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. 

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, main contact Prof. Thilo Stadelmann.

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).

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.

Research questions
Recommender System in Tourism
Further: Blockchain technology for recruiting, Healthcare Informatics, Machine Learning in Environment and Energy fields

Selected projects
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)

Selected publications
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 (, 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