Topic Leaders

In 2015, the ZHAW Center of Enterprise Development started to build up a competence group in ‘Blockchain in Supply Chain Management’. The group is currently focusing on different Blockchain projects and is regularly present as speakers at conferences. In 2018, they founded the Swiss Alliance expert group ‘Blockchain Technology in Supply Chain Management’ which is currently co-led by the group. The Center of Enterprise Development works closely with companies to understand and explore the potential of Blockchain technologies for supply chains. 

Teaching, training and further education 

The Center for Enterprise Development offers an elective module ‘Business Value of Blockchain’ for Bachelor of Science in Business Administration, Business Information Technology and International Management. In addition, the Center of Enterprise Development offers Continuing Education in the form of a continuing education course in ‘Blockchain for Business Applications’ (WBK BGA) and a Blockchain module within the Certificate of Advance Studies Digital Strategy and Creation of Value (CAS DSW). 

Research Topics 

Blockchain technologies in supply chain management, Blockchain technologies for business ecosystems, industry 4.0 & digital transformation, supply chain management, lean management. 

Selected projects 

• Co-leading the expert group ‘Blockchain Technology in Supply Chain Management’
• Research project ‘Blockchain technology in a pharma supply chain’ on the base of Hyperledger Fabric
• Research Project ‘Blockchain technology for an audit trail in a food supply chain’ on the base of Ethereum
• Student projects in cooperation with different companies like WWF, Bossard AG, Avrios AG, Büchi, etc.

Selected publications

Lustenberger, M., Spychiger, F. & Taylor, M. (im Druck). Optimierung des Supply Chain Informationsaustauschs mit Blockchain-Startups, in Digitalisierung in der Praxis: So schaffen KMU`s den Weg in die Zukunft, Wiesbaden: Springer. 

Lustenberger, M. (2018). Blockchain-Technologie im Supply Chain Management. In: F&E-Konferenz zur Industrie 4.0, Brugg-Windisch, 15. Januar 2018. 

Zavolokina, L., Spychiger, F., Tessone C. J. & Schwabe, G. (2018). Incentivizing Data Quality in Blockchains for Inter-Organizational Networks – Learning from the Digital Vehicle Dossier, ICIS International Conference on Information Systems 2018. 

Lustenberger, M. (2017). Blockchain-Technologie in Supply Chain Management. In: Zweite F&E Konferenz zur Industrie 4.0, Winterthur, 11. Januar 2017. 

Main contact: Michael Lustenberger, profile here. 

The Swiss Institute for Entrepreneurship SIFE ist part of the University of Applied Sciences of the Grisons. More than twenty researchers participate in research within four areas: (1) Innovation, (2) Digital Strategies, (3) Corporate Responsibility and (4) Internationalisation and Supply Chain Management.
The institute is a well established research institution with an excellent track record in Innosuisse projects.

The digital transformation has led to many opportunities for companies. Based on the new technologies and connectivity, data are gathered, transferred and stored. These data lead to multiple opportunities related to business models, services or products. We have developed methods and tools to derive and analyse databased business models, services and products. In current research projects, we focus on how to sustainably implement databased services within the organization and derive measure concerning people, technology and organization.

Digital Supply Chains provide another huge potential for improvement as data and technological developments lead to new opportunities. We support companies in how to develop a digital supply chain strategy and discuss new opportunities for supply chain coordination and configuration. Overall, we support industrial partners in their digital transformation as we combine current research methods from innovation, business models and supply chain management. In addition, we have conducted different research projects concerning corporate digital responsibility (i.e. ethical challenges of digitalization, data ethics decision-making, managing ethics and compliance through digital tools).

Bachelor Studies:
Digital Supply Chain Management
Digital Business Management
Service Design and Innovation

Master Studies:
New Business Development

Advanced Studies:
EMBA in Digital Transformation
EMBA in Smart Marketing
EMBA in Digital Technologies and Operations

Current and completed research projects (excerpt):

• The successful implementation of databased services
• Industry 4.0: Digitalisation of the value chain – a strategic roadmap
• Trade Compliance Management of a digital supply chain
• Tools and methods for a company wide digital strategy
• Between Solidarity and Personalization – Dealing with Ethical and Legal Big Data Challenges in the Insurance Industry
• Web-based whistleblowing systems
• AI-based integrity risk monitor
• Ethical challenges of Big Data
• Data ethics decision-making

Publications (excerpt)

• Loi, M., Hauser, C., & Christen, M. (2020). Highway to (Digital) Surveillance: When Are Clients Coerced to Share Their Data with Insurers? Journal of Business Ethics. doi:10.1007/s10551-020-04668-1.
• Christen, M., Blumer, H., Hauser, C., & Huppenbauer, M. (2019). The ethics of Big Data applications in the consumer sector. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied Data Science: Lessons learned for the data driven business (pp. 161–180). Cham: Springer.
• Hauser, C., Blumer, H., Christen, M., Hilty, L., Huppenbauer, M., & Kaiser, T. (2017). Ethische Herausforderungen für Unternehmen im Umgang mit Big Data. Zürich: SATW.
• Deflorin, P., Dinner, K., Moser, P.: Digitale Transformation: Ein vielschichtiger Prozess entlang von vier Entwicklungsstufen, Teile 1-3, KMU Magazin, Nr. 1-2, 2019.
• Deflorin, P., Scherrer, M., Amgarten, J.: “Industrie 4.0 Geschäftsmodelle-Ein Analyse-Raster zum Erkennen von Industrie 4.0 Potenzialen und notwendigen Veränderungen”, Industrie Management, 5/2017.
• Deflorin, P., Hauser, C., Scherrer-Rathje, M.: Schweizer Unternehmen sehen Digitalisierung als Chance. Die Volkswirtschaft, Ausgabe 5, 2015, p. 58-61.

Main contact:
Patricia Deflorin

Digitalization, individualization, and changing consumer behavior patterns are posing new challenges for organizations. But they also offer new opportunities. The Institute of Market Offerings and Consumer Decisions examines what requirements consumers have in terms of innovative offers, products, services, and new technologies. 

We apply psychological expertise to investigate what consumers need, prefer, know, and how they make decisions. Our main focus is on the psychological aspects that come into play in consumer decision-making and the resulting consumer behavior. We explicitly incorporate the environment in which consumers engage with what is on offer. 

Working with companies, governmental, and non-profit agencies, we carry out research projects dealing with a range of questions around consumer needs and requirements related to digital offerings. Due to our interdisciplinary partnerships and our own expertise, we combine knowledge from different fields to drive the innovation and development of marketable products and services.

This is what we offer our partners:

• Generate insights for product and service development based on sound theoretical and industry expertise 
• Provide theory- and evidence-based recommendations to support partners in their development of customer-oriented digital products and services.
• Analyze language data for content, psychological distance, emotions, and emotional intensity (NLP) including linguistic matching

We are actively involved in the Swiss Alliance for Data-Intensive Services. As members in several Data+Service Expert Groups (e.g. Smart Services, Natural Language Processing in Action), we organize events to share our expertise as well as collaborating in research projects with other network members.

Selected research project(s)

• Lifestyle change via sustainable and personalized interventions
• Bike to the future: Using VR to investigate cyclists‘ safety perception
• Development and evaluation of the MyFoodways app: A digital intervention to promote healthy and sustainable food consumption

Find out more on the FHNW website. 

Main contact: Anne Herrmann, profile here.

In 2019, the Digital Society Initiative of the University of Zurich started the “Digital Ethics Lab” (DSI-DEL) as a competence center for handling the ethical challenges of digital transformation. It integrates several projects of the Neuro-Ethics-Technology Group at the Institute of Biomedical Ethics and History of Medicine with the ethics research of several other DSI members.

The aim of the DFSI-DEL is to explore and analyze ethical aspects of digitalization along the following three dimensions:

• Normative: Critically explore ethical questions and implications that the use of digital technologies poses.
• Empirical: Investigate how digital technologies influence people’s moral competences and ethical beliefs.
• Constructive: Develop ethically sound digital technologies and the respective guidelines.

The DSI-DEL organizes workshops and other meetings for regular exchange and promotion of interdisciplinary research along these three lines. It also supports the DSI as a whole by providing ethical expertise for other research groups and for outreach activities.

Research Topics

Current research topics include:

• Ethical issues of Artificial intelligence and Big Data
• Ethics of Cybersecurity
• Creating game-based tools for ethics training

Selected projects 

• Swiss National Research Program 75 “Big Data”: Between Solidarity and Personalization – Dealing with Ethical and Legal Big Data Challenges in the Insurance Industry
• Horizon 2020: Consortium CANVAS – Constructing an Alliance for Value-driven Cybersecurity
• Swiss Foundation for Technology Assessment: Study on the societal effects of artificial intelligence
• Armasuisse Science+Technology: Assessing the impact of digital decision support in critical decision making in the security context

Selected publications

Christen M, Gordijn B, Loi M (eds.) (2019): The Ethics of Cybersecurity. Springer. The International Library of Ethics, Law and Technology, in press

Loi M, Christen M (2019): Two concepts of group privacy. Philosophy & Technology, in press.

Christen M, Blumer H, Hauser C, Huppenbauer M (2019): The ethics of Big Data applications in the consumer sector. In: Braschler M, Stadelmann T, Stockinger K(eds.): Applied Data Science – Lessons Learned for the Data-Driven Business. Springer, 161-180. 

Loi M, Christen M (2019): How to include ethics in machine learning research. ERCIM News 116 (January 2019), 5. 

Main contact: Markus Christen, profile here.

The ZHAW Data Science Laboratory, Datalab, has been founded in 2013 as one of the first dedicated research institutions for Data Science in Europe with a distinct interdisciplinary approach. Today, it hosts more than 80 researchers from six institutes and three departments with such diverse backgrounds such as data protection law, analytics, computer science and entrepreneurship. It features particularly strong research initiatives on deep learning, text analytics and big data science, and brings these and other expertises into play at several Data+Service Expert Groups or through involvement in other network events and conferences such as SwissText or SDS.

Examples of our work include:

• The Bio-SODA project: this SNSF-funded project aims at enabling biologists and medical staff to perform sophisticated semantic queries across large, decentralized and heterogeneous databases via an intuitive interface.

• A book on Applied Data Science: together with colleagues from all over the world, we recently finished a volume that brings together many examples and lessons learned in how to approach data science in the data-driven business (available ca. mid 2019).

• Study program MAS Data Science: this course, usually fully booked many months in advance, gives a thorough technical introduction into the nuts & bolts of data science, machine learning and statistics.

Being very strong in applied research, we started a PhD program in Data Science together with SUPSI and the Universities of Zurich and Neuchatel, and are actively collaborating with numerous companies and research institutions. You can get in touch via datalab@zhaw.ch, main contact Prof. Thilo Stadelmann.

The Department of Banking, Finance, Insurance (ABF) has 80 employees and offers Bachelor’s and Master’s degree programs as well as continuing education programs. Other activities include applied research and consulting in the fields of wealth and asset management, financial management, insurance, pension funds, and real estate. In the field of digitalization, we focus on human centric service design and innovation in financial industry. Anita Sigg, Head of Personal Finance und Wealth Management, brings her knowledge to the expert group smart services.

Examples of our work include:

The Braingroup project: This Innosuisse funded project aims to develop a new online financial plan for retail customers. This enables them to carry out comprehensive financial planning online and independently. The findings from research and practice were summarized in a “life event model”. (Link)

The FinTech project, under the EU’s Horizon2020 funding scheme, aims to create a European FinTech risk management hub. To this end, it will develop ready-to-use FinTech risk management models, which will be dynamically updated and aligned with best research and practice. The project includes training to national regulators (SupTech) and to European FinTech hubs (RegTech) by a group of independent experts that have leading research expertise in the measurement of the risks that arise from the application of big data, artificial intelligence and blockchain technologies and, specifically, of those arising from innovative payments, peer to peer lending and financial robo-advisory. (Link)

Digital Insurance Broking: The goal of the research project is to develop an application that enables the statistical comparison of risk- and insurance profiles. With this information at hand, users of the e-Broker platform can compare themselves against peers and identify coverage gaps and unnecessary insurance coverages. (Link)

Teaching, training and further education

Since 2018 we offer the MAS Business Innovation Engineering for Financial Services with a focus on digitalization of the finance industry. The MAS consists of the two mandatory CAS with a focus on the financial industry (Financial Service Design and Financial Business Innovation). Subsequently, two CAS of choice can be chosen. (Link)

In addition to the MSc courses in Banking and Finance or Accounting and Controlling, we offer various Master of Advanced Studies such as in Financial Consulting or Corporate Finance & Corporate Banking.

Main contact: Anita Sigg

Beatrice Paoli studied Physics in Rome in the field of Biophysics. She then moved to the University of Zurich where she received her Doctorate degree in the field of Computational Biochemistry. She currently leads the Laboratory for Web Science (LWS) of the FFHS. The know-how of the LWS is not only employed in the research but also in educational programs.

Teaching, training and further education
The FFHS offers Bachelor of Science in Informatics, Business Informatics, Business Administration, Industrial Engineering, Nutrition and Dietetics. Moreover the FFHS offers Continuing Education in the form of  MAS, CAS, DAS and EMBA.

Research topics

• GeoHealth Analytics
• Data Science for Energy, Nature and Materials

Selected projects

• 38489.1 IP-ICT – Impulse: Agentenbasierter digitaler Lehrer (Innosuisse)
• Measure Based on Graph Automorphism: Theoretical Aspects and Their Application (2019-2021) – FWF Projekt (ähnlich zu SNF in der Schweiz)
• Optimizing oral iron supplementation regimens during pregnancy using serum hepcidin profiles and iron stable isotopes: defining a dosing regimen with maximal absorption and minimal gastrointestinal side effects (SNF Projekt)
• 46559.1 IP-SBM – A new platform for automatic skills extraction and validation of profiles to identify talents (Innosuisse)
• 39988.1 IP-EE – RASPLAN (Innosuisse)
• Neural-Network based Solution of partial differential Equations as an Alternative to finite Elements Analysis (Hasler Stiftung)
• E-Assessment: Home Based examination and proctoring using artificial intelligence –  Intel Research

Selected publications
https://www.ffhs.ch/en/research/lws/publications

Main contact: Beatrice Paoli

The Geometa Lab is a research group at the Institute for Software (IFS) at the HSR Hochschule für Technik Rapperswil (member of the Fachhochschule Ostschweiz). We conduct research and development, services, consulting and training courses in the field of geoscientific applications. The employees of Geometa Lab are involved in the bachelor’s program in computer science as well as in the master’s program in computer and data science. 

The Geometa Lab:

• is engaged in the field of Data Science, in particular (Spatial) Data Engineering and (Open) Data Management.
• is involved in database management system projects, such as PostgreSQL (Swiss PGDay).
• is involved in open source GIS projects like PostGIS and QGIS.
• integrates open and closed source software, as well as open and government data.
• is specialized in crowd sourced open data, especially OpenStreetMap.

Contact: https://www.hsr.ch/geometalab

Training and further education: https://giswiki.hsr.ch/Kurse

Dr. L. Andrea Dunbar is Head of the Vision Embedded Systems group at CSEM. CSEM is a non-for-profit Research and Technology Organization.

Her technical expertise was originally on photonics crystals and plasmonics before turning to multispectral and vision system. She has published more than 60 papers in international journals and conferences and is a guest lecturer at EPFL.

Andrea got an eMBA from EPFL in 2017. She received her PhD in Optics in 2002 from Trinity College Dublin, Ireland. Andrea did her BSc in Physics at St-Andrews University Scotland, and was educated at Charterhouse School in England.

Her current research areas include Vision systems, Machine Learning, Hyperspectral Imaging systems especially in constrained e.g. Ultra-Low Power environments. She is leading the Expert Group “Machine Learning Clinic” within the Alliance.

Research Topics
Embedded Machine Learning, The Challenge of Labellization, Hierarchical system, Ultra-Low Power Vision Systems and Smart Optics

Selected Projects
NewBorn Care (Nano-Tera project – Budget 550 kCHF), 2013-2017
Reducing the false alarms of neonate vital sign monitoring via a computer vision-based approach to accurately measure heart and respiratory rates in a contactless way

Biowave (CTI project – Budget 1.0MilCHF), 2016-2018
Biometric device reading of vein pattern for access control.  Made possible through extremely thin, low power intelligent vision systems.

Magento (CTI project – Budget 1.1MilCHF), 2017-2019
Platform for magnetically oriented optical features which will be investigated using deep neural networks for the machine learning.

GrainView (CTI project – Budget 850k CHF), 2017-2019
Project on Machine learning to look at mass flow of grains using advanced machine vision algorithms, deep neural networks among others will bring optimization of the milling process.

Selected Publications
Ross P. Stanley; Amina Chebira; Alireza Ghasemi; Andrea L. Dunbar, Hyperspectral imaging using a commercial light-field camera  Proceedings Volume 10110, Photonic Instrumentation Engineering IV;  10111013, (2017).

Pedram Pad, Nemanja Niketic, Amina Chebira, Edo Franzi, Ross P. Stanley, and L. Andrea Dunbar, Versatile, intelligent multispectral imaging camera made with off-the- shelf components. Proceedings Volume 10539, Photonic Instrumentation Engineering V; 105390M (2018)

L. A. Dunbar, R. P. Stanley , M. Lynch, J. Hegarty , R. Houdré, U. Oesterle and M. Ilegems, “Excitation-induced coherence in a semiconductor microcavity”, Physical Review B (Rapid Comm.) (19), 195307 2002.

L. A. Dunbar, R. Houdré, G. Scalari, L. Sirigu, M. Giovannini, J. Faist  “Small optical volume terahertz emitting microdisk quantum cascade lasers”, Applied Physics Letters 141114, 2007.

M. Guillaumée, L. A. Dunbar and R. P. Stanley, “Description of the modes governing the optical transmission through metal gratings”, Optics express 19 (5) 4740 (2011).

Main contact: Andrea Dunbar

We believe that digital health has tremendous potential to enhance the well-being of everyone in our population. The evolution towards patient-centered care requires completely new approaches in the healthcare system. The ZHAW Digital Health Lab is a collaboration of digital health experts from the  fields of biomedicine, health, technology and business and has the aim to become a leading and  internationally recognized competence center in the field of digital health.

We cooperate with service providers, industry, health insurance companies and other research partners to develop innovative solutions to current challenges in the healthcare sector. As a competence network, we promote technology transfer between science, industry and society. 

We offer to our partners: 

• combined expertise of specialized research groups in technology, healthcare and business
• many years of project experience in digital health (EU- and Innosuisse-projects)
• problem-based, solution-driven research

Our interdisciplinary approach forms a unique ground for digital health solutions throughout 
Switzerland and makes us a competent, valued partner. 

The ZHAW Digital Health Lab was initiated in 2018 and is managed by the ZHAW Digital Health Lab board. Find out more about our activities, projects and publications here: www.zhaw.ch/digitalhealth, or get in touch with us via digitalhealth@zhaw.ch.

The Institute for Information and Process Management at FHS St. Gallen has a long R&D track record in the digital health field ranging from sensor-based remote monitoring and patient self-management, active assisted living, systems for behavioural change support to surveillance systems for zoonoses. A more recent focus has been on applying insights from behavioural economics to health promotion, public health, prevention and patient self-management using smart (mobile) technologies with the aim to motivate people to change their health behaviour.

The team members come from a wide range of backgrounds and bring together expertise in data science, sensor technology, ethnographic methods, mobile health solutions, user-centred design, hardware integration, and software architecture.

Selected topics

• Data mining on sensor data to establish correlations between behavioural patterns and personal health
• Track vital data to fill in the “black box” between doctoral visits
• Remote monitoring of patients for the early detection of health problems
• Motivate people to change unfavourable health behaviour
• Sustain motivation by personalising advice and taking into account context
• Facilitate behavioural change by minimising effort through automatic adaptation

Teaching

Minors in data science in the BSc in Industrial Engineering (start spring 2020) as well as the BSc in Business Information Systems (planned).

Selected publications

Reimer, U. / Emmenegger, S. / Maier, E. / Ulmer, T.: SmartCoping: A Mobile Solution for Recognizing Stress and Coping with it. To be published in: N. Wickramasinghe, F. Bodendorf (eds): Mobile Sensors and Analytics for Better Health and Wellness. Springer, 2019.

Reimer, U. / Emmenegger, S. / Maier, E. / Ulmer, T. / Vollbrecht, H.-J. / Zhang, Z. / Khatami, R.: Laying the Foundation for Correlating Daytime Behaviour with Sleep Architecture Using Wearables Sensors. In: C. Röcker, J. O’Donoghue, M. Ziefle, L. Maciaszek, W. Molloy (eds): Information and Communication Technologies for Ageing Well and e-Health. Springer, 2018, pp.147-167.

Reimer, U. / Maier, E. / Ulmer, T.: A Self-Learning Application Framework for Behavioral Change Support. In: C. Röcker, J. O’Donoghue, M. Ziefle, M. Helfert, W. Molloy (Eds.): Information and Communication Technologies for Ageing Well and e-Health. Second International Conference, ICT4AWE 2016, Revised Selected Papers. Springer, 2017, pp. 119-139. 

Reimer, U. / Maier, E. / Laurenzi, E. / Ulmer, T.: Mobile Stress Recognition and Relaxation Support with SmartCoping: User-Adaptive Interpretation of Physiological Stress Parameters. In: Proc. Hawaii Int. Conference on System Sciences (HICSS-50), 2017.

Selected projects

GREAT – Persuasive Ambiences (European AAL Initiative)

• Develop and implement scalable, adaptive and affordable solutions for supporting daily routines for people with dementia
• Employ controllable mood lighting based on optical motion sensors to address behavioural challenges such as agitation and apathy.

(E-) Nudging for Chronic Care (Gebert Rüf Foundation)

• Use insights from behavioural economics to “nudge” people towards a healthier lifestyle
• Develop an application framework for self-learning behavioural change support systems which infer individual preferences and personalised advice from users’ behaviour and vital data

SmartCoping (CTI)

• Use heart rate variability for early stress detection
• Automatically adapt to the individual user
• Help users cope with stress with biofeedback

SmartSleep  (Internat. Bodenseehochschule)

• Recognize sleep stages from wearable sensors
• Find correlations between daytime activities, sleep structure and subjective sleep quality
• Provide personalised advice based on correlations  

Mobile Palliative Care (Gebert Rüf Foundation)

• Develop and evaluate a sensor-based monitoring system to accompany people at the end of life and thus avoid unnecessary admissions to hospital or emergency units

Remote monitoring of severely ill children  (CTI)

• Support family members taking care of ill children at home through on-site monitoring
• Define detailed contingency plans as well as the workflows triggered by alerts

In all these projects, the team closely collaborates with colleagues from other disciplines such as the nursing or social sciences, and application partners such as sleep laboratories, clinics, dementia care units etc.

The Network Science & Complex Systems research group at SUPSI focuses on bridging the gap between the complexity of big data and their understanding and exploitation. The group employs network science methodologies to deliver computational solutions to complex systems involving large-scale datasets. The group operates at the intersection between network and data sciences and focuses on extracting actionable insights from the analysis of complex systems and phenomena, e.g., by identifying relevant elements and connections within a system, revealing patterns across networks, or making explainable predictions about dynamical processes.

Technology and Applicative Areas

As networks describe how things are connected to each other, every system can be represented as a network by describing its parts and how they are connected to each other. This paves the way to multidisciplinary applications of network science methodologies, ranging from business intelligence and health to manufacturing and social media analytics.

Research

The Network Science & Complex Systems group has extensive experience with several academic and industrial projects. Here follow some examples of the ongoing activities.

Analysis and detection of opinion manipulation on social media (SNSF-funded project: Detecting trolls activity in online social networks)

Uncovering and understanding of teenager online information search (SNSF-funded project: Late-teenagers online information search)

Human activity recognition and emotion classification in Virtual Reality (EITM-funded project: Virtual Machina)

Detection and analysis of defective manufacturing elements (Innosuisse-funded project: QU4LITY)

Prof. Giovanna Di Marzo Serugendo is head of both the Institute for Information Service Science and the Computer Science Center of the University of Geneva. They are both active in diverse areas of computer science, service science for digital services and informatics linguistics. Key expertise areas of the Computer Science Center include:

• Artificial Intelligence
• Modelling and Simulation
• Virtual / Augmented Reality
• Smart Cities
• E-Health and QoL
• Information Security

Education

• BSc/MSc/Phd in Computer Science
• BSc/MSc/Phd in Information systems and service science
• BSc/MSc/PhD in in Informatics for humanities

More information

Further education

• Continuous education programs: Blockchain, Information Security, Data Governance, Web development, Information Systems Management, IoT. More information
• Sessions on AI and Software development. More information

Projects

• Project Avenue
• Annual project reports

Open, participatory, and resilient. This is how we imagine the public sector. Through research and development, we explore the changes caused by digitisation in the public sector. Our focus is on the transformation of democratic processes and new forms of participation, on building a suitable data infrastructure and on designing and supporting innovation and change processes. Our scope includes public administrations, public-private partnerships, memory institutions, smart cities and health professions.

The group on “Data and Infrastructure” is working on making open access to public sector data in Switzerland a reality. Appropriate measures must be used to tackle the big data-related challenges facing society. At the organisational level, we help our clients and partners understand the needs of their stakeholders and engineer solutions accordingly. At the semantic and technical levels, we are helping organisations to provide data and information effectively, efficiently and in a format that is fit for purpose, to model ontologies and to break down barriers to interoperability.

The group on “Digital Democracy” draws on foundational research to analyse the effects of digitisation on society and politics, also working with partners in applied research (e.g. public sector groups or NGOs) to develop and advance civic tech apps.

The group on “Innovation” studies innovation and the associated processes of change in the public sector. As we support these transformations, our key concerns are strategic leadership and design, with specific emphasis on e-inclusion, user-centricity and privacy. We take an active stand to ensure that public organisations manage their digitisation and participation projects to benefit all individuals concerned.

Selected research projects

• Digitalisierungsmonitor 2019
• Data Policy für Sport-Daten
• Studie zu Chancen und Risiken der Digitalisierung im Umweltbereich
• Studie Weiterentwicklung DCAT AP CH
• The Once-Only Principle Project (TOOP)
• City Platform-as-a-Service – Integrated and Open (CPaaS.io)
• CAPACOA’s Linked Digital Future Initiative (performing arts)

Selected publications

Brugger J., Fraefel M., Neuroni A. (2019): Digitalisierte Verwaltung in der Schweiz – Strategien, Akteure und Vorhaben im E-Government in: Schünemann W., Kneuer M. (Eds.), E-Government und Netzpolitik im europäischen Vergleich, Seite 103 – 118, 2. Auflage.

Estermann, B. & Schneeberger, S. (2018): OGD-Pilotprojekt 2017/18. Bericht über die OGD-Anwendungsfälle

Estermann, B., Fraefel, M., Neuroni, A., & Vogel, J. (2018): Conceptualizing a National Data Infrastructure for Switzerland, Information Polity, 23(1), 43-65. DOI: 10.3233/IP-170033

Estermann B., Julien, F. (2019): A Linked Digital Future for the Performing Arts: Leveraging Synergies along the Value Chain. Canadian Arts Presenting Association (CAPACOA) in cooperation with the Bern University of Applied Sciences.

Haller, S., Estermann, B. & Dungga Winterleitner, A. (2018): Study in View of the Further Development of DCAT-AP CH. Bern University of Applied Sciences, E-Government Institute

Haller, S., Neuroni, A.C., Fraefel, M., & Sakamura, K. (2018): Perspectives on smart cities strategies: sketching a framework and testing first uses. In: Janssen, M., Chun, A.Ch. & Weerakkody, V. (Eds.), Proceedings of the 19th Annual International Conference on Digital Government Research, DG.O 2018, Delft, The Netherlands, May 30 – June 01, 2018. ACM 2018 (PDF, 577 KB)

Neuroni A., Haller S., van Winden W., Carabias-Hütter V., & Yildirim O. (2019): Public Value Creation in a Smart City Context: An Analysis Framework. Book chapter in: Rodríguez Bolívar M. P. (Eds.): Setting Foundations for the Creation of Public Value in Smart Cities, Springer

Find out more on the Institute’s website or contact the Head of the Institute, Alessia Neuroni.

The Institute of Applied Information Technology (InIT) at ZHAW revolves around the topic of building and analyzing smart information systems. With our five focus areas on Human-Information Interaction, Information Engineering, Information Security, Service Engineering, and Software Systems, we bring in the expertise of almost 100 researchers, developers and data scientists into highly successful and results-driven collaborations with industry and other research labs. Specifically, we delight in challenges such as:

• building natural language interfaces for databases or consumers
• improving speech interfaces for Swiss German dialects
• doing pattern recognition on, e.g., medical images, pedestrian videos, industrial audio and multi-lingual text
• designing secure and scalable big data systems
• finding answers in large and small unstructured information heaps

Together with our partners in the ZHAW Datalab, we were amongst the first in Europe to offer specific Data Science continuing education programmes. We also have a specialization in data science on the Master’s level. Specifically, we teach foundations and advanced topics in AI, ML, databases, information retrieval, visual analytics, information security and cloud computing. Beyond teaching, the InIT is very active in professional networks like the Datalab, Data+Service or CLAIRE.

Recent projects

• QualitAI – Quality control of industrial products via deep learning on images
• Ada – Advanced Algorithms for an Artificial Data Analyst
• DeepScore: Digital Music Stand with Musical Understanding via Active Sheet Technology
• INODE – Intelligent Open Data Exploration
• Call-E: Virtual Call Agent

Selected publications

• Braschler, Stadelmann & Stockinger (Eds.), “Applied data science : lessons learned for the data-driven business”
• Stadelmann et al., “Deep Learning in the Wild“
• Stockinger et al., “Scalable architecture for big data financial analytics : user-defined functions vs. SQL“
• Imhof & Braschler, “A study of untrained models for multimodal information retrieval“
• Rennhard et al., “Improving the effectiveness of web application vulnerability scanning“
• Benitez et al., “Twist Bytes : German dialect identification with data mining optimization“

The Institute of Technology Management (ITEM) is part of the University of St. Gallen since 1989 and the biggest institute with four professors for Production Management (PM), Innovation Management, Operations Management and Entrepreneurship. The ITEM-PM is led by Prof. Dr. Thomas Friedli and embraces today 12 research associates across the three competence centers: Global Manufacturing Networks, Operational Excellence and Smart Manufacturing & Services.

Within the group of Smart Services, we focus on new business models, organizational structures, development processes, as well as value-based marketing and selling approaches for services at manufacturing companies in the era of digitalization and data. Thus, as academic leader in the data-driven business models expert group, we bring in our expertise from 15+ years in research and strong company collaboration in the area of industrial services.

With a distinct focus on “insights for practice”, we concentrate our work on enhancing the capabilities of our industry partners across different sub-categories of Smart Services. Examples therefore are:

Benchmarking Smart Services – Transformation of the Service Organization: Based on a questionnaire developed for and with a consortium of different industry partners, we conducted a benchmarking study to derive successful practice companies, who perform high in areas such as strategy alignment, organizational change, service sales, innovation and delivery.

Value-Based Marketing and Sales of Smart Services: This recently granted Innosuisse project is coping with the endeavor to develop a partly software-based tool for manufacturing companies, who strive to sell their Smart Services based on the value for their customers.

Focus Group – Managing Service Innovation Processes: Besides joint research projects with industry partners, we strongly foster cross-industry exchange about specific topics in the realm of Smart Services. Partly motivated by results of our benchmarking study, we see a need for discussing successful approaches to the development of Smart Services in light of a rising inter-firm collaboration.

Our work together with other institutes and industry is manifold, however mainly covered by focus group events, joint research and industry projects, benchmarking studies and individual industry projects. Yet, we further offer seminars (e.g. Smart Service Seminar) on a regular basis. For more information, please get in touch with our main contact Philipp Osterrieder.

Selected Publications:

Osterrieder, P., & Friedli, T. (2018). Determinants for the organizational configuration of manufacturing companies offering data-based services. ANZAM 2018 Proceedings, 21–41.

Main contact: Philipp Osterrieder

Since nearly 20 years, the Institute of Data Analysis and Process Design (IDP) focuses on harvesting the potential of data in various business applications. Its main competencies are in statistical data analysis and design of business processes in the context of digitalization. Within Data+Service, IDP is the leading academic institution in three topic areas:

• Smart Services
• Predictive Maintenance
• Statistical Data Analysis

Smart Services: IDP has many years of experience in digital innovation projects, in particular with SMEs, with the objective to design customer-centric digital services. The combination of data science and service design is a core element of our approach, enabled by the unique mix of competencies in customer-centred service design, data analysis and business processes. The leading question is: Which user jobs or challenges can be improved by data-driven services and how can such services be concretely implemented? The field of smart service engineering is represented by Dr. Jürg Meierhofer (lead of the Smart Services Expert Group), Dr. Thomas Herrmann and Prof. Dr. Christoph Heitz.

Predictive Maintenance: We develop algorithms and tools to improve maintenance operations by data-based decision making. Data science methods (machine learning, statistical modeling) give new insights, but this has to be plugged in into maintenance processes. This is enabled by our strong background in maintenance engineering with more than 15 years research experience and a leading role among Swiss universities.

This field is represented by Dr. Lilach Goren (lead of the Predictive Maintenance Expert Group), Dr. Braulio Barahona (lead of the Predictive Maintenance Expert Group), and Prof. Dr. Christoph Heitz

Statistical Data Analysis: Applied statistics in real-world applications is a core competency of the IDP, with around 15 researchers and nearly 20 years research history. We use classical statistical methods such as Generalized Linear Models and Random Forest, but also machine learning techniques.

Education / further education:

Further Education

• CAS Datenanalyse
• CAS Statistical Modeling
• CAS Data Product Design
• CAS Industrie 4.0

Master level courses

• Servitization of manufacturing
• Service Operations and Management
• Business Analytics
• Advanced Statistical Data Analysis

Bachelor level

various courses in statistical data analysis, service engineering, business process simulation, maintenance, mathematical optimization.

Research questions addressed:

• How can data and analytics be applied to create value in B2C (individual users) and B2B (business users, often in manufacturing)?
• How can data-driven business models be designed?
• How can maintenance decisions be improved by data and analytics (predictive maintenance)?
• How can relevant knowledge be extracted from real-world data, with methods ranging from classical statistical modeling to machine learning?

Selected research project(s)

Smart Services:

• Data Science für KMU (Data4KMU)
• Development of a station-independent e-bike sharing service with user co-production

Predictive Maintenance

• Machine Learning Based Fault Detection for Wind Turbines
• Decision Support System For Predictive Maintenance of Laser Cutting Machines

Statistical Data Analysis in different business-related application fields

• Entwicklung von Algorithmen zur Analyse von Fussballspielern und Spielsituationen anhand von Bewegungsdaten
• Umsatzprognosen für die Gastronomie
• Predicting customer behavior by combining freetext information with structured customer data

Main contact: Christoph Heitz

Huge amount of data is collected in sports related activities. National and international sports federations are keen to leverage their data. With 80% of all international federations residing in Switzerland and with the HSLU having one of the largest Data Science master’s programs in Europe, our Sports Analytics Community is ideally situated to tackle the big data challenges in sports: increasing volume of data and its variety, velocity and veracity.

Variety: 

There are many different sports, each having its specific metrics and data scheme. There are medical, physical, tactical data, video footage, signal and event data, as well as aggregated statistics. Data comes from many different sources, in different formats and with different semantics. 

Velocity: 

Furthermore, sport is a very short-lived and dynamic domain. Data needs to be acquired, processed and used at a fast rate, in real-time or near real-time. In addition, this environment also requires a great deal of agility. 

Veracity: 

Many new data-generating products are flooding the sports analytics market, but often the metrics are developed without the necessary domain expertise, because the dialog between domain experts, data scientists and engineers is missing.

We are dedicated to developing solutions to all these challenges and making them available to sports organizations.

We offer our partners:

• Data scientists from HSLU and sport scientists from SFISM to find solutions for particular industry problems
• Systematic and validated approaches to modelling performance
• State of the art sports analytics research
• Efficient exchange between sports organizations, sport scientists and data scientists

Sports organizations with close ties to us are: 

• Swiss Federal Institute of Sport
• Swiss Olympic
• Swiss Football Association 
• Swissski
• Swiss Ice Hockey Federation
• Swiss Athletics
• International Ice Hockey Federation 

Strong collaboration with the industry ensures that solutions create an impact on the sports analytics market. We also work closely with the following companies: 

• Advanced Sports Instruments (ASI), Lausanne
• Asport (Swisscom), Zürich
• Axiamo, Biel
• Deepsports, Zürich
• Dartfish, Fribourg
• Force8, Zürich
• Begasoft, Bern
• Biznet, Schönbühl
• Datasport, Obergerlafingen

Main Contact: Martin Rumo