Patrick Wessa

Research Interests

My current research activities focus on leading the Vitamin AI research project which uses digital biomarkers for the early detection of Alzheimer's Disease (AD) and monitoring the disease's progress. In particular, we attempt to predict Mild Cognitive Impairment (MCI) and AD based on eye-tracking, facial micro-expressions, speech recordings, and a variety of neurocognitive tests. The data analysis relies on advanced statistical modelling (Artificial Intelligence / Machine Learning) and combines a large collection of digital biomarkers.

I am also involved in helping enterprises from various industries to build innovative solutions (with AI/ML), and in teaching several statistics and information science courses at the University of Leuven. One of my (long-term) pet projects focuses on innovative, statistical Web Technologies and Applications for the purpose of reproducible, scientific research and collaborative, academic education. In particular, my work focuses primarily on Compendium Technologies, Reproducible Statistical Computing, and associated Meta Databases.

Projects

Here are a few projects I am currently working on:

• early detection of Alzheimer's Disease based on digital biomarkers
• robo-advisors for wealth management
• reproducible & collaborative writing (actually I think of this as a part of "reproducible computing")
• constructivist, educational technology (Computer Assisted Learning and Technology Enhanced Learning)
• applied statistics (incl. reproducible statistical computing, modelling, and analysis)
• cloud computing applications for medical decision making purposes (diagnosis, monitoring, and research)
• software & database design, engineering, and development
• technology acceptance, usability and evaluation of Information Systems
• educational & research technology
• automated Big Data Analytics

In summary, I am interested in multidisciplinary, scientific questions from (almost) any field that can be studied through solid, statistical research and innovative information technology.

Websites

Here you find a list of some of my websites:

• Alzheimer's Research: vitamin-ai.com
• Combining OLAP & OLTP Databases in medical information systems (ispm.wessa.net -- URL is subject to change)
• Statistical Software based on the R Framework: www.wessa.net
• Statistical, Reproducible Computing Repository: www.freestatistics.org
• Resources about Statistics - Econometrics - Forecasting: www.xycoon.com
• Reproducible Computing for Large & Complex Datasets: currently under construction (see www.freestatistics.net)
• RFC: an innovative Virtual Learning Environment which is content-centered rather than course-centered (the URL of the RFC home page is supernova.wessa.net/rfc/). If you are wondering why this makes any difference, feel free to read about it here and here.

Work in progress

Here you find a list of some papers that are under development:

• Digital Biomarkers in early AD detection (multiple papers)
• A new approach in parameter optimisation of Time Series Models (beating all conventional ML algorithms)
• AI learns to detect lies in real-time conversation
• Detecting false answers in surveys
• Discourse Analysis of Peer Reviews within a Social Networking Paradigm
• Social Interaction and Learning: Freeriding or Construction of Knowledge?
• Fraud Detection in Collaborative Writing
• Football Attendances: a near perfect prediction model
• Technology Acceptance of Learning Technology
• Scientific Collaboration in Neuroscience: A Scientific Collaboration Model for Data Sharing and Federated Computing

Recent Publications

This is an out-of-date selection of some papers:

• Wessa, P. (2008). Measurement and control of statistics learning processes based on constructivist feedback and reproducible computing. Proceedings of the international conference on virtual learning, Siveco: Bucharest. (best paper award)
• Wessa, P. (2008). How reproducible research leads to non-rote learning within a socially constructivist e-learning environment. In G. Papadopoulos (Eds.), Proceedings of the 7th ECEL, Reading: Academic Conferences Limited. (best paper award)
• Wessa, P. (2008). Assessment of reproducible computing as an e-learning tool in statistics education. In G. Richards (Eds.), Proceedings of the world conference on e-learning in corporate, government, healthcare, and higher education 2008, Chesapeake: Association for the Advancement of Computing in Education.
• Wessa, P. (2008). Learning statistics based on the compendium and reproducible computing. Proceedings of the world congress on engineering & computer science 2008, Hong Kong: Newswood. (best paper award)
• Milis, K., Wessa, P., Poelmans, S., Doom, C., Bloemen, E. (2008). The impact of gender on the acceptance of virtual learning environments. Proceedings of the international conference of education, research and innovation, Madrid.
• Wessa, P., Poelmans, S., Milis, K., Bloemen, E., Doom, C. (2008). Usability and acceptance of e-learning in statistics education, based on the compendium platform. Proceedings of the international conference of education, research and innovation, Madrid: International Association of Technology, Education and Development.
• Wessa, P., Van Stee, E. (2008). The Xycoon stock market game : virtual learning environment or real-life laboratory?. Proceedings of the international conference of education, research and innovation, Madrid.
• Wessa, P. (2008). How to objectively rate investment experts in absence of full disclosure? An approach based on a near perfect discrimination model. Advances in Methodology and Statistics, 5(1), 19-32.
• Wessa, P. (2009). Discovering computer-assisted learning processes based on objective exam score transformations. Procedia - Social and Behavioral Sciences, 1(1), 2589-2594.
• Wessa, P. (2009). How reproducible research leads to non-rote learning within socially constructivist statistics education. Electronic Journal of e-Learning, 7(2), 173-182.
• Wessa, P. (2009). Quality control of statistical learning environments and prediction of learning outcomes through reproducible computing. International Journal of Computers, Communications & Control, 4(2), 185-197.
• Wessa, P. (2009). A framework for statistical software development, maintenance, and publishing within an open-access business model. Computational Statistics, 24(2).
• Wessa, P. (2009). Reproducible computing: a new technology for statistics education and educational research. In S. Ao (Eds.). IAENG transactions on engineering technologies. 2 : Special edition of the world congress on engineering and computer science, San Francisco, CA, 22-24 October 2008 (pp. 86-97). Melville, New York: American Institute of Physics.
• Wessa, P. (2009). Exploring social networks in reproducible computing and collaborative assignments. In F. Salajan (Eds.), Proceedings of the 4th international conference on e-learning (ICEL), Reading, UK: Academic Conferences Limited.
• Wessa, P., Baesens, B. (2009). Fraud detection in statistics education based on the compendium and reproducible computing. In M. Burgin, M. Chowdhury, C. Ham, S. Ludwig, W. Su, S. Yenduri (Eds.), IEEE Proceedings of the world congress on computer science and information engineering, Washington: IEEE Computer Society.
• Wessa, P. (2009). How to model the design efficiency of the virtual learning environment?. In M. Vlada, G. Albeanu, D. Mircea Popovici, R. Jugureanu, O. Istrate (Eds.), Proceedings of the 4th international conference on virtual learning, Romania: Bucharest University Press.
• Wessa, P., Van Dijk, R. (2009). Perceived versus actual quality in student-centered statistics education. In F. Mulder, F. Pannekoek, D. Vincent, C. Holmberg, P. Henderikx (Eds.), Proceedings of the 23rd ICDE world conference on open learning and distance education, Oslo, Norway: International Council for Open and Distance Education.
• Wessa, P., Baesens, B. (2009). Explorative data mining of constructivist learning experiences and activities with multiple dimensions. Proceedings of the international conference on computer and instructional technologies, Las Cruces, NM, USA: World Academy of Science, Engineering and Technology.
• Wessa, P., Van Stee, E. (2009). Role and effect of reproducible computing technology in statistics learning. In A. Bilsel, M. Garip (Eds.), Proceedings of the frontiers in science education research conference (FISER'09), Famagusta TRNC (North Cyprus): Eastern Mediterranean University Press.
• Wessa, P., De Rycker, A. (2010). Reviewing peer reviews: a rule-based approach. In I. Ismail (Eds.), Proceedings of the 5th international conference on e-learning (ICEL), South Oxfordshire, UK: Academic Conferences International.
• Wessa, P., Poelmans, S., De Rycker, A., Holliday, I. (2010). Design and implementation of a web-based system for formative peer review in statistics education. ICERI 2010 proceedings, Madrid: IATED (International Association of Technology, Education and Development).
• Wessa, P., Poelmans, S., Milis, K., Van Stee, E. (2010). Modeling educational technology acceptance and satisfaction. Proceedings of EDULEARN09, Barcelona: International Association of Technology, Education and Development.
• Wessa, P., Holliday, I., Reddy, P. (2011). A new learning environment based on reproducible ubiquitous computing: experiences and prospects. In T. Kidd, I. Chen (Eds.). Ubiquitous learning: strategies for pedagogy, course design and technology (pp. 179-196). Charlotte: Information Age Publishing.
• Wessa, P., De Rycker, A., Holliday, I. (2011). Content-based VLE designs improve learning efficiency in constructivist statistics education. PLoS ONE, 6(10).
• Wessa P., Holliday I.E. (2012) Does Reviewing Lead to Better Learning and Decision Making? Answers from a Randomized Stock Market Experiment. PLoS ONE, 7(5).
• Poelmans, S., & Wessa, P. (2013). A Constructivist Approach in an e-Learning Environment for Statistics, Interactive Learning Environments status: accepted.
• Wessa, P. Poelmans, S., Holliday, I. (2013), Analysis of Constructivist, Network-Based Discourses Analysis: Concepts, Prospects, and Illustrations, In (Eds.). Innovative Methods and Technologies for Electronic Discourse Analysis
• Wessa, P., Poelmans, S., Holliday, I. (2014). Analysis of Constructivist, Network-Based Discourses: Concepts, Prospects, and Illustrations. In: Lim H., Sudweeks F. (Eds.), bookseries: Advances in Human and Social Aspects of Technology (AHSAT), Innovative Methods and Technologies for Electronic Discourse Analysis, Chapt. 2. Hershey (USA): IGI Global, 19-42.
• Wessa, P., Holliday, I. (2015). Reproducible Computing. In: Khosrow-Pour M. (Eds.), Encyclopedia of Information Science and Technology. Hershey: IGI Global, 6583-6591.
• Keiser, O., Blaser, N., Davies, M., Wessa, P., Eley, B., Moultrie, H., Rabie, H., Technau, K., Ndirangu, J., Garone, D., Giddy, J., Grimwood, A., Gsponer, T., Egger, M. (2015). Growth in virologically suppressed HIV positive children on antiretroviral therapy: individual and population-level references. The Pediatric Infectious Disease Journal (accepted).
• Wessa, P., Holliday, I., Vanuytbergen, S. (submitted). Numeracy Skills of Business Students and the Relationship to their Statistics Achievement. Journal of Research in Science Teaching
• Wessa, P. (forthcoming). The Hitchhiker's Guide to Reproducible Statistical Computing, ...

Contact Information

You can contact me through e-mail: patrick at wessa dot net

Short Bio

If you are looking for a short bio, here's a text that might be useful in publications:

Patrick Wessa is an information science & statistics professor (PhD, Institute for Statistics and Econometrics, University of Basel, CH) with a strong interest in information technologies. His research is located at the Leuven Institute for Research on Information Systems (LIRIS, University of Leuven, Belgium) and is mainly focused on multidisciplinary, scientific questions that can be studied through reproducible statistics and information technology. His innovations have been made freely accessible through a series of web applications that have become increasingly popular among academics and have been cited in a large number of scholarly articles. In recent years, he disseminated his findings through a variety of publication outlets in the domains of software engineering, computational statistics, computer-supported education, and multidisciplinary science.