I am currently working as an assistant professor of Data Analytics at the Marketing, Innovation, and Organization Department (Research Group Data Analytics) at Ghent University. Besides that, I am also Visiting Professor at the University of Namur and a member of the CVAMO research group at Flanders Make. Prior to joining Ghent University, I worked as an assistant professor at the University of Edinburgh Business School and postdoctoral researcher at the KU Leuven. I studied Business Engineering and received a PhD in Business Economics at Ghent University in 2018. I have taught a wide range of data analytics courses ranging from basic statistics and database management to advanced predictive analytics and social media and web analytics. My research focuses on applications of descriptive, predictive and prescriptive analytics in social media, customer relationship management, hospitality, sports and production and manufacturing. My research has been published in several well-known international journals such as the European Journal of Operational Research, Omega, Decision Sciences, among others.

📫 Get in touch:

• E-mail: [email protected]
• LinkedIn: www.linkedin.com/in/matthias-bogaert-79a28148
• Twitter (all expressed opinions are my own): https://twitter.com/matthbogaert

I am currently guest editor of a special issue Ensemble Learning for Operations Research & Business Analytics in Annals of Operations Research: LINK to the call for papers.

• Janssens, B., Schetgen, L., Bogaert, M., Meire, M., & Van den Poel, D. (2023). 360 Degrees Rumor Detection: When Explanations Got Some Explaining To Do. European Journal of Operational Research. (LINK)
• Bogaert, M., & Delaere, L. (2023). Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art. Mathematics, 11(5), 1137. (LINK)
• Janssens, B., Bogaert, M., Bagué, A., & Van den Poel, D. (2022). B2Boost: Instance-dependent profit-driven modelling of B2B churn. Annals of Operations Research, 1-27. (LINK)
• Janssens, B., Bogaert, M., & Maton, M. (2021). Predicting the next Pogacar: a data analytical approach to detect young professional cycling talents. Annals of Operations Research, 1-32. (LINK)
• Janssens, B., Bogaert, M., & Van den Poel, D. (2021). Evaluating the influence of Airbnb listings’ descriptions on demand. International Journal of Hospitality Management, 99, 103071. (LINK)
• PhD Dissertation

• Baron, E., Janssens, B., & Bogaert, M. (2023). Bike2Vec: Vector Embedding Representations of Road Cycling Riders and Races. arXiv preprint arXiv:2305.10471.(LINK)
• Janssens, B., & Bogaert, M. (2021). Imputation of non-participated race results. 8th Workshop on Machine Learning and Data Mining for Sports Analytics, ECML/PKDD 2021 Workshop.(LINK)

For a full list of all my publications, you can view my CV here.

My area of research lies at the interface of IT (databases), algorithms (machine learning, AI, optimization, and econometrics models) and business applications. Hence, my research focusses on applying descriptive, predictive and prescriptive analytics to business-related problems. I believe that the main goal of data analytics should be to use data to gain insight and eventually increase business performance.

My methodological interests include:

• Big data
• Predictive modeling
• Ensemble learning
• Text mining
• Natural Language processing
• Recommender systems
• Deep learning
• Reinforcement learning.

My theoretical interests include, but are not limited to:

• Social media
• CRM (acquisition, churn and retention management, cross-selling)
• Hospitality (e.g., reviews)
• Finance
• Sports
• Production & Manufacturing

Current courses:

• Social Media and Web Analytics (Course Specifications)
• Advanced Predictive Analytics (Course Specifications)
• Predictive and Prescriptive Analytics (Course Specifications)
• Business Analytics and Big Data (Course Specifications)

Previous courses:

• Business Research Methods (at the University of Edinburgh)
• Predictive Analytics and Modeling of Data (at the University of Edinburgh)
• Data Mining (at the University of Edinburgh)
• Principles of Database Management (at the KU Leuven)

• AggregateR: CRAN link / Github link
• DecorateR: CRAN link / Github link
• 🐍 B2Boost, in colab with Dr. Bram Janssens Github link
• 🐍 empulse, in colab with Shimanto Rahman PyPi link / Github link

I work at the Research Group Data Analytics at the Faculty of Economics and Business Administration of Ghent University.The research group Data Analytics engages in teaching and research on the use of data to improve and optimize business processes. This research is based on techniques such as statistics, data mining and machine learning (e.g. deep learning and reinforcement learning) and optimization. The research group focuses on methodological as well as technical innovations and applications in a large number of application areas.

Cost and Value Analytics, Models & Optimization (CVAMO) is a new research lab that groups 10 professors and about 28 pre- or postdoctoral researchers form different departments linked to the degree in Business Engineering at the faculty of Economics and Business Administration (Ghent University). The goal of CVAMO is to successfully introduce business analytics, cost and value driven modeling and technological innovation in ther manufacturing and production setting.

CVAMO is a core lab in the Strategic Research Center Flanders Make (www.flandersmake.be), that groups research labs from all Flemmish universities. Based on high-tech research, Flanders Make offers active support to companies in the manufacturing industry to develop and optimize products and production processes.

Below you can see a picture of the members in our research group (beginnig at the bottom row, from right to left): Shimanto Rahman (assitant), Prof Dr Matthias Bogaert, Juliana Sanchez Ramirez (PhD, shared with IESEG School of Management), Dylan Van Mulders (assitant), Xiao Wang (PhD), Lukas De Kerpel (assistant), Yaël De Rocker (PhD), Prof Dr Dries Benoit, Athur Thuy (PhD), Brammert Termont (PhD), Dr Bram Janssens (postdoc), Prof Dr Dirk Van den Poel, Matteo Ballegeer (PhD), Wannes Janssens (PhD), Toon Van Camp (PhD).