Scholar
Stefan Lessmann
Google Scholar ID: k71Ji-YAAAAJ
Professor of Information Systems, Humboldt-University of Berlin
Citations & Impact
Citations
7,103
H-index
39
i10-index
71
Publications
20
Co-authors
10
list available
Publications
Resume (English only)
Academic Achievements
- Published several papers in leading international journals and conferences, including the European Journal of Operational Research, IEEE Transactions on Software Engineering, and the International Conference on Information Systems. Actively participates in knowledge transfer and consulting projects with industry partners, ranging from small start-up companies to global players.
Research Experience
- Since 2008, has been a guest lecturer at the School of Management of the University of Southampton, teaching under- and postgraduate courses on quantitative methods, electronic business, and web application development. Joined Humboldt-University of Berlin in 2014, where he heads the Chair of Information Systems at the School of Business and Economics.
Education
- Received a diploma in business administration and a PhD from the University of Hamburg in 2002 and 2007, respectively. Completed habilitation in the area of predictive analytics in 2012.
Background
- His work focuses on the analysis and support of managerial decision making. Much of his research is concerned with the development, application, and validation of empirical prediction models. Topics of interest include artificial neural networks, deep learning, and recurrent neural network architectures; Big Data Analytics; credit risk modeling using regression, classification, and survival analysis; ensemble models and forecast combination; marketing and e-commerce analytics (e.g., churn management, online marketing, customer targeting, real-time bidding); non-standard paradigms to learn from data (active learning, adversarial learning, learning with privileged information, PU learning, semi-supervised learning, etc.); sentiment and social network analysis; time series forecasting (e.g., in finance or demand planning) with RNNs and other machine learning methods.
