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...]
07.10.2024
- 11.10.2024
09:30 - 12:30
09:30 - 12:30
Theory & Methods
Romberg Course: Computational Linear Algebra for Machine Learning
[ more ...]
[ more ...]
Compact Courses
Speaker: Dr. Tiangang Cui • University of Sydney • Romberg Visiting Scholar
Location: Mathematikon • Seminar Room 10, 5th floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: This course is fully booked
Organizer: HGS MathComp
Location: Mathematikon • Seminar Room 10, 5th floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: This course is fully booked
Organizer: HGS MathComp
ECTS: 2
This course is part of the HGS MathComp Romberg Program.
Target Audience: This course will be on the introductory/intermediate level, targeting graduate students from various fields who are users of Maths/CompSci methods. Advanced students in mathematics who want to learn computational linear algebra can also attend, additional reading material will be provided for advanced students.
Assumed Knowledge: Familiar with basic concepts of linear algebra and can at least program in Python or MATLAB.
Mode of delivery: 15 hours of teaching (20 lecture hours). This includes three lecture hours and one tutorial per day.
Target Audience: This course will be on the introductory/intermediate level, targeting graduate students from various fields who are users of Maths/CompSci methods. Advanced students in mathematics who want to learn computational linear algebra can also attend, additional reading material will be provided for advanced students.
Assumed Knowledge: Familiar with basic concepts of linear algebra and can at least program in Python or MATLAB.
Mode of delivery: 15 hours of teaching (20 lecture hours). This includes three lecture hours and one tutorial per day.
Abstract: The overall aim of this course is to study matrix computations that lie at the core of a wide variety of applications in machine learning and data science. We will give an introduction to the mathematical theory of computational linear algebra (with derivations of the methods and some elementary proofs). This will broadly include methods for solving linear systems of equations, least-squares problems, eigenvalue problems, and other matrix decompositions. We will learn to implement the computational methods efficiently, as well as how to thoroughly test their implementations for accuracy and performance. More importantly, we will learn how to apply these methods to solve realistic machine learning application problems, including collaborative filtering (recommendation systems), image deblurring, kernel methods, and beyond.
Speaker: Nina Bisheh (Universitätsrechenzentrum), Dr. Georg Schwesinger (Universitätsbibliothek Heidelberg), Dr. Sebastian Zangerle (Universitätsbibliothek Heidelberg)
Location: On-site in Heidelberg
Registration: Please register on the event website
Organizer: Graduate Academy
Location: On-site in Heidelberg
Registration: Please register on the event website
Organizer: Graduate Academy
ECTS: 0.5
This course is part of the course program of the Graduate Academy.
The latest information and a registration link are available on the course website (log in with Uni-ID).
HGS MathComp fellows can get a reimbursement of the course fees. Please submit your proof of payment and certificate of participation to hgs@iwr.uni-heidelberg.de.
The latest information and a registration link are available on the course website (log in with Uni-ID).
HGS MathComp fellows can get a reimbursement of the course fees. Please submit your proof of payment and certificate of participation to hgs@iwr.uni-heidelberg.de.
Collecting, processing and analyzing data are central activities for virtually every researcher. Topics like data sharing and data publication are becoming increasingly important. Nevertheless, many research projects lack a structured and well-organized data management. This course is meant to give a general, discipline-independent introduction into various topics central to an efficient management of research data with a special focus on questions related to data archiving and data sharing. Both are central aspects of good scientific practice. Archiving and long-term preservation of research data are prerequisites for the scrutiny of scientific results based on the analysis of this data. Data sharing on the other hand increases transparency of research results and enables possible re-usage of data for new research questions, in combination with additional data sets and in interdisciplinary contexts.
Speaker: New HGS MathComp PhD fellows
Location: Mathematikon • Conference Room, Room 5/104, 5th Floor • Im Neuenheimer Feld 205 • 69120 Heidelberg
Organizer: HGS MathComp
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.
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”
“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”