Günter Klambauer

phone: +43-732-2468-4520
fax: +43-732-2468-4539

e-mail: klambauer@bioinf.jku.at

Room: S3 327 (Computer Science Building, Science Park 3)

Curriculum Vitae: PDF

Consulting hours (Sprechstunde)

Upon email request.

Current

Selected Publications

Publication list

Author	Title	Year	Journal/Proceedings	Reftype	DOI/URL
Akbar, R., Robert, P.A., Weber, C.R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A. and others	In silico proof of principle of machine learning-based antibody design at unconstrained scale	2021	BioRXiV	article
Fürst, A., Rumetshofer, E., Tran, V., Ramsauer, H., Tang, F., Lehner, J., Kreil, D., Kopp, M., Klambauer, G., Bitto-Nemling, A. and others	CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP	2021	arXiv preprint arXiv:2110.11316	article
Hoedt, P.-J., Kratzert, F., Klotz, D., Halmich, C., Holzleitner, M., Nearing, G., Hochreiter, S. and Klambauer, G.	MC-LSTM: Mass-Conserving LSTM	2021	Proceedings of the 38th International Conference on Machine Learning Vol. 139, pp. 4275-4286	article
Kimeswenger, S., Tschandl, P., Noack, P., Hofmarcher, M., Rumetshofer, E., Kindermann, H., Silye, R., Hochreiter, S., Kaltenbrunner, M., Guenova, E. and others	Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns	2021	Modern Pathology Vol. 34(5), pp. 895-903	article
Klotz, D., Kratzert, F., Gauch, M., Sampson, A.K., Klambauer, G., Brandstetter, J., Hochreiter, S. and Nearing, G.	Uncertainty estimation with LSTM based rainfall-runoff models	2021	EGU General Assembly Conference Abstracts, pp. EGU21-13308	inproceedings
Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S. and Nearing, G.	Uncertainty Estimation with Deep Learning for Rainfall--Runoff Modelling	2021	Hydrology and Earth System Sciences Discussions, pp. 1-32	article
Mercado, R., Rastemo, T., Lindelöf, E., Klambauer, G., Engkvist, O., Chen, H. and Bjerrum, E.J.	Graph networks for molecular design	2021	Machine Learning: Science and Technology Vol. 2(2), pp. 025023	article
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Adler, T., Kreil, D., Kopp, M.K., Klambauer, G., Brandstetter, J. and Hochreiter, S.	Hopfield networks is all you need	2021	International Conference on Learning Representations	article
Robert, P.A., Akbar, R., Frank, R., Pavlović, M., Widrich, M., Snapkov, I., Chernigovskaya, M., Scheffer, L., Slabodkin, A., Mehta, B.B. and others	One billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction	2021	BioRXiV	article
Roland, T., Boeck, C., Tschoellitsch, T., Maletzky, A., Hochreiter, S., Meier, J. and Klambauer, G.	Machine Learning based COVID-19 Diagnosis from Blood Tests with Robustness to Domain Shifts	2021	medRxiv	article
Schimunek, J., Friedrich, L., Kuhn, D., Hochreiter, S., Rippmann, F. and Klambauer, G.	Comparative assessment of interpretability methods of deep activity models for hERG	2021	19th International Workshop on (Q)SAR in Environmental and Health Sciences	conference
Schimunek, J., Friedrich, L., Kuhn, D., Rippmann, F., Hochreiter, S. and Klambauer, G.	A generalized framework for embedding-based few-shot learning methods in drug discovery	2021	ELLIS Machine Learning for Molecule Discovery Workshop	conference	PDF
Seidl, P., Halmich, C., Mayr, A., Vall, A., Ruch, P., Hochreiter, S. and Klambauer, G.	Benchmarking recent Deep Learning methods on the extended Tox21 data set	2021	19th International Workshop on (Q)SAR in Environmental and Health Sciences	conference
Seidl, P., Renz, P., Dyubankova, N., Neves, P., Verhoeven, J., Segler, M., Wegner, J.K., Hochreiter, S. and Klambauer, G.	Modern Hopfield Networks for Few-and Zero-Shot Reaction Template Prediction	2021	arXiv preprint arXiv:2104.03279	article
Vall, A., Hochreiter, S. and Klambauer, G.	BioassayCLR: Prediction of biological activity for novel bioassays based on rich textual descriptions	2021	ELLIS Machine Learning for Molecule Discovery Workshop	conference	PDF
Vall, A., Sabnis, Y., Shi, J., Class, R., Hochreiter, S. and Klambauer, G.	The promise of AI for DILI prediction	2021	Frontiers in Artificial Intelligence Vol. 4, pp. 15	article
Adler, T., Brandstetter, J., Widrich, M., Mayr, A., Kreil, D., Kopp, M., Klambauer, G. and Hochreiter, S.	Cross-Domain Few-Shot Learning by Representation Fusion	2020	arXiv preprint arXiv:2010.06498	article
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B. and Hochreiter, S.	Two time-scale update rule for training GANs	2020		misc
Hofmarcher, M., Mayr, A., Rumetshofer, E., Ruch, P., Renz, P., Schimunek, J., Seidl, P., Vall, A., Widrich, M., Hochreiter, S. and others	Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks	2020	Available at SSRN 3561442	techreport	DOI
Klotz, D., Kratzert, F., Sampson, A.K., Klambauer, G., Hochreiter, S. and Nearing, G.	Learning from mistakes: Online updating for deep learning models.	2020	EGU General Assembly Conference Abstracts, pp. 8853	inproceedings
Kratzert, F., Klotz, D., Klambauer, G., Nearing, G. and Hochreiter, S.	The performance of LSTM models from basin to continental scales	2020	Copernicus Meetings	techreport
Kratzert, F., Klotz, D., Shalev, G., Nevo, S., Klambauer, G., Nearing, G. and Hochreiter, S.	Towards deep learning based flood forecasting for ungauged basins	2020	EGU General Assembly Conference Abstracts, pp. 8932	inproceedings
Kratzert, F., Klotz, D., Shalev, G., Nevo, S., Klambauer, G., Nearing, G. and Hochreiter, S.	Towards deep learning based flood forecasting for ungauged basins	2020	Copernicus Meetings	techreport
Mayr, A., Klambauer, G., Unterthiner, T. and Hochreiter, S.	The LSC Benchmark Dataset: Technical Appendix and Partial Reanalysis	2020	LIT AI Lab & Institute for Machine Learning, Johannes Kepler University Linz	techreport
Mercado, R., Rastemo, T., Lindelöf, E., Klambauer, G., Engkvist, O., Chen, H. and Bjerrum, E.J.	Practical notes on building molecular graph generative models	2020	Applied AI Letters Vol. 1(2)	article
Nearing, G., Kratzert, F., Klotz, D., Hoedt, P.-J., Klambauer, G., Hochreiter, S., Gupta, H., Nevo, S. and Matias, Y.	A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling	2020	AI for Earth Sciences Workshop at NEURIPS	inproceedings
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G.K., Greiff, V. and others	Hopfield networks is all you need	2020	arXiv preprint arXiv:2008.02217	article
Renz, P., Van Rompaey, D., Wegner, J.K., Hochreiter, S. and Klambauer, G.	On failure modes in molecule generation and optimization	2020	Drug Discovery Today: Technologies	article
Sturm, N., Mayr, A., Le Van, T., Chupakhin, V., Ceulemans, H., Wegner, J., Golib-Dzib, J.-F., Jeliazkova, N., Vandriessche, Y., Böhm, S. and others	Industry-scale application and evaluation of deep learning for drug target prediction	2020	Journal of Cheminformatics Vol. 12(1), pp. 1-13	article	DOI
Widrich, M., Schäfl, B., Pavlović, M., Sandve, G.K., Hochreiter, S., Greiff, V. and Klambauer, G.	DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning	2020	bioRxiv	article	DOI
Widrich, M., Schäfl, B., Ramsauer, H., Pavlović, M., Gruber, L., Holzleitner, M., Brandstetter, J., Sandve, G.K., Greiff, V., Hochreiter, S. and others	Modern Hopfield networks and attention for immune repertoire classification	2020	arXiv preprint arXiv:2007.13505	article
Hofmarcher, M., Rumetshofer, E., Clevert, D.-A., Hochreiter, S. and Klambauer, G.	Accurate prediction of biological assays with high-throughput microscopy images and convolutional networks	2019	Journal of chemical information and modeling Vol. 59(3), pp. 1163-1171	article	DOI
Hofmarcher, M., Unterthiner, T., Arjona-Medina, J., Klambauer, G., Hochreiter, S. and Nessler, B.	Visual scene understanding for autonomous driving using semantic segmentation	2019	Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 285-296	incollection	DOI
Kimeswenger, S., Klambauer, G., Lang, G., Hofmarcher, M., Tschandl, P., Sinz, C., Petronio, G., Silye, R., Kittler, H. and Hötzenecker, W.	Neural networks detect cutaneous basal cell carcinomas in histological sections (452)	2019	Journal of Investigative Dermatology Vol. 139(9), pp. S292	article
Kimeswenger, S., Rumetshofer, E., Hofmarcher, M., Tschandl, P., Kittler, H., Hochreiter, S., Hötzenecker, W. and Klambauer, G.	Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images	2019	Workshop on Machine Learning for Health (ML4H at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.	article
Kimeswenger, S., Klambauer, G., Lang, G., Silye, R. and Hotzenecker, W.	The power of neural networks to detect cutaneous basal cell carcinomas in histological sections	2019	Vol. 28(3)Experimental Dermatology, pp. E38-E38	inproceedings
Klotz, D., Kratzert, F., Herrnegger, M., Hochreiter, S. and Klambauer, G.	Towards the quantification of uncertainty for deep learning based rainfall-runoff models.	2019	Vol. 21Geophysical Research Abstracts	inproceedings
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S. and Nearing, G.	Benchmarking a catchment-aware Long Short-Term Memory Network (LSTM) for large-scale hydrological modeling	2019	arXiv preprint arXiv:1907.08456	article	DOI
Kratzert, F., Klotz, D., Klambauer, G., Hochreiter, S. and Nearing, G.S.	Large-Scale Rainfall-Runoff Modeling using the Long Short-Term Memory Network	2019	AGU Fall Meeting 2019	inproceedings
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S. and Klambauer, G.	NeuralHydrology--Interpreting LSTMs in Hydrology	2019	Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 347-362	incollection	DOI
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S. and Nearing, G.	Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets.	2019	Hydrology & Earth System Sciences Vol. 23(12)	article	DOI
Kratzert, F., Klotz, D., Herrnegger, M., Hochreiter, S. and Klambauer, G.	Using large data sets towards generating a catchment aware hydrological model for global applications.	2019	Vol. 21Geophysical Research Abstracts	inproceedings
Nearing, G., Kratzert, F., Klotz, D., Klambauer, G. and Hochreiter, S.	Large-Scale Rainfall-Runoff Modeling using the Long Short-Term Memory Network	2019	AGUFM Vol. 2019, pp. H53B-05	article
Preuer, K., Klambauer, G., Rippmann, F., Hochreiter, S. and Unterthiner, T.	Interpretable deep learning in drug discovery	2019	Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp. 331-345	incollection	DOI
Renz, P., Hochreiter, S. and Klambauer, G.	Uncertainty Estimation Methods to Support Decision-Making in Early Phases of Drug Discovery	2019	Workshop on Safety and Robustness in Decision-making at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada.	inproceedings	[PDF]
Vondra, S., Kunihs, V., Eberhart, T., Eigner, K., Bauer, R., Haslinger, P., Haider, S., Windsperger, K., Klambauer, G., Schütz, B. and others	Metabolism of cholesterol and progesterone is differentially regulated in primary trophoblastic subtypes and might be disturbed in recurrent miscarriages	2019	Journal of lipid research Vol. 60(11), pp. 1922-1934	article	DOI
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S. and Klambauer, G.	Do internals of neural networks make sense in the context of hydrology?	2018	AGUFM Vol. 2018, pp. H13B-06	article
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J.K., Ceulemans, H., Clevert, D.-A. and Hochreiter, S.	Large-scale comparison of machine learning methods for drug target prediction on ChEMBL	2018	Chemical science Vol. 9(24), pp. 5441-5451	article	DOI
Hoedt, P.J., Hochreiter, S., and Klambauer, G.	Characterising activation functions by their backward dynamics around forward fixed points	2018	Critiquing and Correcting Trends in Machine Learning NeurIPS 2018 Workshop	article	[PDF]
Povysil, G., Tzika, A., Vogt, J., Haunschmid, V., Messiaen, L., Zschocke, J., Klambauer, G., Hochreiter, S. and Wimmer, K.	panelcn. MOPS: CNV detection in targeted NGS panel data for clinical diagnostics	2018	Vol. 26EUROPEAN JOURNAL OF HUMAN GENETICS, pp. 698-698	inproceedings
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S. and Klambauer, G.	Fréchet ChemblNet Distance: A metric for generative models for molecules	2018	arXiv preprint arXiv:1803.09518	article
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S. and Klambauer, G.	Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery	2018	Journal of chemical information and modeling Vol. 58(9), pp. 1736-1741	article	DOI
Preuer, K., Lewis, R.P., Hochreiter, S., Bender, A., Bulusu, K.C. and Klambauer, G.	DeepSynergy: predicting anti-cancer drug synergy with Deep Learning	2018	Bioinformatics Vol. 34(9), pp. 1538-1546	article	DOI
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S. and Klambauer, G.	Fréchet ChemNet distance: a metric for generative models for molecules in drug discovery	2018	Journal of chemical information and modeling Vol. 58(9), pp. 1736-1741	article	DOI
Rumetshofer, E., Hofmarcher, M., Röhrl, C., Hochreiter, S. and Klambauer, G.	Human-level protein localization with convolutional neural networks	2018	International Conference on Learning Representations	inproceedings
Simm, J., Klambauer, G., Arany, A., Steijaert, M., Wegner, J.K., Gustin, E., Chupakhin, V., Chong, Y.T., Vialard, J., Buijnsters, P. and others	Repurposed high-throughput image assays enables biological activity prediction for drug discovery	2018	Cell Chemical Biology, pp. 108399	article
Sturm, N., Sun, J., Vandriessche, Y., Mayr, A., Klambauer, G., Carlsson, L., Engkvist, O. and Chen, H.	Application of bioactivity profile-based fingerprints for building machine learning models	2018	Journal of chemical information and modeling Vol. 59(3), pp. 962-972	article	DOI
Unterthiner, T., Nessler, B., Seward, C., Klambauer, G., Heusel, M., Ramsauer, H. and Hochreiter, S.	Coulomb GANs: Provably optimal nash equilibria via potential fields	2018	International Conference on Learning Representations 2018	article
Eigner, K., Filik, Y., Mark, F., Schütz, B., Klambauer, G., Moriggl, R., Hengstschläger, M., Stangl, H., Mikula, M. and Röhrl, C.	The unfolded protein response impacts melanoma progression by enhancing FGF expression and can be antagonized by a chemical chaperone	2017	Scientific reports Vol. 7(1), pp. 1-12	article	DOI
Klambauer, G., Unterthiner, T., Mayr, A. and Hochreiter, S.	Self-Normalizing Neural Networks	2017	Advances in Neural Information Processing Systems 30, pp. 972-981	inproceedings
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G. and Hochreiter, S.	GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium	2017	https://arxiv.org/abs/1706.08500v4	article	[PDF]
Povysil, G., Tzika, A., Vogt, J., Haunschmid, V., Messiaen, L., Zschocke, J., Klambauer, G., Hochreiter, S. and Wimmer, K.	panelcn. MOPS: Copy-number detection in targeted NGS panel data for clinical diagnostics	2017	Human mutation Vol. 38(7), pp. 889-897	article	DOI
Wittwehr, C., Aladjov, H., Ankley, G., Byrne, H.J., de Knecht, J., Heinzle, E., Klambauer, G., Landesmann, B., Luijten, M., MacKay, C. and others	How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology	2017	Toxicological Sciences Vol. 155(2), pp. 326-336	article	DOI
De Troyer, E., Sengupta, R., Martin Otava, J.D., Zhang, S.K., Culhane, A., Gusenleitner, D., Gestraud, P., Csardi, G., Hochreiter, S., Klambauer, G. and others	The biclustGUI Shiny App	2016		manual
Eigner, K., Filik, Y., Klambauer, G., Swoboda, A., Moriggl, R., Stangl, H., Mikula, M. and Roehrl, C.	Impact of endoplasmic reticulum stress on melanoma malignancy	2016	Vol. 26Melanoma Research, pp. E37-E37	inproceedings
Mayr, A., Klambauer, G., Unterthiner, T. and Hochreiter, S.	DeepTox: toxicity prediction using deep learning	2016	Frontiers in Environmental Science Vol. 3, pp. 80	article	DOI
Sharma, N., Pulito, C., Klambauer, G., Mattoli, L., Strano, S., Blandino, G., Lucci, J. and Bender, A.	Anti-mesothelioma mechanism of action studies of a complex Cynara scolymus fraction using in silico target prediction and gene expression profiling	2016	Planta Medica Vol. 82(S 01), pp. P73	article	DOI
Eduati, F., Mangravite, L.M., Wang, T., Tang, H., Hochreiter, S., Klambauer, G., Mayr, A., Rusyn, I., Wright, F.A., Stolovitzky, G. and others	Prediction of human population responses to toxic compounds by a collaborative competition	2015	Nature biotechnology	article	DOI
Klambauer, G., Wischenbart, M., Mahr, M., Unterthiner, T., Mayr, A. and Hochreiter, S.	Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map	2015	Bioinformatics Vol. 31(20), pp. 3392-3394	article
Klambauer, G., Verbist, B., Vervoort, L., Talloen, W., TheQSTARConsortium, Shkedy, Z., Thas, O., Bender, A., Göhlmann, H.W. and Hochreiter, S.	Using transcriptomics to guide lead optimization in drug discovery projects	2015	Drug Discovery Today Vol. 20(5), pp. 505-513	article
Martin Heusel Djork-Arné Clevert, G.K.A.M.K.S.T.U.S.H.	ELU-Networks - Fast and Accurate CNN Learning on ImageNet	2015	International Conference on Computer Vision (ICCV 2015)	inproceedings
Unterthiner, T., Mayr, A., Klambauer, G. and Hochreiter, S.	Toxicity prediction using deep learning	2015	arXiv preprint arXiv:1503.01445	article
Unterthiner, T., Mayr, A., Klambauer, G., Steijaert, M., Wegner, J.K., Ceulemans, H. and Hochreiter, S.	Aiding Drug Design with Deep Neural Networks	2014	Workshop on Machine Learning in Computational Biology, NIPS 2014	inproceedings
Unterthiner, T., Mayr, A., Klambauer, G., Steijaert, M., Wegner, J.K., Ceulemans, H. and Hochreiter, S.	Deep learning as an opportunity in virtual screening	2014	Deep Learning and Representation Learning Workshop, NIPS 2014	inproceedings
Unterthiner, T., Mayr, A., Klambauer, G., Steijaert, M., Wegner, J.K., Ceulemans, H. and Hochreiter, S.	Multi-task deep networks for drug target prediction	2014	Workshop on Transfer and Multi-Task Learning, NIPS 2014	inproceedings
Bender, A., Göhlmann, H., Hochreiter, S. and Shkedy, Z.	Computational Methods Aiding Early-Stage Drug Design (Dagstuhl Seminar 13212)	2013	Vol. 3(5)Dagstuhl Reports	inproceedings
Clevert, D.-A., Mayr, A., Mitterecker, A., Klambauer, G., Valsesia, A., Forner, K., Tuefferd, M., Talloen, W., Wojcik, J., Göhlmann, H. and others	Increasing the discovery power of-omics studies	2013	Systems Biomedicine Vol. 1(2), pp. 84-93	article	DOI
Klambauer, G.	Package ‘cn. mops’	2013		manual
Klambauer, G., Unterthiner, T. and Hochreiter, S.	DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions	2013	Nucleic acids research Vol. 41(21), pp. e198	article	DOI
Klambauer, G., biocViews Sequencing, R. and GeneExpression, D.	Package ‘dexus’	2013		manual
Clevert, D.-A., Heusel, M., Mitterecker, A., Talloen, W., Göhlmann, H., Wegner, J., Mayr, A., Klambauer, G. and Hochreiter, S.	Exploiting the Japanese toxicogenomics project for predictive modelling of drug toxicity	2012	CAMDA Vol. 2012, pp. 26-9	article
Karlsson, J., Torreno, O., Ramet, D., Klambauer, G., Cano, M. and Trelles, O.	Enabling large-scale bioinformatics data analysis with cloud computing	2012	2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, pp. 640-645	inproceedings
Klambauer, G., Schwarzbauer, K., Mayr, A., Clevert, D.-A., Mitterecker, A., Bodenhofer, U. and Hochreiter, S.	cn. MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate	2012	Nucleic acids research Vol. 40(9), pp. e69	article	DOI
Mahr, M. and Klambauer, G.	Package ‘Rchemcpp’	2012		manual
Clevert, D.-A., Mitterecker, A., Mayr, A., Klambauer, G., Tuefferd, M., Bondt, A.D., Talloen, W., Göhlmann, H. and Hochreiter, S.	cn. FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate	2011	Nucleic acids research Vol. 39(12), pp. e79-e79	article
Klambauer, G., Schwarzbauer, K., Mayr, A. and Hochreiter, S.	A normalization technique for next generation sequencing experiments	2010	Nature Precedings, pp. 1-1	article	DOI
Schwarzbauer, K., Klambauer, G., Mayr, A. and Hochreiter, S.	Identifying Copy Number Variations based on Next Generation Sequencing Data by a Mixture of Poisson Model	2010	Nature Precedings, pp. 1-1	article	DOI

Scientific Challenges

Tox21 Data Challenge (2014): Winner of the Grand Challenge, Nuclear Receptor Panel, Stress Response Panel, and six of twelve subchallenges. https://tripod.nih.gov/tox21/challenge/leaderboard.jsp https://ncats.nih.gov/news/releases/2015/tox21-challenge-2014-winners (Teams ``Bioinf@JKU'' and ``Bioinf@JKU-ensembleX'', Method DeepTox)
NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge (2013): Best performing method at the prediction of average cytotoxicity. https://www.synapse.org/#!Synapse:syn1761567/wiki/60840 (Team ``Austria'')

Supervised Master Theses

Backward dynamics of self-normalizing networks, Pieter-Jan Hoedt, PDF
Generative RNN models for molecular strings with biological activity profiles, ongoing
Generative adversarial networks for molecular graphs, ongoing
Improving image-based compound activity prediction with convolutional neural networks, ongoing
Deep Learning for Drug Combination Synergy Prediction
The Maximum Common Subgraph Kernel For Predicting Kinase Inhibitors
Panelcn.MOPS reaches clinical standards as a copy number variation detection tool for targeted panel sequencing
Accurate detection of tumor copy number variations in high-throughput sequencing data
Detecting CNVs in the 1000 Genomes Project Data Using cn.MOPS and Relating the Results to Transcriptome Sequencing Data

Education and personal data

Since 2014: Post-Doc Researcher at Johannes Kepler University Linz
2014: Award of Excellence of the Austrian Ministry of Science
04/2014: PhD in Bioinformatics (JKU Linz)
2012: Austrian Life Science Award
2009-2014: Researcher at Johannes Kepler University Linz
2007-2009: Secondary School Teacher, BORG Linz and BORG Bad Leonfelden
07/2007: MSc in Mathematics and Biology (University of Vienna)
09/2006-02/2007: Studies Abroad, Universita degli Studi, Padova
2001-2007: Mathematics and Biology Diploma-Studies, University of Vienna

Research Topics

Deep Learning
Self-normalizing neural networks
Convolutional neural networks
Recurrent neural networks and reinforcement learning
Machine learning methods for drug discovery
Life science data analysis and bioinformatics

Teaching

2020: Deep Learning and Neural Nets II, Lecturer, Johannes Kepler University, Linz, Austria.
2020: Artificial Intelligence in Life Sciences, Lecturer, Johannes Kepler University, Linz, Austria.
2019: Deep Learning and Neural Nets I, Lecturer, Johannes Kepler University, Linz, Austria.
2019: Sequence analysis and phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
2019: Introduction to Machine Learning, Lecturer, Johannes Kepler University, Linz, Austria.
2019: Artificial Intelligence in Life Sciences, Lecturer, Johannes Kepler University, Linz, Austria.
2018: Sequence analysis and phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
2018: Introduction to Machine Learning, Lecturer, Johannes Kepler University, Linz, Austria.
2018: Artificial Intelligence in Life Sciences, Lecturer, Johannes Kepler University, Linz, Austria.
2017: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
2016: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
2014: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
2013: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
2012/2013: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
2012: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
2011/2012: Exercises in Bioinformatics I: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
2011: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
2010/2011: Exercises in Bioinformatics I: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
2010: Special Topics on Bioinformatics: Population genetics, Lecturer, Johannes Kepler University, Linz, Austria.
2009/2010: Exercises in Bioinformatics I: Sequence Analysis and Phylogenetics, Lecturer, Johannes Kepler University, Linz, Austria.
2008-2009: Mathematics and Biology, Secondary School Teacher, BORG Linz, Linz
2007-2008: Mathematics and Biology, Secondary School Teacher, BORG Bad Leonfelden, Bad Leonfelden

Trivia: My Erdös number is 4 and my Bacon index is also 4 (see here).