Professor Bart Baesens is a professor of Big Data & Analytics at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, credit risk modeling, fraud detection, and marketing analytics. He co-authored more than 250 scientific papers and 10 books some of which have been translated into Chinese, Kazakh and Korean, and sold more than 20,000 copies of these books world-wide. Bart received the OR Society’s Goodeve medal for best JORS paper in 2016 and the EURO 2014 and EURO 2017 award for best EJOR paper. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.
Bart can be contacted at email@example.com. You can also follow @DataMiningapps on Twitter, visit Bart's LinkedIn page or check out DataMiningApps on Facebook.
Professor Wouter Verbeke, Ph.D., is associate professor of data analytics at Vrije Universiteit Brussel (Brussels, Belgium). He graduated in 2007 as a Civil Engineer and obtained a Ph.D. in applied economics at KU Leuven in 2012. His research is situated in the field of prescriptive and profit-driven analytics and is driven by real-life business applications in fraud, customer relationship, credit risk, supply chain, and human resources management.
In 2014, he won the distinguished EURO award for best article published in the European Journal of Operational Research in the category 'Innovative Applications of O.R. His work has been published in established international scientific journals such as IEEE Transactions on Knowledge and Data Engineering and European Journal of Operational Research. He has authored two books, entitled 'Fraud Analytics Using Descriptive, Predictive & Social Network Techniques' and 'Profit-driven Business Analytics', published by Wiley.
See http://mobi.vub.ac.be/mobi/team-member/prof-wouter-verbeke/ for more information.
Professor Stefan Lessmann received a diploma in business administration and a PhD from the University of Hamburg in 2002 and 2007, respectively. He worked as a senior lecture in business informatics at the Institute of Information Systems of the University of Hamburg. Since 2008, Stefan is a guest lecturer at the School of Management of the University of Southampton, where he gives under- and postgraduate courses on quantitative methods, electronic business, and web application development.
Stefan completed his habilitation on decision analysis and support using ensemble forecasting models in 2012. He then joined the Humboldt-University of Berlin in 2014, where he heads the Chair of Information Systems at the School of Business and Economics. Since 2015, he serves as associate editor for the International Journal of Business Analytics, Digital Finance, and the decision analytics department of Business and Information System Engineering (BISE). Stefan secured substantial amounts of research funding and published several papers in leading international journals and conferences, including the European Journal of Operational Research, the IEEE Transactions of Software Engineering, and the International Conference on Information Systems. Stefan’s research concerns the support of managerial decision-making using quantitative empirical methods. He specializes on applications of (deep) machine learning techniques in the broad scope of marketing and risk analytics. He actively participates in knowledge transfer and consulting projects with industry partners; from start-up companies to global players and not-for-profit organizations.
Professor Christophe Mues is a professor at the School of Management of the University of Southampton (UK). One of his key research interests is in the business intelligence domain, where he has investigated the use of decision table and diagram techniques in a variety of problem contexts, most notably business rule modeling and validation. Two other key research areas are knowledge discovery and data mining, with a strong interest in applying data mining techniques to financial risk management and, in particular, credit scoring. He has cooperated with public services, companies, and financial institutions in each of these areas, and his findings have been published in various journals and presented at international conferences. He has taught training courses on Credit Scoring for Basel II in several European and Asian countries, all in collaboration with SAS.
Tim Verdonck is a professor at the Department of Mathematics of KU Leuven. He has a degree in Mathematics and a PhD in Science: Mathematics, obtained at the University of Antwerp. During his PhD he successfully took the Master in insurance and the Master in financial and actuarial engineering, both at KU Leuven. He belongs to ROBUST@Leuven, the research group on Robust Statistics and his research focuses on the adaptation and application of robust methods for financial, actuarial and economic data sets.
Prof. dr. David Martens is the head of the Applied Data Mining research group at the University of Antwerp. His research focuses on the development and use of predictive data mining techniques for a better decision making process.
Prof. dr. Seppe vanden Broucke is an assistant professor at the department of Decision Sciences and Information Management at KU Leuven. Seppe received his PhD in Applied Economics at KU Leuven, Belgium in 2014.
Seppe's research interests include business data science and analytics, machine learning, process management, and process mining. Seppe has authored several books on topics such as database management and web scraping and his research been published in well-known international journals and presented at top conferences.
See http://www.seppe.net for more information.
María Óskarsdóttir is a post-doctoral researcher and an active R user. She holds a PhD in Business Economics from KU Leuven (Belgium). Her research puts focus on applying social network analytics techniques for predictive modeling in marketing, credit scoring and insurance.