The role of the Academic Members of the Alliance is to support the community with their specific competencies. For making these “centers of competencies” tangible, each Academic Member describes specifically their field of expertise.
Topic Leaders are listed individually by name here with the specific area of expertise they represent. They are the prime contact point for requests within the Alliance with respect to their topic.
UNIFR eXascale Infolab (XI) is a research group at the University of Fribourg, Switzerland. We design, build and deploy next-generation infrastructures for Big Data, with a focus on social, scientific, and linked data.
Teaching, training and further education
Swiss Joint Master of Science in Computer Science: http://mcs.unibnf.ch/
- Big Data Infrastructures
- Representation Learning for Complex Graphs
- Deep Probabilistic Modeling for Debugging Noisy Data
- Relation Extraction Using Distant Supervision
- Efficient Missing Blocks Recovery for Large Time Series
- Privacy-Preserving Social Media Data Publishing
Main contact: Prof. Dr. Philippe Cudré-Mauroux (https://exascale.info/phil/)
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).
Blockchain technologies in supply chain management, Blockchain technologies for business ecosystems, industry 4.0 & digital transformation, supply chain management, lean management.
- 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.
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.
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.
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.
Current research topics include:
- Ethical issues of Artificial intelligence and Big Data
- Ethics of Cybersecurity
- Creating game-based tools for ethics training
- 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
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 firstname.lastname@example.org, 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
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.
Embedded Machine Learning, The Challenge of Labellization, Hierarchical system, Ultra-Low Power Vision Systems and Smart Optics
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.
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 email@example.com.
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 of the FFHS. Her research currently focuses on applications of machine learning to diverse kind of data.
The Laboratory for Web Science (LWS) develops new algorithms in the context of recommendation systems in tourism or for recruiting. 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.
- Recommender System in Tourism
- Blockchain technology for recruiting
- Healthcare Informatics
- Machine Learning in Environment
- Energy fields
Psychometric Recommendation Engine (PRE) for Multimedia Service Platforms (CTI Nr. 16257.1 PFES-ES, funding 349940)
Development of a large-scale recommender systems based on users’ psychometric profiles.
An infrastructure for a location and context based recommendation system (Georec)– Pilot Project (Funded by FFHS, funding: 63.000 CHF)
M. Blattner, B-rank: A top N recommendation algorithm, in Proceedings of the 1st International Multi-Conference on Complexity, Informatics and Cybernetics. pp 336-341, 2010
Künstliche Intelligenz im Schweizer Gesundheitswesen – Chancen und Herausforderungen, Joachim Steinwendner, Martina Perani, IT for Health, Netzwoche (https://www.netzwoche.ch/news/2018-09-12), September 2018
Deep Learning als Chance für KMUs?, Beat Tödtli, Netzwoche, August 2017
Beatrice Paoli, Monika Laner, Andrea L. Sablone, Hagen Worch, «Big Data, Big Chance», Computerworld, Oktober 2016
Main contact: Beatrice Paoli
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
- BSc/MSc/Phd in Computer Science
- BSc/MSc/Phd in Information systems and service science
- BSc/MSc/PhD in in Informatics for humanities
- Continuous education programs: Blockchain, Information Security, Data Governance, Web development, Information Systems Management, IoT. More information
- Sessions on AI and Software development. More information
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.
- 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
- 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.
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:
- 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
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)
- Data Science für KMU (Data4KMU)
- Development of a station-independent e-bike sharing service with user co-production
- 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