The program of this year's summer school will focus on Graph Neural Networks, covering both fundamental concepts and practical applications.
Participants can expect a mix of lectures and interactive sessions that provide insight into the mathematical foundations as well as real-world use cases of graph-based machine learning methods.
KCDS members as well as doctoral researchers from KIT and other universities/ research centers are welcome to join! There is no participation fee. Please note that KCDS doesn't cover travel and accomodation expenses.
The program of this year's summer school will focus on Graph Neural Networks, covering both fundamental concepts and practical applications.
\n\nParticipants can expect a mix of lectures and interactive sessions that provide insight into the mathematical foundations as well as real-world use cases of graph-based machine learning methods.
\n\n\n\n
KCDS members as well as doctoral researchers from KIT and other universities/ research centers are welcome to join! There is no participation fee. Please note that KCDS doesn't cover travel and accomodation expenses.
\n
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 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.
\n\n
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.
\n\n\n\n
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.
\n\n\n\n
\n\n
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.
\n\n\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.
\n\n
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.
\nThe workshop for registered participants is complemented by a keynote by Richard Samworth to which everyone interested is invited, no registration necessary!
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.
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 the Royal Statistical Society Guy Medal in Silver in 2025. 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.
Richard Samworth will present recent work with Elliot H. Young and Rajen D. Shah on "Outrigger local polynomial regression“. A preprint of the paper is available on arXiv https://arxiv.org/abs/2603.11282 .
The workshop for registered participants is complemented by a keynote by Richard Samworth to which everyone interested is invited, no registration necessary!
\n\n\n\n
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.
\n\n\n\n
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 the Royal Statistical Society Guy Medal in Silver in 2025. 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.
\n\n\n\n
Richard Samworth will present recent work with Elliot H. Young and Rajen D. Shah on "Outrigger local polynomial regression“. A preprint of the paper is available on arXiv https://arxiv.org/abs/2603.11282 .
\n DTSTART;TZID=Europe/Berlin:20261013T163000 DTEND;TZID=Europe/Berlin:20261013T180000 LOCATION:KIT Campus South, NTI Lecture Hall (building 30.10) END:VEVENT END:VCALENDAR