University, Linz, Austria
- 2009: Research Assistant, Department of Bioinformatics, Institute for Microbiology and Genetics, University of Göttingen, Germany
- 2009: Summer-Internship as Software Developer at Orgachim ISC, Ruse, Bulgaria
- 2008: Summer-Internship as Software Developer at Würth-Phönix SRL, Bolzano, Italy