Applied Data Science - Lessons Learned for the Data-Driven Business

Braschler, Stadelmann, Stockinger (Eds.)

Springer, 2019

Companion website to the book Applied Data Science - Lessons Learned for the Data-Driven Business.

Update Mar 12, 2019: Final “good for print” proofreading until Mar 21.

Update Jan 29, 2019: Cover design is drafted and ISBN is out (978-3-030-11820-4). Pre-order available e.g. at Amazon.

Update Dec 07, 2018: Production started.

Update Sep 28, 2018: The manuscript has been submitted to the publisher.

Book cover


While Data Science is somewhat a “hype topic” these days, and numerous books on the topic have been published recently, there is little literature that actually addresses the applied side of Data Science - which is, as we argue, where discussion of Data Science should actually start: as a discipline that blends and merges a diverse set of well-established research fields, Data Science is all about finding the right synergy to build exciting (and efficient) data products for projects both in academia and industry.

This volume highlights Data Science as something that is real, where technology is deployed in data-intensive projects, experiences are collected and lessons are learned. The book is clearly positioned as complementary to textbooks that cover the theoretical fundamentals of Data Science. While we start the book by including a “big picture” overview of the field Data Science, this overview is not intended to compete with the deep literature that exists for the fundamental research fields that underlie the discipline. Rather, by discussing the glue between these fields, we enable the reader to appreciate the discussion in the remainder of the book, which presents Data Science applications (the “Data Products”, or the use cases of data-driven businesses). This second part is the “meat” of the book: a number of chapters in collaboration has been co-developed with authors from academia and industry, where technology transfer in practice is described.

The book adopts the view that Data Science is a unique blend of skills from analytics, engineering & communication aiming at generating value from the data itself. It is inherently applied and interdisciplinary. The following skill set map of a data scientists gives an overview of this blend:

Data science skill set map

Target group

The book aims at learners and professionals in the academic and business sector:

  • Professionals that are looking for applied data sciene or are working in the role of Data Scientist
  • Decision-makers working in companies that are data-driven businesses
  • Students that want to broaden their understanding of the disciplines that underlie data science and their application
  • Researchers in specific areas of DS wanting to see the bigger picture

Preliminary table of contents

Part I - The big picture

Part I introcuces the main topic: What is applied data science? What is a data product? And what traits do successful data scientists have? It then approaches important aspects of societal integration of data science, namely legal aspects as well as risks and side effects.

Part II - Nuts & bolts of Data Science

Part II is a curated collection of guest contributions emphasizing the “lessons learned” in various data science analyses. It covers topics such as methodical aspects (e.g. text analysis, machine learning, visualization, small/big data or modern analysis workflows), application fields (such as industry 4.0, finance, life sciences, health or security) and overarching principles (like what is great DS research? What are legal and ethical implications of data science?).

  • What is Data Science? by Brodie
  • On developing Data Science by Brodie
  • The ethics of Big Data applications in the consumer sector by Christen et al.
  • Statistical Modelling by Ruckstuhl & Dettling
  • Beyond ImageNet - Deep Learning in Industrial Practice by Stadelmann et al.
  • The Beauty of Small Data – an Information Retrieval Perspective by Braschler
  • Narrative Visualization of Open Data by Ackermann & Stockinger
  • Security of Data Science and Data Science for Security by Tellenbach et al.
  • Online Anomaly Detection over Big Data Streams by Rettig et al.
  • Modeling and Simulation for Complexity Management in Business Operations by Hollenstein et al.
  • Data Warehousing and Exploratory Analysis for Market Monitoring by Geiger & Stockinger
  • Mining Human Behavior Datasets for Insight, Prediction, and Planning by Leidig & Wolffe
  • Economic Measures of Forecast Accuracy for Demand Planning - A Case-Based Discussion by Ott et al.
  • Large-Scale Data-Driven Financial Risk Assessment by Breymann et al.
  • Governance and IT Architecture by Bignens et al.
  • Image Analysis at Scale for Finding the Links between Structure and Biology by Mader

Part III - Lessons learned

Part III, written by the editors, reflects on the major data science technologies and their relations to the subdisciplines and application fields, together with a summary of the “lessons learned” from all contributed chapters. Based on these, the chapter Lessons Learned from Challenging Data Science Case Studies highlights future directions for the field and society at large.

About the authors

Editors’ background

At ZHAW, the Institute of Applied Information Technology has covered topics at the interfaces of some of the fields that now get subsumed under “Data Science” as early as 2005. The three editors of this volume are among the founders of the ZHAW Datalab, a Data Science Research Institute (established in 2013) that provides the umbrella for leveraging these competences. Located at a University of Applied Sciences, the projects of the Datalab are geared towards technology transfer and always include industrial partners. Datalab has started a series of events called the “Swiss Conference on Data Science” since 2014 that has been a community builder in the country and has grown into an industrial-academic conference. Again, the event has a focus on technology transfer and attracts considerable industrial participation. This background brings us in a position to propose a book on the applied aspects of data science, based on real-world use cases with contributions both from academia and industry

Martin Braschler Prof. Dr. Martin Braschler is deputy director of the Institute of Applied Information Technology (InIT) at Zurich University of Applied Sciences and leads the Information Engineering research group. His main research interests are in the field of information retrieval (IR) evaluation, multilingual IR, and natural language processing. He is well-known in the academic community as one of the original initiators of the CLEF campaigns for IR evaluation. Drawing from a strong background in industry, Martin Braschler also has extensive experience in the transfer of IR technology to the commercial marketplace.

Thilo Stadelmann Prof. Dr. Thilo Stadelmann is professor of computer science at ZHAW School of Engineering in Winterthur. He received his doctor of science degree from Marburg University in 2010, where he worked on multimedia analysis and voice recognition. Thilo joined the automotive industry for 3 years prior to switching back to academia. His current research focuses on applications of machine learning, especially deep learning, to diverse kinds of data. He is head of the ZHAW Datalab and vice president of SGAICO, the Swiss Group for Artificial Intelligence and Cognitive Sciences.

Kurt Stockinger Prof. Dr. Kurt Stockinger is a Professor of Computer Science and Director of Studies in Data Science at Zurich University of Applied Sciences. His research focuses on Data Science, i.e. Big Data, data warehousing, business intelligences and advanced analytics. He is also on the Advisory Board of Callista Group AG. Previously Kurt Stockinger worked at Credit Suisse in Zurich, Switzerland, at Lawrence Berkeley National Laboratory in Berkeley, California, at California Institute of Technology, California as well as at CERN in Geneva, Switzerland. He holds a Ph.D. in computer science from CERN / University of Vienna.

Contributing authors’ biographies

The following list of authors, in alphabetical order, contributes to the book:

Philipp Ackermann Dr. Philipp Ackermann is lecturer and researcher in Visual Computing, Human Computer Interaction, and eHealth at the School of Engineering of the Zurich University of Applied Sciences (ZHAW). Before he became member of ZHAW in 2013 he founded and lead for 17 years Perspectix (, a software company specialized in 3D product configuration. He received his PhD in 1995 from the University of Zurich where he was research assistant at the MultiMedia Lab. He is an active developer of iOS and Mac apps (e.g., ArtistInfo and ARchi VR,

Mohammadreza Amirian Mohammadreza Amirian received the Master degree in Communications Technology from Ulm University, Ulm, Germany, in 2017. He is currently working as a researcher at the Institute of Applied Information Technology (InIT) of Zurich University of Applied Sciences (ZHAW), Winterthur, Switzerland, and simultaneously pursuing a Ph.D. degree at Ulm University. Besides his research interests in bio-physiological signal processing for person-centered medical and affective pattern recognition, his current research focuses on robust deep learning algorithms for industrial applications in quality assessment and learning to learn.

Serge Bignens Prof. Serge Bignens is Professor and Head of the Institute for Medical Informatics at the Bern University of Applied Sciences (BFH) and board member of the Swiss Society of Medical Informatics. His research interests comprise Personal Data Ownership and Governance, Personal Health Records, mHealth Applications, Data Protection and Data Security and Citizen Science. He holds a Master of Science after having studied at the EPFL, Lausanne, Switzerland and Carnegie Mellon University (CMU), Pittsburgh, USA and a Master of Advanced Studies in Health Economics and Management from the University of Lausanne. He co-founded, an not for profit cooperative building and operating a secured IT platform allowing citizens to manage their personal health data and share part of it for defined clinical studies.

Helene Blumer Helene Blumer obtained her Master of Science in Business Administration at the University of Applied Sciences HTW Chur. Working as a Research Associate at the Swiss Institute for Entrepreneurship (SIFE), she is involved in various research and development projects in the field of corporate responsibility, focusing primarily on ethical considerations in the context of big data, internal reporting systems of Swiss businesses, and compliance dialogues along the supply chain.

Richard Bödi Richard Bödi is Lecturer and Head of the Bachelor Programme in Business Engineering at the Zurich University of Applied Sciences (ZHAW). Before joining ZHAW, he was a Research Staff Member at the IBM Research Lab in Rüschlikon for 12 years working on optimization problems and forecasting algorithms in the area of logistics and warehousing. He holds a PhD and a Habilitation in Mathematics from University of Tuebingen, Germany (1992/1996).

Wolfgang Breymann Prof. Dr. Wolfgang Breymann is head of the group Finance, Risk Management and Econometrics at Zurich University of Applied Sciences, Institute of Data Analysis and Process Design, which he shaped by developing the research activities in financial markets and risk. He is one of the originators of project ACTUS for standardizing financial contract modelling and member of the board of directors of the ACTUS Financial Research Foundation. His current R&D interests are focused on the automation of risk assessment to improve the transparency and resilience of the financial system.

Michael L. Brodie Dr. Michael L. Brodie has over 40 years of experience in research and industrial practice in databases, distributed systems, integration, artificial intelligence, and multidisciplinary problem solving. Dr. Brodie is a Research Scientist, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology; advises startups; serves on Advisory Boards of national and international research organizations; and is an adjunct professor at the National University of Ireland, Galway, and at the University of Technology, Sydney. As Chief Scientist of IT at Verizon for over 20 years, he was responsible for advanced technologies, architectures, and methodologies for IT strategies and for guiding industrial-scale deployments of emerging technologies. Current interests include Big Data, Data Science, and Information Systems evolution. He has served on several National Academy of Science committees. Dr. Brodie holds a Ph.D. in databases from the University of Toronto and a Doctor of Science (honoris causa) from the National University of Ireland.

Lukas Budde Dr. Lukas Budde studied mechanical engineering at the Technical University in Darmstadt till 2011. After that he did his PhD at the Institute of Technology Management at the University of St.Gallen in 2015 where he is working as a Post-Doc today. In his research he focuses on complexity management and digitalization where he did several research projects in the last seven years. Besides that he is partner of the Complexity Management Academy in Aachen where he also conducts specific lectures for industry and is co-founder of an IT platform for intercompany process optimization.

Nils Andri Bundi Nils A. Bundi is a research associate at Zurich University of Applied Sciences (ZHAW) and “associate” data scientist at ZHAW’s Datalab. In this role, Nils has contributed to a wide range of projects in both the financial industry and academia at the interface of financial data, mathematics, and technology over the past five years. As architect and core-team member of the ACTUS project, an open source initiative developing and promoting a global standard for the algorithmic representation of financial instruments, Nils has shaped its data standards and algorithmic library for the past four years. He is also CTO and member of the board of directors of Ariadne, a Swiss FinTech startup offering a scalable financial simulation and reporting platform. Nils holds an Engineering BSc. and Finance MSc. from ZHAW and is a PhD Candidate in Financial Engineering at Stevens Institute of Technology, Hoboken (USA).

Clemens H. Cap Clemens H. Cap has studied mathematics, computer science and physics at the University of Innsbruck and holds a PhD in mathematics. After a time as postdoc at the University of Zurich he obtained his habilitation. He worked as an assistant and associate professor at the universities of Zurich and Mannheim and since 1997 is full professor for information and communication services at the University of Rostock. His research interests comprise Internet applications, cyber-security, distributed systems and the social impact of information technology. He has conducted several research projects with national, international and industrial funding, including DFG, BMBF and EU. He is a regular lecturer in the Baltic states and an adjunct professor at Zurich university.

Markus Christen Markus Christen is a Research Group Leader at the Institute of Biomedical Ethics and History of Medicine and Managing Director of the UZH Digital Society Initiative. His research interests are in empirical ethics, neuroethics, ICT ethics and data analysis methodologies. He received his MSc in philosophy, physics, mathematics and biology at the University of Berne, his PhD in neuroinformatics at the Federal Institute of Technology in Zurich and his habilitation in bioethics at the University of Zurich. In addition to his academic career, he has many years of experience in project management, public relations, and science journalists working for Swiss communication and consulting firms, press and electronic media and investment boutiques.

Mark Cieliebak Dr. Mark Cieliebak is researcher and lecturer at Zurich University of Applied Sciences (ZHAW), where he develops efficient algorithms for text and data analytics. He is also founder and CEO of SpinningBytes AG, a Swiss company which develops software for automatic text understanding. He received his Ph.D. from the Swiss Federal Institute of Technology (ETH) in 2003 for his research on algorithms and complexity. Before joining ZHAW, he was CIO in a startup for social media monitoring software (later acquired by Microsoft). Mark Cieliebak is author of more than 30 scientific publications. In 2014, he was awarded the “Best Teaching Award” of his university.

Philippe Cudré-Mauroux Philippe Cudre-Mauroux is a Full Professor and the Director of the eXascale Infolab at the University of Fribourg in Switzerland. He received his Ph.D. from the Swiss Federal Institute of Technology EPFL, where he won both the Doctorate Award and the EPFL Press Mention in 2007. Before joining the University of Fribourg, he worked on information management infrastructures at IBM Watson (NY), Microsoft Research Asia, and MIT. He recently won the Verisign Internet Infrastructures Award, a Swiss National Center in Research award, a Google Faculty Research Award, as well as a 2 million Euro grant from the European Research Council. His research interests are in next-generation, Big Data management infrastructures for non-relational data. Webpage:

Marcel Dettling Dr. Marcel Dettling is lecturer for statistical data analysis and predictive modelling at the Zurich University of Applied Sciences (ZHAW) and at the Federal Institute of Technology (ETH Zürich). He has both an MSc (2000) and a PhD (2004) in Mathematics from ETH Zürich. The focus during his thesis was on Supervised Learning in High Dimensional Problems. He then applied his knowledge to biomedical applications at the Johns Hopkins Medical School. Since 2006, he is employed at ZHAW’s Institute for Data Analysis and Process Design where he has worked on numerous R&D projects with industry partners and government offices in many different application fields. The focus of these projects was about descriptive and predictive modelling in complex systems with big datasets. He is also strong in prototyping statistical software.

Oliver Dürr Prof. Dr. Oliver Dürr is professor of data science at the Institute for Optical Systems at the HTWG Konstanz University of Applied Sciences, and a former researcher and lecturer for data analysis and applied statistics at the ZHAW (Zurich University of Applied Sciences). After his PhD in theoretical physics he worked 10 years in a bioinformatic company developing and applying machine learning and statistical methods to all kind of high dimensional -omics data. He is now working mainly on deep learning.

Thomas Friedli Thomas Friedli is a Professor for Production Management at St.Gallen University in Switzerland. His main research interests are in the fields of managing operational excellence, global production management and management of industrial services. He is a lecturer in the (E)MBA programs in St.Gallen, Fribourg and Salzburg. He spent several weeks as Adjunct Associate Professor at the Purdue University in West Lafayette, USA. Prof. Friedli leads a team of 15 researchers who develop new management solutions for manufacturing companies in today’s business landscape. He also is the editor, author or co-author of 13 books and various articles.

Rudolf M. Füchslin Prof. Dr. Rudolf Marcel Füchslin (1966) (Dr. phil., Dipl. Physiker ETH) Study of theoretical physics at ETH Zurich. PhD in computational physics at Univ. Zürich. Various positions at German and Italian Institutions. Presently head of the group for Applied Complex Systems Sciences at the School of Engineering of the Zurich Univ. for Applied Sciences Winterthur (Switzerland) and co-director of the European Centre for Living Technology in Venice (Italy). Presently, Rudolf’s main research interest lies on the interface between data science, bio – inspired technology and the application of complex systems science.

Melanie Geiger Dr. Melanie Geiger is a research associate at Zurich University of Applied Sciences and received her PhD degree in 2018 from the University of Neuchâtel, Switzerland. She is interested in information retrieval, machine learning and in particular reinforcement learning and related areas. She has obtained her M.Sc. in computer science from ETH Zurich with a focus on machine learning and computer vision.

Stefan Glüge Stefan Glüge studied Informations Technology at the Otto-von-Guericke University Magdeburg, Germany. His Ph.D thesis investigated the implicit learning of sequences in Recurrent Neural Networks. Since 2013, he is research associate in the Bio-Inspired Modeling & Learning Systems Group at the Zurich University of Applied Sciences, Switzerland. His research interest are Recurrent Neural Networks, Automatic Speech Processing, Emotion Recognition from Speech, Time Series Analysis, Time Series Prediction, and Reinforcement Learning.

Ernst Hafen Prof. Dr. Ernst Hafen obtained his PhD from the Biocenter at the University of Basel in 1983. From 1984 to 1986 he worked at the University of California in Berkeley as a postdoctoral fellow before joining the University of Zurich as an assistant professor in 1987. He was promoted to full professor in 1997. From 2005 to 2006 he served as president of ETH Zurich. Since 2005 he holds a professorship at the Institute of Molecular Systems Biology at ETH Zurich. He is founding member and the president of Ernst Hafen also serves as the president of the Biotechnopark Zürich-Schlieren.

Christian Hauser Christian Hauser is Professor of Business Economics and International Management at the Swiss Institute for Entrepreneurship (SIFE) at the University of Applied Sciences HTW Chur. He is a member of the topical platform Ethics of the Swiss Academy of Engineering Sciences (SATW) and of the United Nations Principles for Responsible Management Education (PRME) Working Group on Anti-Corruption, as well as head of the first PRME Business Integrity Action Center in Europe. His research interests include international entrepreneurship, SME and private sector development, corporate responsibility, and business integrity.

Stefan Hegy Stefan Hegyi, MLaw is a research associate at ZHAW School of Management and Law and the Zurich Center for Information Technology and Data Protection (ITPZ) in Winterthur. He teaches and advises on technology related legal issues, in particular data protection. Stefan Hegyi has completed his legal studies in Winterthur, Paris, Neuchâtel and Columbia University (NY). Before joining ZHAW, he worked in a law firm in Zurich advising clients on all aspects of IT law. Stefan Hegyi is a member of the ZHAW Datalab managing board.

Gundula Heinatz-Bürki Dr. Gundula Heinatz Bürki is Managing Director of the Swiss Alliance for Data-Intensive Services, a Swiss-wide network of competences for innovative companies and universities. In her role she enables the collaboration between industry and universities to develop new data-driven products and services. In her past, she was the Head of Smart Analytics in a Swiss insurance company and as the business manager responsible for the Mobiliar Lab for Analytics at the ETH Zurich. She holds a PhD in business computer science from TU Dresden (1998).

Jonas Heitz Jonas Heitz is a Research Assistant and Master Student at the Information Engineering Group at Zurich University of Applied Sciences (ZHAW). His research focuses on Data Science with the main topics Big Data, Machine Learning and advanced database technology. Previously Jonas Heitz worked as a research intern at Lawrence Berkeley National Laboratory in Berkeley, California and completed his bachelor study in computer science at ZHAW.

Lukas Hollenstein Dr. Lukas Hollenstein is a Senior Lecturer in Mathematics, Modelling & Simulation at the ZHAW Institute of Applied Simulation in Wädenswil. He studied theoretical physics at the University of Zürich and received his PhD in Cosmology from the University of Portsmouth (UK) in 2009 for his work on modelling and detecting dynamical Dark Energy and the generation of large-scale magnetic fields in the early Universe. Continuing on this line of research he worked as a postdoc at the University of Geneva and at the CEA Saclay (F). He joined the ZHAW in 2013 to work on simulation problems in production, logistics, supply-chain, etc., lately focusing on simulation-based optimization.

Markus Huppenbauer Markus Huppenbauer studied Philosophy and Theology in Zurich, qualifying with a Licentiate degree in 1985. In 1990 he gained a doctorate in Philosophy and has been a University Lecturer for Ethics at the Faculty of Theology in Zurich since 1999. In 2017 he was appointed Associate Professor ad personam for Ethics at the University of Zurich, where he serves as the Director of the Center for Religion, Economy and Politics (CREP) too. His main focus in research and teaching is business ethics. He has special expertise in the management of large interdisciplinary projects that combine ethical expertise with natural and social sciences and that have a particular focus on practical and application-oriented matters.

Peter Kauf Dr. Peter Kauf is CEO of PROGNOSIX, a startup company in the field of predictive analytics. His work focuses on data driven applications that enable collaboration of human intuition and algorithmic intelligence. Previously, Peter Kauf was a lecturer in mathematics and statistics at ZHAW Zurich university of applied science. He holds a PhD in applied mathematics from ETH Zurich (2011).

Jonathan P. Leidig Dr. Jonathan P. Leidig is a Professor at Grand Valley State University. His research involves information retrieval, visualization, and health-related modeling and simulation. He previously held appointments at Argonne National Laboratory (Department of Energy) and the Virginia Bioinformatics Institute. He holds a Ph.D. in Computer Science from Virginia Tech.

Lukas Lichtensteiger Dr. Lukas Lichtensteiger is senior lecturer in mathematics at ZHAW School of Engineering in Winterthur. He received his MSc in theoretical physics and his PhD in robotics and artificial intelligence from University of Zurich in 2004. After a postdoc in the USA, he worked in the semiconductor industry for 6 years until 2014 when he switched back to academia. His current research focuses on applied optimization and machine learning.

Johannes Micheler Johannes Micheler is working as a statistician at the European Central Bank in Frankfurt. He is responsible for the technical developments of the Centralised Securities Database, a micro database for financial contracts. Previously he has worked at the Austrian Central Bank in Vienna using micro data for credit risk modelling. He has a degree in Economics from the University of Vienna.

Mourad Khayati Mourad Khayati is currently working as a Senior Researcher and a lecturer at the University of Fribourg in Switzerland. He received his PhD from the University of Zürich in Switzerland working under the supervision of Prof. Michael Böhlen. His research interests include Time Series, recovery of missing values, and matrix decomposition techniques.

Kevin Mader Dr. Kevin Mader is founder and CTO of 4Quant Ltd, a Swiss company which develops medical image processing software. He is a lecturer at the Swiss Federal Institute of Technology (ETH Zurich) where he teaches a course on Image Analysis and Big Data. He also teaches courses on Machine Learning and Image Analysis for Radiologists at the SIIM and RSNA conferences. He received his Ph.D. from the Swiss Federal Institute of Technology (ETH) in 2013 for his research on high-throughput imaging and analysis using X-rays from synchrotrons.

Jürg Meierhofer The design and engineering of services is the common thread throughout the activities of Jürg Meierhofer. He has been lecturer, researcher, and project manager in service science and service engineering at the Zurich University of Applied Sciences (ZHAW) since 2014. Previously, he worked as a manager for service innovation and optimization in the telecommunications and insurance industry for more than ten years. He has got his PhD from the Swiss Federal Institute of Technology in Zurich (ETHZ) as well as an executive MBA degree from the international institute of management in technology (iimt).

Thomas Ott Thomas Ott studied theoretical physics at ETH Zurich and holds a PhD in Neuroinformatics. Since 2011 he is professor at ZHAW Zurich University of Applied Sciences and heads the research group Bio-inspired Modeling and Learning Systems. He is the deputy head of the Institute of Applied Simulation as well as one of the program directors of the Master’s program on Applied Computational Life Sciences. Furthermore, Thomas Ott is a co-founder of the startup company PROGNOSIX. His research interests include methodologies and applications of systems that combine diverse modeling techniques and machine learning.

Michał Piórkowski Michał Piórkowski is currently working as an Enterprise Data Science Lead at PMI. He received his Ph.D. from the Swiss Federal Institute of Technology EPFL in 2009. Before starting at PMI, he was working for Swisscom where he was heading the Big Data Mobility Insights team. He was driving the development (from conception to launch) of Swisscom’s Mobility Insights Platform that turns mobile network monitoring data into insights about human mobility at scale. The Mobility Insights Platform is the first commercialized Smart Data solution by Swisscom and it implements all seven principles of Privacy by Design. Michał Piórkowski’s research interests include big data, predictive and prescriptive analytics, privacy engineering and artificial intelligence.

Marc Rennhard Prof. Dr. Marc Rennhard is a professor of computer science and head of the Institute of applied Information Technology (InIT) at ZHAW School of Engineering in Winterthur, Switzerland. He has been awarded MSc and doctoral degrees in Electrical Engineering from ETH Zurich and also holds the ETH teaching diploma for higher education and the CISSP security professional certification. His main teaching and research interests are in information security and currently, his own research focuses on secure software, security engineering and automated security testing. From 2010 - 2016, he was a member of the board of the Information Security Society Switzerland (ISSS), a network of 1’200 security professionals in Switzerland.

Laura Rettig Laura Rettig is a Ph.D. student at the University of Fribourg, Switzerland under the supervision of Philippe Cudré-Mauroux. Her areas of interest are big data infrastructures, semantic data, entity linking, and deep learning. She received her Master’s degree from the University of Fribourg, with her thesis written on big data streaming using real-world telecommunication data during an internship at Swisscom.

Andreas Ruckstuhl Prof. Dr. Andreas Ruckstuhl is Professor of Statistical Data Analysis at the Zurich University of Applied Sciences (ZHAW) and lecturer of Applied Statistics at ETH Zürich. He holds a PhD (1995) in Mathematics from ETH Zürich. The focus of the thesis was on applications of robust inferential methods in molecular spectroscopy. In 1999, he joined ZHAW, where he has worked on numerous R&D projects with industrial partners and government agencies in many different applications.. The focus of these projects was in statistical data analysis and in the development and application of robust inferential methods.

Murat Sariyar Prof. Dr. Murat Sariyar is a Professor of Medical Informatics at Bern University of Applied Sciences. His main domains of interest include Data Science, Business Intelligence, Anonymisation, Precision Medicine, Statistical Bioinformatics, IT for biobanks, and technical design of identifiers. He obtained his habilitation in medical informatics with a cumulative work on machine learning at the HU Berlin. He has conducted several research projects with national (BMBF) and international funding (EU).

Remo Schweizer Remo Schweizer, M.Sc., studied computer science at ZHAW School of Engineering and is a recent graduate of the Master of Science in Engineering programme at ZHAW, where he focused on computer security.

Beate Sick Beate Sick is professor of applied statistics at ZHAW. She is also a part-time lecturer and scientific collaborator at the Biostatistics Department of EBPI at UZH and a lecturer for statistics at ETH. Her focus in research projects lies on the analysis of high-dimensional experimental data mainly generated in biological experiments or medical/epidemiological studies. Lately her tool of choice have been deep learning methods. Before she has gained experience in high throughput data analysis as head of bioinformatics at the DNA array facility at UNIL and in biomarker discovery at a small startup company called Oncoscore. Beate Sick received her PhD from ETH for an experimental work focusing on high resolution optical microscopy using single molecules as sensitive probes in polymers and as labels attached to functioning proteins.

Jan Stampfli Jan Stampfli is working as a big data engineer at the Federation of Migros Cooperatives. Prior to that, he worked as a researcher at the Zurich University of Applied Sciences contributing to projects in deep learning and information retrieval. He completed his BSc in computer science specialising in service engineering at the Zurich University of Applied Sciences.

Bernhard Tellenbach Prof. Dr. Bernhard Tellenbach is professor of computer science at ZHAW School of Engineering in Winterthur. He received his doctor of science degree from ETH Zurich in 2012, where he worked on network- and information security topics. During his PhD and some time after, he occasionally worked as a penetration tester and security consultant. His current research focuses on security monitoring, malware and security testing and training. He is head of the Information Security research group at ZHAW, leads the Cyber Security platform at the Swiss Academy of Engineering Sciences (SATW) and presides the Swiss Cyber Storm association.

Vasily Tolkachev Vasily Tolkachev is a researcher in computer vision and deep learning at Zurich University of Applied Sciences, working on applications in high-contents screening. His extensive experience spans applied research projects in predictive maintenance, quality diagnostics, and industrial engineering with leading companies. He is particularly interested in semi-supervised learning, transfer learning, as well as large-scale learning. He received his MSc Statistics degree from ETH Zurich, where he conducted research on statistics of stochastic processes.

Michael Widmer Dr. Michael Widmer studied law at the universities of Basel and Zürich. He is a lecturer for data protection law at the Center for Social Law of the ZHAW School of Management and Law and is leading the Zurich Center for Information Technology and Privacy ITPZ.

Greg Wolffe Greg Wolffe is a Professor of Computer Science at Grand Valley State University. He received his B.S. in Biology from Michigan State University and spent over a decade working in the medical field before obtaining his Ph.D in Computer Engineering from the University of Wisconsin, Milwaukee. He teaches courses in high-performance computing and machine learning; and his current research focuses on these areas, especially as applied to problems in the life sciences.

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