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.
- Blockchain in Supply Chain
- Business Models and Supply Chain M.
- Consumer Psychology
- Data-driven Value Creation
- Data Ethics
- Deep Learning & Analytics
- Digital Health Solutions
- Digitalization in Banking and Finance
- GeoHealth Analytics
- Geoscientific Application
- Information Systems
- Interconnected Intelligence
- Network Science & Complex Systems
- Predictive Maintenance
- Sensor Data
- Smart Services
- Smart Marketing & Communication
“Blockchain in Supply Chain” – ZHAW AGM
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.
“Business Models and Supply Chain Management” – FHGR
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).
Digital Supply Chain Management
Digital Business Management
Service Design and Innovation
New Business Development
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
- 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.
“Consumer Psychology, Product and Service Development, Applied Natural Language Processing” – FHNW School of Applied Psychology
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.
“Data-driven value creation” – BFH Institute for Applied Data Science & Finance
The group on applied data science and finance is at the forefront of integrating cutting-edge data science methodologies into the complex world of finance. We focus on a range of topics, from developing advanced predictive models for stock market movements and exploring the potential of machine and deep learning in credit risk assessment, to ensuring complex models offer explainable and trustworthy outcomes for the various stakeholders in the financial industry. By leveraging the power of complex, heterogeneous data available to financial agents, the group aims to provide actionable insights that can drive investment strategies, optimize financial operations, and shape the future of financial decision-making.
COST FinAi: https://fin-ai.eu/
SNF project “Network-based credit risk models in P2P lending markets”
SNF project “Anomaly and fraud detection in blockchain networks”
SNF project: Narrative Digital Finance
Innosuisse: DataInc – Intelligent Data Integration and Cleaning: https://www.aramis.admin.ch/Grunddaten/?ProjectID=49856
- Lyócsa, Š., Vašaničová, P., Hadji Misheva, B. et al. Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets. Financ Innov 8, 32 (2022). https://doi.org/10.1186/s40854-022-00338-5
- Hadji Misheva, B., Hirsa, A. and Osterrieder, J. and Kulkarni, O. and Fung Lin, S. (2021) Explainable AIin Credit Risk Management (March 1, 2021). Available at SSRN: https://ssrn.com/abstract=3795322 orhttp://dx.doi.org/10.2139/ssrn.3795322
- Jander, M. and Osterrieder, J. (2023). Application of Long Short-Term Memory Networks to S&P500 Volatility Prediction. SSRN
- Osterrieder, J., & Seigne, M. (2023). The Mysteries of Share Buyback Execution: Trading Anomalies, Benchmarks, and Psychological Misconceptions. Benchmarks, and Psychological Misconceptions (July 15, 2023).
- Hadji Misheva, B., Jaggi, D., Posth, J.-A., Gramespacher, T., & Osterrieder, J. (2021). Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey. Frontiers in Artificial Intelligence.
“Data Ethics” – University of Zurich, Digital Society Initiative
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.
“Deep Learning & Analytics” – ZHAW Datalab
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 150 researchers from 12 institutes and centers across three departments with 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. The ZHAW Datalab brings these and other expertise into play at several expert groups or through involvement in other network events and conferences such as Swiss Text or Swiss Conference on Data Science (SDS).
Examples of our work include:
INODE (Intelligent Open Data Exploration): This project funded by the European Commission has the goal to provide extensive access to open datasets through natural language queries in the fields of cancer biomarker research, research and innovation policy making and astrophysics.
A book on Applied Data Science: Together with colleagues from all over the world, we wrote a book that brings together many examples and lessons learned about how to approach data science from the perspective of a data-driven business.
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 are running a PhD program in Data Science together with the University of Zurich and are actively collaborating with numerous companies and research institutions. You can get in touch via firstname.lastname@example.org – the main contacts are Dr. Manuel Dömer and Prof. Dr. Kurt Stockinger.
“Interdisciplinary Approaches for Patient-centered Digital Health Solutions” – ZHAW Digital Health Lab
We believe that digital health has tremendous potential to enhance the well-being of everyone in our population. In aiming to make healthcare more accurate, personalized, and preventive, digital health focuses on the individual.
We face the challenges of digitalization in the healthcare sector by developing solutions for healthcare innovations. Through its broad base within ZHAW, the ZHAW Digital Health Lab combines the expertise of specialized research groups in technology, healthcare, and business. This forms a unique breeding ground for digital health solutions throughout Switzerland.
We aim to be a leading, internationally recognized Swiss competence center in digital health. To achieve this, we rely on interdisciplinary cooperation and scientific exchange between our supporters. We work closely with service providers, industry representatives, health insurance companies, policy-makers, and other research partners to facilitate research, innovation, and technology transfer.
The ZHAW Digital Health Lab creates innovations for the healthcare sector that focus on people. We improve health care and promote technology transfer between science, industry and society.
As a competence network, we work together with our partners to develop solutions to current challenges in the healthcare sector. In doing so, we exploit the potential of our interdisciplinary approach. We rely on result-oriented cooperation with our network and are valued as a competent 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.
“Digitalization in Banking, Finance and Insurance” – ZHAW ABF
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
“GeoHealth Analytics” – Fernfachhochschule Schweiz, Laboratory for Web Science
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.
- GeoHealth Analytics
- Data Science for Energy, Nature and Materials
- 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
Main contact: Beatrice Paoli
“Geoscientific Applications” – Ostschweizer Fachhochschule, Institute for Software, Geometa Lab
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.
Training and further education: https://giswiki.hsr.ch/Kurse
“Smart Information Systems” – ZHAW InIT
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“
“Interconnected Intelligence and Smart Vision Systems” – Swiss Center for Electronics and Microtechnology (CSEM)
L. Andrea Dunbar is the Data & AI Focus Activity Manager at CSEM and a Professor of Practice at EPFL. She is an active board member, supports deep tech start-ups and is a member of the TinyML Swiss Group. She is on the technical committee for IEEE and a TinyML conferences. She is co-leading the Expert Group “Machine Learning Clinic” within the Alliance.
She received her PhD in Optics in 2002 from Trinity College Dublin, Ireland, her BSc in Physics is from St-Andrews University Scotland and was educated at Charterhouse School in England. Andrea obtained an eMBA from EPFL in 2017. She has published more than 80 papers (h-index 20) and has 10 patents.
Her current research areas include intelligent vision systems, resource constrained machine learning, and hyperspectral imaging, hierarchical system optimization and neuromorphic computing. Her group over the last years specialized in constrained edge vision systems, e.g. ultra-low power, low latency environments.
She is teachings data science for business, innovation management and digital
CSEM is a Swiss technology innovation center developing advanced technologies which it then transfers to industry to strengthen the Swiss economy. The non-profit orientated, public-private organization is internationally recognized, and works to support the disruptive activities of companies in Switzerland and abroad.
Constrained &Embedded Machine Learning, optical neural networks, neuromorphic computing, hierarchical optimization and photometric stereo.
GRAINVIEW (CTI project), 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.
ANDANTE (EU Project) 2019-2024
Technology for Edge applications: The future products in the Edge IoT domain stand on HW/SW platforms based on efficient Neuromorphic, artificial and spiking neural networks, solutions
VIVALDI (Innosuisse project), 2021-2023
Project using an intelligent vision system to make logistics tracking in the harsh environment of the steel industry.
NEOS (Innosuisse project), 2020-2023
Project on Machine learning to measure eye movement to improve diganostics of brain disorders using AI algorithm with VR headset.
MIEWA (Innosuisse project) 2023-
Project on multispectral analysis of pigments to help art identification with start-up from group (www.MATIS.art)
Improved illumination-induced multispectral Imaging system and machine learning approach for the examination of artworks
M Didier, G Bernasconi, S Blanc, V Carrel, PA Héritier, P Pad, L. A Dunbar
Optics for Arts, Architecture, and Archaeology (O3A) IX 12620, 1262007 (2023)
Privacy-preserving image acquisition for neural vision systems
Y Sepehri, P Pad, C Kündig, P Frossard, LA Dunbar
IEEE Transactions on Multimedia (2022)
Optimizing the consumption of spiking neural networks with activity regularization
S Narduzzi, SA Bigdeli, SC Liu, LA Dunbar
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2022)
Defect segmentation for multi-illumination quality control systems
D Honzátko, E Türetken, SA Bigdeli, LA Dunbar, P Fua
Machine Vision and Applications 32, 1-16 (2021)
Efficient neural vision systems based on convolutional image acquisition
P Pad, S Narduzzi, C Kundig, E Turetken, SA Bigdeli and L. Andrea Dunbar – Proceedings of the IEEE/CVF Conference on Computer Vision, CVPR, (2020)
Main contact: Andrea Dunbar
“Network Science & Complex Systems” – SUPSI
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.
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)
“Smart Services, Predictive Maintenance, and Statistical Data Analysis” – ZHAW IDP
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
“Making sense of sensor data for supporting behavioural change” – OST
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.
- 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
Minors in data science in the BSc in Industrial Engineering (start spring 2020) as well as the BSc in Business Information Systems (planned).
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.
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
- 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.
“Smart Services” – University of St. Gallen, ITEM
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
“Smart Marketing & Communication with Deep Learning and NLP” – Hochschule Luzern, Institut für Kommunikation und Marketing (IKM)
Who we are
Since 2019 the Institute for Marketing and Communication at HSLU has had a competence group specializing in Marketing & Communication Technologies, Analytics and Data Engineering, i.e., Smart Marketing and Communication. The interdisciplinary competence group is characterized by application- and practice-oriented research and works closely with industry and universities. The members of the competence group participate in various Data Innovation Alliance expert groups.
Topics covered by the competence group include:
- Customer, Marketing & Communication Analytics with Natural Language Processing & Deep Learning
- Recommender Systems
- Chatbot Systems – Large Language Models (LLMs) / generative AI
- Digital Ethics
- Augmented CRM
- Sharing and Exchange Platforms
What we teach
- Modules in MSc in Applied Information and Data Science
- Database Management for Data Scientists
- Data Warehouse and Data Lake Systems
- Big Data in the Cloud
- Modern Data Engineering
- Data Strategy and Governance
- Hands on Visualisation for Data Science
- Computational Language Technologies
- Recommender Systems
- Customer Data Analytics
- Ethical Issues of Big Data
- Designing and Managing Data Science Projects
- Continuing education
- Algorithmic Nudging for Sustainability in E-Commerce
- AI-based Persona-Builder
- Lock-In Effekte im E-Food
- “Let me help you!”: German-language chatbot system for emotional support of older people in the fight against loneliness
- Data-driven stylistic business book summarization for personalized reading and learning using natural language generation (NLG) and text style transfer (TST)
- Swiss Digital Ethics Compass
- Clean Energy EXchange (CEEX) – Development of AI based Trading Advisor to operate Virtual Topology and Dynamic Grid Prices
- Monitor Suffizientes Sharing Schweiz
- Lu, Guang; Dollfus, Christian; Wozniak, Thomas; Fleck, Matthes & Heroth, Timo (2022). Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing. International Journal of Data Science and Analytics, 1-22.
- Griesser, Simone & Guang, Lu (2022). Exploring owner-based brand personality on Facebook and LinkedIn with Natural-Language-Processing (NLP). 2022 9th Swiss Conference on Data Science (SDS), 9th, 13-18. doi: 10.1109/SDS54800.2022.00010
- Wozniak, Thomas; Stieger, Mirjam; Schaffner, Dorothea; Schu, Matthias & Lu, Guang (2022). Algorithmic Nudging for Sustainability in E-Commerce: Conceptual Framework and Research Agenda. 35th Bled eConference Proceedings, 1.