Departement Mathematik

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Herbstsemester 2024

Datum / Zeit Referent:in Titel Ort
4. Oktober 2024
15:15-16:15 		Samuel Pawel
Center for Reproducible Science, UZH
Details

ZueKoSt: Seminar on Applied Statistics

Titel Meta-scientific perspectives on simulation studies
Referent:in, Affiliation Samuel Pawel , Center for Reproducible Science, UZH
Datum, Zeit 4. Oktober 2024, 15:15-16:15
Ort HG G 19.1
Abstract Simulation studies are widely used in methodological research fields such as statistics, psychometrics, bioinformatics, ecology, econometrics, or machine learning. They generate artificial data sets under specified mechanisms and compare the performance of data analysis methods under different conditions. Careful design, analysis, and reporting of simulation studies is important because they often provide the basis for data analysis decisions in scientific and medical practice. Problems with the reporting of simulation studies were first described nearly half a century ago. Recent attention to reproducibility issues in the biomedical and social sciences has led to more critical reflection on simulation studies and new proposals for improving them. In this talk, I will provide an overview of these recent meta-scientific developments. I will also discuss how questionable research practices (QRPs), such as cherry-picking of favorable results, can affect the validity of simulation studies. To illustrate this point, I present a simulation study of a novel prediction method with no expected performance gain, and show how easy it is to make the method appear superior to well-established competing methods when QRPs are employed. I also discuss approaches for addressing QRPs, and present a newly developed template for preregistration of simulation studies. References: - Pawel, S., Kook, L., Reeve, K. (2024). Pitfalls and potentials in simulation studies: Questionable research practices in comparative simulation studies allow for spurious claims of superiority of any method. Biometrical Journal. https://doi.org/10.1002/bimj.202200091 - Siepe, B.S., Bartos, F., Morris, T.P., Boulesteix, A.-L., Heck, D.W., Pawel, S. (2024). Simulation Studies for Methodological Research in Psychology: A Standardized Template for Planning, Preregistration, and Reporting. Psychological Methods (to appear). https://doi.org/10.31234/osf.io/ufgy6
Meta-scientific perspectives on simulation studiesread_more
HG G 19.1
7. November 2024
15:15-16:15 		Zhijing Jin
Incoming Assistant Professor at the University of Toronto; PhD at Max Planck Institute & ETH
Details

ZueKoSt: Seminar on Applied Statistics

Titel The Potential of Automating Causal Inference with Large Language Models
Referent:in, Affiliation Zhijing Jin, Incoming Assistant Professor at the University of Toronto; PhD at Max Planck Institute & ETH
Datum, Zeit 7. November 2024, 15:15-16:15
Ort HG G 19.1
Abstract Causal reasoning is a cornerstone of human intelligence and a critical capability for artificial systems aiming to achieve advanced understanding and decision-making. While large language models (LLMs) excel on many tasks, a key question still remains: How can these models reason better about causality? Causal questions that humans can pose span a wide range of fields, from Newton’s fundamental question, “Why do apples fall?” which LLMs can now retrieve from standard textbook knowledge, to complex inquiries such as, “What are the causal effects of minimum wage introduction?”—a topic recognized with the 2021 Nobel Prize in Economics. My research focuses on automating causal reasoning across all types of questions. To achieve this, I explore the causal reasoning capabilities that have emerged in state-of-the-art LLMs, and enhance their ability to perform causal inference by guiding them through structured, formal steps. Finally, I will outline a future research agenda for building the next generation of LLMs capable of scientific-level causal reasoning. https://zhijing-jin.com/fantasy/about/
The Potential of Automating Causal Inference with Large Language Modelsread_more
HG G 19.1
15. November 2024
15:15-16:15 		Jan Dirk Wegner
Department of Mathematical Modeling and Machine Learning (DM3L), University of Zurich
Details

ZueKoSt: Seminar on Applied Statistics

Titel Monitoring Earth with Remote Sensing and Deep Learning
Referent:in, Affiliation Jan Dirk Wegner, Department of Mathematical Modeling and Machine Learning (DM3L), University of Zurich
Datum, Zeit 15. November 2024, 15:15-16:15
Ort HG E 41
Abstract Modern deep learning in combination with satellite data offers great opportunities to protect nature at global scale. I will present ongoing research to map crops at country-scale, for species distribution modeling, to estimate vegetation parameters such as biomass and vegetation height, and how conflicts can be monitored remotely. Traditional approaches usually must be adapted for specific ecosystems and regions. It is therefore very difficult to carry out homogeneous, large-scale modeling with high spatial and temporal resolution and, at the same time, good accuracy. Data-driven approaches, especially modern deep learning methods, promise great potential here to achieve globally consistent, transparent assessments of our environment. Bio: Jan Dirk Wegner leads the EcoVision Lab at the DM3L at University of Zurich as an Associate Professor. Jan was PostDoc (2012-2016) and senior scientist (2017-2020) in the Photogrammetry and Remote Sensing group at ETH Zurich after completing his PhD (with distinction) at Leibniz Universität Hannover in 2011. His main research interests are at the frontier of machine learning, computer vision, and remote sensing to solve scientific questions in the environmental sciences and geosciences. Jan was granted multiple awards, among others an ETH Postdoctoral fellowship and the science award of the German Geodetic Commission. He was selected for the WEF Young Scientist Class 2020 as one of the 25 best researchers world-wide under the age of 40 committed to integrating scientific knowledge into society for the public good. Jan is vice-president of ISPRS Technical Commission II, associated faculty of the ETH AI Center, director of the PhD graduate school "Data Science" at University of Zurich, and his professorship is part of the Digital Society Initiative at University of Zurich. Together with colleagues, Jan is chairing the CVPR EarthVision workshops.
Monitoring Earth with Remote Sensing and Deep Learningread_more
HG E 41
29. November 2024
15:15-16:00 		David Wissel
Boeva Lab, ETHZ
Details

ZueKoSt: Seminar on Applied Statistics

Titel Empirical evaluations and methods for (multi-)omics survival analysis
Referent:in, Affiliation David Wissel, Boeva Lab, ETHZ
Datum, Zeit 29. November 2024, 15:15-16:00
Ort HG G 19.1
Abstract Survival analysis has been a task of significant interest within the statistics community throughout the years. More recently, the machine learning and bioinformatics communities have also increasingly become interested in survival analysis. In this seminar, we survey recent developments focusing especially on high-dimensional multi-omics survival analysis. We concentrate particularly on empirical evaluations highlighting the difficulty of this task, the need for more data, and standardized evaluation. In the second part of the seminar, we discuss recent work on knowledge distillation for sparse survival models and methods for structure selection in sparse partially linear survival models.
Empirical evaluations and methods for (multi-)omics survival analysisread_more
HG G 19.1
6. Dezember 2024
15:15-16:15 		Siddhartha Mishra
ETHZ
Details

ZueKoSt: Seminar on Applied Statistics

Titel Learning PDEs
Referent:in, Affiliation Siddhartha Mishra, ETHZ
Datum, Zeit 6. Dezember 2024, 15:15-16:15
Ort HG G 19.1
Abstract PDEs are considered to be language of physics as they provide mathematical descriptions of a whole range of physical phenomena. The complexity and prohibitive computational cost of traditional physics-based numerical schemes necessitates the search for fast and efficient surrogates, based on machine learning. In this lecture, we survey recent developments in the field of learning solution operators for PDEs by focussing on structure preserving neural operators and on foundation models for sample efficient and generalizable multi-operator learning. We also briefly discuss graph neural network based learning of PDEs on arbitrary domain geometries and conditional Diffusion models for learning multi-scale physical systems such as Turbulent Fluid Flows. 
Learning PDEsread_more
HG G 19.1

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