Faculty of Economics and Business Administration

Profilbild Prfessorin Greven
Faculty of Economics and Business Administration

Chair of Statistics

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Expertise

Professor Greven's expertise is particularly in statistics, data science, statistical learning, machine learning, statistical modeling, statistical inference, biostatistics, and their applications in various fields (economics, social sciences, medicine, epidemiology, engineering, linguistics, etc.). She is also well-versed in functional data analysis and statistics for object data (curves, images, shapes, etc.).

  • 2 projects on functional data analysis for sensor data with Siemens CT
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Profilbild Prof. Lessmann
Faculty of Economics and Business Administration

Chair of Information Systems

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Expertise

Professor Lessmann's research focuses on machine learning and artificial intelligence (MLAI) methodologies and their use cases in managerial decision support. Areas of interest include but are not limited to explainable AI, causal/interpretable machine learning, natural language processing, predictive analytics and time series forecasting.

He is specialized in MLAI applications in the broad scope of marketing and risk analytics. He is also actively involved in knowledge transfer, professional education and consulting projects with industry partners ranging from start-ups to global players and non-profit organizations.

http://humboldt.gmbh/forschungskooperation

Scientific Services
  • Prototyping, evaluation, and benchmarking of ML/AI-based prediction and decision models
  • Development of PD, LGD, and EAD forecasting models
  • Processing of textual data for knowledge extraction
  • Assessment of the information value of commercially available data sources (e.g., credit bureau data) for predictive modeling
  • Estimation of personalized, individual-level treatment effects
  • Professional education in ML/AI, data science and business analytics

FinTech - Based on concepts of Bayesian statistics and semi-supervised machine learning, Professor Lessmann and his team have developed a system to solve the problem of inference rejection in the lending industry. Their solutions provide significantly better risk scorecards and make it easier for lenders to predict the predictive power of a scorecard (e.g., the PD) in operation. The project was conducted with a leader in the microcredit space.

E-commerce - Together with WebTrekk and Uebermetrics, Lessmanns working group is developing an AI-driven web controlling system that examines web metric time series (page views, visits, bounce rates, checkouts, sales, etc.) and identifies the causes of observed patterns (e.g., anomalies). To this end, their system integrates deep causal detection algorithms based on reinforced learning and xAI methods. To extend the scope of their root cause explanations, they also monitor various social media streams and use advanced NLP algorithms for event detection. The unique combination of these AI concepts provides web controllers and store managers with advanced insights into their business operations.

Digital marketing - Lessman and his team have developed advanced causal machine learning models for real-time targeting of e-coupons and other digital marketing stimuli. Their optimal (i.e., profit-maximizing) methodology includes both single and multiple treatment scenarios and leverages traditional supervised learning algorithms to enable cost-effective deployment.

Professor Lessmann has taught a number of professional education courses in subject areas such as credit risk modeling, fraud detection, advanced analytics, and machine learning, to name a few. Some of these courses are offered as part of the SAS Business Knowledge Series, but are also available on the R and Python platforms. Requests for professional development in the areas of his expertise are welcome.

 

  • funding by various bodies including DFG, DAAD, IBB (e.g. Transfer Bonus, ProFIT)
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Prof. Danilov
Faculty of Economics and Business Administration

Management

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Expertise

Professor Danilov is engaged in empirical human resource management research, as well as the identification of causal relationships of the effect of human resource management tools on employee motivation and productivity. She also conducts research in organizational and human resource economics, in the field of empirical human resource management (HRM), and in the course of this she works with Big Data and People Analytics using machine learning.

 

Scientific Services
  • randomized studies (A/B tests) on incentive setting
  • work design and employee motivation
  • analysis of data (accounting, personnel, etc.)
  • "Delegation of decision making and productivity and employee satisfaction" at a world-leading energy company.
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Profilbild Härdle
Faculty of Economics and Business Administration
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Expertise

Prof. Haerdle’s main research interests are quantitative finance, esp. multivariate methods in banking and finance, dimension reduction techniques, and computational statistics. In his roles both as coordinator of the Collaborative Research Center “Economic Risk” (CRC 649) and director of the interdisciplinary Center for Applied Statistics and Economics (C.A.S.E.) he primarily investigates economic risks on a global scale. Prof. Haerdle’s research aims at facilitating the evaluation of such risks and to reduce uncertainty to improve economic actors’ decision-making.
Prof. Haerdle is Distinguished Visiting Professor Wang Yanan Institute for Studies in Economics (WISE) at Xiamen University, China, as well as director of the International Research Training Group “High Dimensional Non Stationary Time Series” (ITRG 1792). Among other distinctions he received the “Econometric Theory Multa Scripsit Award” in 2012.

Scientific Services
  • multivariate statistical analysis (factor analysis, cluster Analysis, etc.)
  • portfolio optimisation
  • risk management
  • hedging
  • pricing derivatives
  • functional data analysis
  • non- and semi-parametric methods
  • data visualisation
  • Ongoing cooperation with and lecturing for leading international financial institutions
  • Center for Applied Statistics and Economics (C.A.S.E.): interdisciplinary research centre with the goal to analyze and solve current complex economic problems and those arising in related fields with the help of quantitative methods and computing. Its research subjects range from weather risk, aging societies, crime to property markets
  • Collaborative Research Center “Economic Risk” (CRC 649): center of transdisciplinary research where insights from economics, mathematics and statistics converge to analyze economic risks and risk factors. The CRC offers an international platform for discussion of the latest research results and collaborations
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