Consumer Goods & (Online) Retail

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 Professor Akbik
Faculty of Mathematics and Natural Sciences
Department of Computer Science

Chair of Machine Learning

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Expertise

Prof. Dr. Alan Akbik conducts research in the field of machine learning (ML) and natural language processing (NLP). His goal is to enable machines to capture, understand, and use natural language like a human.

To this end, he has developed one of the world's leading deep learning frameworks for NLP, which is already being used in over 1,000 research and industrial projects.

http://humboldt.gmbh/forschungskooperation

Scientific Services

Consulting in:

  • Deep Learning
  • Natural Language Processing (NLP)
  • Machine Learning (ML)
  • 3 Patents at IBM Research
  • Zalando Outstanding Achievement Award
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Profilbild Jan Mendling
Philipp Simonis
Faculty of Mathematics and Natural Sciences
Department of Computer Science

Process Management and Information Systems

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Expertise

Mendling's research focuses on the question how business processes can be efficiently and effectively supported by information systems. To this end, Mendling and his team address questions of the information technology as well as of managerial questions.

Within the framework of business process management they developed a series of technical solutions for process mining and also management tools like the BPM Billboard.

The expertise of Mendling and his team is summarized in the seminal textbook "Fundamentals of Business Process Management", which is used by over 250 universities in 70 countries.

http://humboldt.gmbh/forschungskooperation

Scientific Services
  • cooperation with businesses from different branches
  • design of training concepts for process management
  • performance of training courses and projects for process improvement
  • development of new analysis software for business processes

In the context of his projects, Mendling worked together with many medium-sized companies and DAX-companies from different branches. 

LL.M. Digitalization and Tax Law

 

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