September 17-21, 2018 at Saarland University, Germany

5th European Data Science Summer School

Prof. Dr.-Ing. Wolfgang Maaß

Prof. Dr. Josef van Genabith


The 5th European Data Science Summer School at Saarland University will take place on September 17-21, 2018.

The Summer School is an intensive hands-on introduction to data science for PhD and master students. It addresses the need for future experts in data science that venture to solve big-data challenges with skills in business, science and computing – “a combination that is still hard to find” (cf. Mattmann, Nature 2013). We will introduce you to basic but also advanced statistics and its application in business and healthcare situations. Lectures are combined with hands-on modules that will help you to understand theoretical principles and how to apply them.

No previous knowledge of the subject is required, although attendants should have a minimal programming skill set. As an introduction or refreshment of these programming skills, we can recommend the courses in JavaScript and Python offered by CodeAcademy.

The European Data Science Summer School is taught by leading scientists from Europe as well as by industrial experts. It is held at Saarland University within the facilities of the Department of Business Administration. To learn more about the European Data Science Summer School, please check the program details. You can also email us to


The 5th European Data Science Summerschool will start in:







Mo Di Mi Do Fr
09:00-10:30 Welcome Note
(President of Saarland U) Introduction (Maaß)
Databases for Big Data (Schuhknecht) Support Vector Machines and Neural Networks (Shcherbatyi) Deep Learning (Borth) Reinforcement Learning (Kudenko)
11:00 - 12:30 Introduction into Statistics (Klößner) Databases for Big Data Hands-On (Schuhknecht) SVM/NN Hands-On (Shcherbatyi) Deep Learning Hands-On (Borth) Reinforcement Learning Hands-On (Kudenko)
lunch lunch lunch lunch lunch
14:00 - 15:30 Linear Regression/ Regularization / KNN (Maaß) Data Science in Bioinformatics (Almeida) Time Series with Recurrent Neural Networks (Shcherbatyi) Visit of DFKI, MPI, CISPA Data Science Methods and Data Science in Industry (Maaß)
16:00 - 17:30 Linear Regression, Regularization, KNN Hands-On (Shcherbatyi) Data Science in Bioinformatics Hands-On (Almeida) RNN Hands-On (Shcherbatyi) Poster Presentations, BBQ Wrap-Up

Speakers 2018

Saarland University

Wolfgang Maaß (Maass) is professor in Business Informatics at Saarland University, scientific director at Deutsche Forschungszentrum für Künstliche Intelligenz (DFKI), and adjunct professor Stony Brook University Health Sciences Center School of Medicine, NY. He studied Computer Science at the RWTH Aachen and the Saarland University. His PhD in Computer Science at the Saarland University was funded by the German National Science Foundation (DFG). He was post-doc researcher at the Institute of Technology Management (ITEM) and Media and Communications Management Institute (MCM) at the University of St. Gallen, Switzerland where he also received his habilitation by he Department of Management. Previously he was lecturer at the University of St. Gallen and professor of media and computer science at Furtwangen University, Germany. He was guest researcher at the National Center for Geographic Information and Analysis (NCGIA), UC Santa Barbara, CA, USA and guest professor at the Department of Bioinformatics and Computational Biology at MD Anderson Cancer Center, University of Texas, TX and at the Department for Biomedical Informatics at Stony Brook University Health Sciences Center School of Medicine, NY. In his research he investigates the transformation of industries by applying methods of Artificial Intelligence.

Saarland University

Josef van Genabith is a Full Professor Full Professor and Chair of Translation-Oriented Language Technologies at the University of Saarland, Germany since 2007. He graduated with the 1st Staatsexamen in Electronic Engineering (Information Technology) and English (Language, Linguistics and Literature) at RWTH Aachen, Germany in 1988, received a Ph.D. in Linguistics 1993 from the University of Essex (U.K.) and worked as a researcher at the University of Essex 1991-1992 and the Institut für Maschinelle Sprachverarbeitung IMS, Universität Stuttgart (Germany) 1992-1996. He joined the School of Computing at Dublin City University (DCU) as a Lecturer in 1996, became Senior Lecturer in 1999 and Associate Professor in 2002. In 2003 he became a Visiting Researcher at IBM’s Dublin Centre for Advanced Studies (CAS) and an IBM Faculty Fellow in 2004. In 2007 he was awarded a € 16.8M Science Foundation Ireland CSET grant (2007-2012) as Founding Director for the Centre for Next Generation Localisation (CNGL).

Stony Brook University

Since 2015 Jonas Almeida is Professor and Chief Technology Officer for Biomedical Informatics at Stony Brook University (State University of NY, Long Island). This followed 4 years as the inaugural director of a new Division in Informatics in the Dept of Pathology of the Univ Alabama at Birmingham (UAB), and 5 years as Professor of Bioinformatics in the Division of Applied Mathematics of the University of Texas MDAnderson Cancer Center (2005-2010). His current research interests are at the intersection of Computational Statistics and Cloud Computing for Bioinformatics application development in the Web Platform. This research pulls together threads from past work on mathematical modeling and machine learning for Medical Genomics, at a time when these fields are challenged by the increasingly data driven nature of modern Biomedical Research. This work is often pursued with public Biomedical Big Data resources such as The Cancer Genome Atlas (TCGA) as reference for Personalized Medicine applications. As Population Health data becomes available in real-time (see for example, /tcgascope or /loadsparcs), a new frontier opens for Machine Learning embedded as Web Computing in the increasingly patient-facing HealthIT enterprise.

German Research Center for Artificial Intelligence (DFKI)

Dr. Damian Borth is the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, the Principle Investigator of the NVIDIA AI Lab at the DFKI, and founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian‘s research focuses on large-scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. His work has been awarded by NVIDIA at GTC Europe 2016, the Best Paper Award at ACM ICMR 2012, the McKinsey Business Technology Award 2011, and a Google Research Award in 2010. Damian currently serves as a member of the steering group at the VolkswagenStiftung, the review committee at the Baden-Wurttemberg Stiftung, the assessment committee for the Investment Innovation Benchmark (IIB) and several other steering- and program committees of international conferences and workshops. He is also a founding member the Financial Data Science Association and scientific director of the Certified Financial Data Science Program at Deutsche Vereinigung fur Finanzanalyse und Asset Management (DVFA). Damian did his postdoctoral research at UC Berkeley and the International Computer Science Institute (ICSI) in Berkeley, where he was involved in big data projects at the Lawrence Livermore National Laboratory. He received his PhD from the University of Kaiserslautern and the German Research Center for Artificial Intelligence (DFKI). During that time, Damian stayed as a visiting researcher at the Digital Video and Multimedia Lab at Columbia University.

Saarland University

Stefan is an expert in financial markets, data science, and econometrics. He gathered over 20 years of experience in those fields with many publications in renowned journals like the Journal of Applied Econometrics, the Journal of Banking and Finance, the Journal of Economic Dynamics and Control, and many others. He also develops - joint with other data scientists - add-on packages for the statistics software R, in particular for the Multivariate Synthetic Control Method Using Time Series, for Computing Spillover Measures, and for the Implementation of the Pearson distribution system. Stefan holds a Ph.D. in Economics and the Venia Legendi in Statistics and Econometrics from the University of Saarland (Germany). He teaches the next generation of data researchers and gives national and international lectures on Data Science. He also obtained a Diploma in Mathematics and a Diploma in Business Administration.

Saint Petersburg Academic University

Daniel Kudenko is professor at the Saint Petersburg National Academic Research University of the Russian Academy of Science and member of the Artificial Intelligence Group at the University of York, UK. Prof. Kudenko received a Diplom in Computer Science from the University of the Saarland, Germany and a PhD in Machine Learning from Rutgers University, USA. His research is focused around machine learning (specifically reinforcement learning), AI for interactive entertainment, user modelling, and human-AI interaction, and he has published over 120 peer-reviewed papers in various areas of artificial intelligence and co-edited three books published by Springer. Prof. Kudenko was a member of the management committee of AgentLink II, the European Network of Excellence on Agent Computing, and coordinator of the inter-network SIG on ìAgents that Learn, Adapt, and Discoverî (AgentLink II , ILPNet, EUnite). He has been a principal and co-investigator on a number projects funded by EPSRC, InnovateUK, and DSTL, and has a strong record in knowledge transfer and collaboration with industry, having worked on many projects with industrial partners, including QinetiQ, Eidos, and Mood International. Prof. Kudenko has been serving on program committees and senior program committees of numerous conferences, including AAMAS, AAAI, IJCAI, ECAI, and ICML.

Saarland University

Felix Martin Schuhknecht is a postdoctoral researcher at the Information Systems chair of Prof. Jens Dittrich in the Computer Science Department of Saarland University, where he finished his Ph.D. studies in 2016. His research focuses on main-memory (adaptive) indexing methods, data partitioning and sorting, storage layouts, and memory management techniques. He has published work in these areas on major conferences, journals, and workshops in the field of databases and information systems, including PVLDB/VLDB, VLDB Journal, CIDR, SIGMOD DaMoN, and BTW. In 2014, he won a VLDB best paper award (the second ever given to an Experiments and Analysis paper) on a study about adaptive indexing. Currently, he is a member of the program committee of ICDE 2017 as well as the VLDB 2017 demo track. In addition, he has served as an external reviewer for several data management conferences including ACM SIGMOD, VLDB, ICDE, BTW, and SOCC.

German Research Center for Artificial Intelligence (DFKI)

Iaroslav Shcherbatyi obtained his Masters Degree in Computer Science at the University of Saarland. His master's thesis won Best Master's Thesis Award at German Conference on Pattern Recognition. He previously worked at Max Planck Institute for Informatics, where he was performing research in areas of mathematical optimization (mainly in context of computer vision), machine learning and human - computer collaboration. As of 2015, he works as a research associate at the chair of Information and Service Systems at Saarland University, where he applies his machine learning and optimization skills to information systems domain.


Participants get 2 ECTS for participating at the 5th European Data Science Summer School. After the Summer School, there is the voluntary chance to prepare a mini-project to get another 1 ECTS. If you intend the preparing of a mini-project, please register for that via email ( until Tuesday 12 September 2018.


Registration The Summer School is an intensive hands-on introduction to data science for doctoral researchers and advanced students. Open places can be given to professionals.

The registration fee for the Summer School includes all sessions, lunch, coffee breaks and the social event. Information about participants fee's will be available soon.

Degree Registration fee
Master students 295.00 Euro;
PhD students 295.00 Euro;
Professionals 695.00 Euro;

Registration for PhD and Master students

To register, please send a message with your name, your course of studies and your current semester to

Registration for professionals

To register, please send an application including CV and motivation letter until 31st of July, 2018 to If you are accepted you will get an email with future information.


The 2018 European Data Science Summer School will be held at Saarland University, in Saarbrücken, Germany.


Hermann Neuberger Sportschule

Sportschule 4
66123 Saarbrücken
Tel. +49(0)681 3879494

Doubleroom: 40 Euro incl. Breakfast per day (for each person)

Europa-Jugendherberge Saarbrücken

Meerwiesertalweg 31
66123 Saarbrücken
Telefon 0681/33040

Doubleroom: 35 Euro incl. Breakfast per day (for each person)

B and B Hotel Saarbrücken Hbf

Europaallee 14
66113 Saarbrücken
Tel. +49 (0)681 793080

Singleroom: 46.5 Euro per day


Best Western Victors Residenz-Hotel

Kalmanstraße 47-51
66113 Saarbrücken
Telefon: +49 (6951) 709505

Singleroom: 94 Euro incl. Breakfast per day


Hotel am Triller

Trillerweg 57
66117 Saarbrücken
+49 (681) 580000

Singleroom: 107.15 Euro incl. Breakfast per day


Some impressions of the European Data Science Summer School from previous years.

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