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HGS MathComp - Where Methods Meet Applications

The Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) at Heidelberg University is one of the leading graduate schools in Germany focusing on the complex topic of Scientific Computing. Located in a vibrant research environment, the school offers a structured interdisciplinary education for PhD students. The program supports students in pursuing innovative PhD projects with a strong application-oriented focus, ranging from mathematics, computer science, bio/life-sciences, physics, and chemical engineering sciences to cultural heritage. A strong focus is put on the mathematical and computational foundations: the theoretical underpinnings and computational abstraction and conception.

HGS MathComp Principal Investigators are leading experts in their fields, working on projects that combine mathematical and computational methodology with topical research issues. Individual mentoring for PhD candidates and career development programs ensure that graduates are fully equipped to take up top positions in industry and academia.

Upcoming Events [see all...]

17.10.2024
16:15 - 17:15
Theory & Methods
Intrinsic Subspaces of High-Dimensional Inverse Problems and Where to Find Them
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Talk
Speaker: Dr. Tiangang Cui • University of Sydney • Romberg Visiting Scholar
Location: Mathematikon • Seminar Room A, Ground Floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: No registration required!
Organizer: Interdisciplinary Center for Scientific Computing (IWR)
ECTS: 1 for 5
This talk will be held as part of the IWR Scientific Computing Seminar.

The high dimensionality is a central challenge faced by many numerical methods for solving large-scale Bayesian inverse problems. In this talk, we will present some recent developments in the identification of low-dimensional subspaces that offer a viable path to alleviating this dimensionality barrier. Utilising concentration inequalities, we are able to identify the intrinsic subspaces from the solutions of certain eigenvalue problems and derive corresponding dimension-truncation error bounds. The resulting low-dimensional subspace enables the design of inference algorithms that can scale sub-linearly with the apparent dimensionality of the problem.
 
21.10.2024
14:30 - 16:15
Theory & Methods
HGS MathComp Membership Colloquium
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Colloquium
Speaker: New HGS MathComp PhD fellows
Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg
Organizer: HGS MathComp
ECTS: 0
Introduction of new HGS MathComp members and their PhD projects.

The BlueSheet Meeting will be held online for all new members of HGS MathComp on October 29, 2024 between 14:00 - 15:00.

14:30 Sebastian Stricker (Supervisor B. Savchynskyy)
“Unsupervised learning of multi-graph matching algorithms with applications in bioimaging”

14:50 Wilfredo Colmenares (Supervisor R. Strzodka)
“Parallel Krylov Subspace Methods“

15:10 Francisca Vieira (Supervisor B.Velten)
“Modelling perturbation responses along a time course”

15:30 Lukas Hatscher (Supervisor Denis Schapiro)
"Spatial Omics for improving stratification of lung cancer and multiple myeloma
Patients"

15:50 Krešimir Beštak (Supervisor D. Schapiro)
“Computational methods for spatial omic analysis of myocardial tissue in the context of cardiovascular diseases”
 
28.10.2024 - 30.10.2024
Theory & Methods
Indo-German Workshop on Hardware-aware Scientific Computing (IGHASC)
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Workshop
Location: Mathematikon • Im Neuenheimer Feld 205 • 69120 Heidelberg, Germany
Registration: Please apply on the workshop website
Organizer: Project Hardware-Aware Algorithms in Scientific Computing (HAASC)
Link:
ECTS: 0
The purpose of this workshop is to foster interactions between scientists from India and Germany on the most recent innovations, trends, and challenges in frontier areas of scientific computing, Including but not limited to:

- High-Performance Computing
- Resource-aware numerical methods
- Scalable methods for solving partial differential equations (PDEs)
- Optimal control of PDEs
- Large-scale Bayesian inference and data assimilation
- Scientific Machine Learning

Important dates:

- Abstract submission: July 15, 2024
- Acceptance notification: September 15, 2024
- Early registration deadline: August 1, 2024
- Final registration deadline: October 15, 2024

For more information and registration please visit the website of the workshop.