ZüKoSt: Seminar on Applied Statistics

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Spring Semester 2022

Date / Time

Speaker

Title

Location

4 March 2022
15:15-16:15

Magali Champion
ETH Zürich

Event Details

ZüKoSt Zürcher Kolloquium über Statistik

Title	l_1-spectral clustering algorithm: a spectral clustering method using l_1-regularization
Speaker, Affiliation	Magali Champion, ETH Zürich
Date, Time	4 March 2022, 15:15-16:15
Location	HG G 19.1
Abstract	Detecting cluster structure is a fundamental task to understand and visualize functional characteristics of a graph. Among the different clustering methods available, spectral clustering is one of the most widely used due to its speed and simplicity, while still being sensitive to high perturbations imposed on the graph. In this work, we present a variant of the spectral clustering, called l_1-spectral clustering, based on Lasso regularization and adapted to perturbed graph models. By promoting sparse eigenbases solutions of specific l_1-minimization problems, it detects the hidden natural cluster structure of the graph. The effectiveness and robustness to noise perturbations is confirmed through a collection of simulated and real biological data. Joint work with C. Champion, M. Blazère, R. Burcelin and JM. Loubes.

l_1-spectral clustering algorithm: a spectral clustering method using l_1-regularizationread_more

HG G 19.1

6 May 2022
15:15-16:15

Björn Menze
Universität Zürich

Event Details

ZüKoSt Zürcher Kolloquium über Statistik

Title	On tumors and vessels in medical image data
Speaker, Affiliation	Björn Menze, Universität Zürich
Date, Time	6 May 2022, 15:15-16:15
Location	HG G 19.1
Abstract	Biomedical image data offers quantitative information about health, disease, and disease progression under treatment - both at the patient and at the population level. Computational routines are instrumental in extracting this information in a structured fashion, typically following a succession of image segmentation, 'radiomic' feature extraction, and predictive modeling with respect to a given image marker or disease-related outcome. This pipeline can also be complemented by a functional and patient-specific modeling of the features or processes underlying the given image observations, for example, the tumor-growth underlying a set of magnetic resonance scans acquired prior to and after treatment. I will talk about this biomedical image data processing pipeline, focusing on two aspects of our work in Zurich: the analysis of tumor images using patient-adapted tumor growth models, and the extraction of whole brain vascular networks from 3D image data. I will demonstrate how to extract PDE model parameters from image observables using CNNs and show how we extract sparse physical networks from noisy image volumes using different learning strategies. I will also comment on data that made publicly available for both applications.

On tumors and vessels in medical image dataread_more

HG G 19.1

13 May 2022
15:15-16:15

Matthias Templ
ZHAW School of Engineering, Zürich

Event Details

ZüKoSt Zürcher Kolloquium über Statistik

Title	An Ontology on Data Anonymization and Privacy Computing Approaches
Speaker, Affiliation	Matthias Templ, ZHAW School of Engineering, Zürich
Date, Time	13 May 2022, 15:15-16:15
Location	HG G 19.1
Abstract	This talk is a practical presentation that aims to give an overview and ontology of different concepts on how to handle confidential data. It is motivated by the "fact" that different communities have different views and opinions on anonymization likely without knowing and understanding each other. To put it bluntly, a computer scientist will likely propose a very different solution to an anonymization problem than a survey statistician, and some scientists (and companies) believe that synthetic data is the sanctuary and solution par excellence, others simply promote privacy-preserving data processing, while national statistical offices generally tend to reject these concepts, etc. Given the various methodological developments in the field of sensitive data protection, a conceptual classification and comparison between different methods from different domains is missing. Specifically, the goal is thus to provide guidance to practitioners who do not have an overview of appropriate approaches for specific scenarios, whether it is differential privacy for interactive queries, $k$ anonymity methods and synthetic data generation for publishing data, or secure federated analytics for multi-party computations without sharing the data itself. After the brief introduction of the most important anonymization concepts, an overview and ontology is provided on methods based on key criteria that describe a context for handling data in a privacy-compliant manner that enables informed decisions in the face of many alternatives. Throughout this presentation, it is emphasized that there is no panacea and that – as always - context matters.

An Ontology on Data Anonymization and Privacy Computing Approachesread_more

HG G 19.1

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