# Seminar on Statistics and Data Science

This seminar series is organized by the research group in statistics and features talks on advances in methods of data analysis, statistical theory, and their applications. The speakers are external guests as well as researchers from other groups at TUM. All talks in the seminar series are listed in the Munich Mathematical Calendar.

The seminar takes place in room **8101.02.110**, if not announced otherwise.** **To stay up-to-date about upcoming presentations please join our mailing list. You will receive an email to confirm your subscription.

## Upcoming talks

## Previous talks

### within the last 180 days

## 25.09.2024 09:00 Niels Richard Hansen, Negar Kiyavash, Martin Huber, Niklas Pfister, Leonard Henckel, Jakob Runge, Francesco Locatello, Isabel Valera, Sara Magliacane, Qingyuan Zhao, Jalal Etesami: Miniworkshop on Causal Inference 2024

## 06.08.2024 10:15 Sven Wang (Humboldt University Berlin): Statistical algorithms for low-frequency diffusion data: A PDE approach.

## 02.07.2024 14:00 Thomas Richardson (University of Washington, Seattle): Short Course on “Graphical causal modeling” (Lecture 3/3)

## 27.06.2024 14:00 Thomas Richardson (University of Washington, Seattle): Short Course on “Graphical causal modeling” (Lecture 2/3)

## 25.06.2024 14:00 Thomas Richardson (University of Washington, Seattle): Short Course on “Graphical causal modeling” (Lecture 1/3)

## 17.06.2024 09:00 Saber Salehkaleybar (Leiden University): Causal Inference in Linear Structural Causal Models.

## 10.06.2024 10:30 Adèle Ribeiro (Philipps-Universität Marburg): Recent Advances in Causal Inference under Limited Domain Knowledge.

## 05.06.2024 12:15 Han Li (The University of Melbourne): Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?

## 15.05.2024 17:00 Richard Samworth (University of Cambridge): Optimal convex M-estimation via score matching.

## 13.05.2024 15:15 Chandler Squires (MIT, Cambridge): Decision-centric causal structure learning: An algorithm of data-driven covariate adjustment.

For talks more than 180 days ago please have a look at the Munich Mathematical Calendar (filter: "Oberseminar Statistics and Data Science").