KIT Center MathSEE is one of the nine interdisciplinary research centers at Karlsruhe Institute of Technology. It pools knowledge between the sciences, engineering and economics and builds a bridge to the mathematical sciences achieving success through interdisciplinary research on mathematical methods for scientific and societal challenges. It also aims to bring experiences on a national, european and international scale together and be the town square for exchange of ideas, best practices and co-operations.

Through the symposium on applications of mathematics, the center fosters discussion and promotes new partnerships in science and with the industry. On-site costs are kindly covered by MathSEE so that the registration fee for the symposium itself is waived for all participants.

In September 2026, the third MathSEE Symposium will take place and in this spirit, we welcome you most warmly to join us at the symposium and become part of the Math-SEE community!

The symposium will feature 8 plenary lectures and several topically focussed parallel sessions on the applications of mathematical methods along the following tracks:

We are excited about your contribution and look forward to discuss, to share and to network to widen the mathematical community!

In September 2026, the third MathSEE Symposium will take place and in this spirit, we welcome you most warmly to join us at the symposium and become part of the Math-SEE community!

The symposium will feature 8 plenary lectures and several topically focussed parallel sessions on the applications of mathematical methods along the following tracks:

We are excited about your contribution and look forward to discuss, to share and to network to widen the mathematical community!

Workshop: October 12+13, 2026 - places limited, registration necessary

Poster session: October 12, 2026, 4.45 pm, KIT Campus South, Building 10.81, in front of the Theodor-Rehbock-Hörsaal (HS59). Posters will be presented by workshop participants. Attendance without presenting a poster is possible without registration.

Keynote lecture: October 13, 2026 at 4.30pm, KIT Campus South, NTI Lecture Hall - no registration

For more than two centuries, least-squares regression has been a cornerstone of statistical practice, while classical nonparametric smoothing methods have long served as standard tools for analysing complex data. In this workshop, we will revisit these methods from a modern perspective and ask: Are we making the best possible use of them? Recent work in statistical theory by Richard Samworth and others shows that these familiar methods can often be improved by incorporating additional structural information, such as shape constraints or properties of the underlying error distribution.

This two-day workshop, led by Richard Samworth, will explore recent developments in distributionally adaptive statistical methods. Richard Samworth is Professor of Statistical Science and Director of the Statistical Laboratory at the University of Cambridge. A Fellow of the Royal Society, he is the recipient of numerous distinctions, including the COPSS Presidents' Award, the David Cox Medal, and, in 2025, the Royal Statistical Society Guy Medal in Silver. His research has made fundamental contributions to nonparametric statistics, statistical learning theory, and high-dimensional methodology, particularly in shape-constrained estimation and adaptive nonparametric procedures. A defining feature of his research is the combination of rigorous theoretical guarantees with methods designed to be computationally efficient and practically applicable.

The first day will focus on linear regression and shape-constrained estimation. Starting from the classical least-squares framework, the workshop will examine how structural information, including monotonicity, can be used to improve estimation and inference.

The second day will turn to nonparametric regression. It will begin with local polynomial methods and their theoretical foundations before introducing recent extensions, including Outrigger local polynomial regression, which adapts to the underlying error distribution while retaining strong theoretical guarantees.

The theoretical lectures will be complemented by practical sessions in R and Python, allowing participants to apply the methods discussed during the workshop. A joint poster session, with a particular focus on early-career researchers, will provide an opportunity to present ongoing work, exchange ideas across disciplines, and receive feedback from other participants and senior researchers. The poster session on Monday and the plenary talk on Tuesday will also be open to researchers from the university and neighbouring institutions.

The workshop is primarily intended for doctoral candidates and postdoctoral researchers from the KCDS Graduate School and the Heidelberg Graduate School MathComp, as well as members of the Helmholtz Association and researchers in related fields who have a strong interest in modern mathematical statistics.

Participants will gain insight into current developments in adaptive statistical methodology and their connections to broader challenges in statistical learning and modern data analysis.

The workshop and keynote lecture are organized by the Institute of Statistics (STAT) in cooperation with MathSEE / KCDS and HGS MathComp at Heidelberg University. The workshop was made possible through Course Funding from HIDA, which supported its development and implementation.

Workshop: October 12+13, 2026 - places limited, registration necessary
Poster session: October 12, 2026, 4.45 pm, KIT Campus South, Building 10.81, in front of the Theodor-Rehbock-Hörsaal (HS59). Posters will be presented by workshop participants. Attendance without presenting a poster is possible without registration.
Keynote lecture: October 13, 2026 at 4.30pm, KIT Campus South, NTI Lecture Hall - no registration

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The first day will focus on linear regression and shape-constrained estimation. Starting from the classical least-squares framework, the workshop will examine how structural information, including monotonicity, can be used to improve estimation and inference.
The second day will turn to nonparametric regression. It will begin with local polynomial methods and their theoretical foundations before introducing recent extensions, including Outrigger local polynomial regression, which adapts to the underlying error distribution while retaining strong theoretical guarantees.

\n\n

The workshop is primarily intended for doctoral candidates and postdoctoral researchers from the KCDS Graduate School and the Heidelberg Graduate School MathComp, as well as members of the Helmholtz Association and researchers in related fields who have a strong interest in modern mathematical statistics.
\nParticipants will gain insight into current developments in adaptive statistical methodology and their connections to broader challenges in statistical learning and modern data analysis.

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The BAYST workshop aims to bridge recent advances in Bayesian structural learning with applied sciences, fostering scientific discussion and collaboration between theoretical and applied researchers.

BAYST will consist of 20 invited talks (with discussion) and 2 poster sessions (submissions are open!) over two and a half days. Registration is free for all attendees. Coffee breaks, a welcome reception, and lunches will be provided for presenters (of either a poster or a talk).

Moreover, BAYST will serve as the kick-off meeting for a potential new section in the International Society of Bayesian Analysis (ISBA) that will be officially proposed to the ISBA Executive Committee after the workshop. The section on Bayesian Structural Learning (BSL) aims to provide an inclusive home for researchers working with high-dimensional or structurally complex data, whether their focus is methodological or applied. Initial officers would be David Dunson, Nadja Klein, Rodney Sparapani, and Deborah Sulem. If you are interested in supporting this initiative, contact us at kleinlab@scc.kit.edu to sign our petition.