Christoph Jansen

BACKGROUND:

Hi there! I'm an assistant professor in data science at the School of Computing & Communications (SCC) of Lancaster University Leipzig.

Before, I was a PostDoc at the Department of Statistics of LMU Munich, where I finished my habilitation in January 2025 with a thesis on ML under weakly structured information. During summer semster 2022 I was appointed as an interim W2-professor for Statistics . In 2018, I earned a Ph.D. in Statistics with a thesis on decision making in complex information settings at the same Department (supervisor: Thomas Augustin).

Further, I am currently serving as a member of the executive committee of the Society for Imprecise Probabilities: Theories and Applications (SIPTA). On this site, I collect information about my research and teaching activities. Here, you can find a (hopefully) up-to-date list of my publications, preprints and references to current research projects. If you are interested in any of the topics and would like to discuss them, just write me a message. I am always happy to chat.

RECENT NEWS:
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We have a new preprint making A Statistical Case Against Empirical Human-AI Alignment. See here.
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We have two papers accepted at NeurIPS 2024, one even as a spotlight! The papers are on Statistical Multicriteria Benchmarking (see here) and on Reciprocal Learning (see here).
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RESEARCH INTERESTS:

tl;dr: In a nutshell, I utilize methods from decision and game theory as well as the theory of imprecise probabilities to make machine learning (ML) methods more reliable and robust to their implicit assumptions. My work touches on various areas of ML such as uncertainty quantification, trustworthy ML, robust statistical inference and analysis of non-standard data and is published in major ML venues as JMLR, NeurIPS and UAI.

More specifically:

Decision Theoretic Foundations of Machine Learning: On a general level, I am very interested in pretty much everything (even loosely) related to decision making under uncertainty, ranging from axiomatic theories to applications in substance matter contexts. More specifically, I mainly focus on decision-making under weakly structured information, where the weak structure can originate from partially ordered preferences or imprecise probabilistic information or both. Much of my work focuses on developing models formalizing such situations as well as algorithms for efficient decision support. Here, the main focus is always on what can be derived from such generalized modeling for statistical decision theory and machine learning. This is not least due to the fact that decision theory can be understood as a formal superstructure of most common statistical methods.

Optimization Theory: When applying decision theory to real-world problems, e.g. in the context of decision support algorithms and expert systems, optimization theory often plays a crucial role. I have a huge interest in developing new (linear) optimization-based algorithms for determining optimal decisions. Beyond that, I am also very interested in optimization theory and approximation theory from a purely theoretical point of view.

Stochastic Orderings: Again mainly originating from a decision-theoretic point of view, I am also very fascinated by stochastic orderings. When generalizing optimality criteria from classical decision theory to more complexly structured information settings, there often arise very nice and natural generalizations of classical stochastic orderings like e.g., (first-order) stochastic dominance or statistical preference. These generalizations then can be re-adapted to e.g. statistical contexts and applications, allowing for completely new perspectives also here.

Imprecise Probabilities: A significant part of my work is concerned with theories for describing and characterizing uncertainty beyond perfect stochasticity. Such theories, commonly summarized under the umbrella term imprecise probability theories, allow for very general models of uncertainty and, accordingly, for flexible and information-efficient descriptions of reality. Here, I am interested in both theoretical aspects (for instance non-additive measures, general integration theory) as well as the huge variety of applications in both Statistics and Decision Theory (for instance Robust Statistics or Choice Models accounting for Ambiguity).

Please feel free to check my papers and other stuff!

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