Events
Research Data in Discrete Mathematics

MPI für Mathematik in den Naturwissenschaften Leipzig

E1 05 (Leibniz-Saal)

conference

13.03.23 14.03.23

Research Data in Discrete Mathematics

Discrete mathematics has a long-standing tradition of generating and utilizing data to substantiate theories. Conversely, questions regarding the systematic generation of (counter-)examples drives research.

This 2-day “noon-to-noon” workshop is dedicated to the interplay of discrete data and discrete structures from a contemporary perspective. How FAIR is our data? How can modern technology help to analyze and interpret discrete data? Which connections to the sciences become visible through data? We approach these questions “hands-on” with invited lectures and panel discussions.

Program

12:00 -	Informal Lunch in Town
13:00 - 13:30	Registration
13:30 - 14:30	Peter Paule (Research Institute for Symbolic Computation (RISC). Johannes Kepler University Linz) The unreasonable effectiveness of computer algebra in the mathematical sciences Despite the current renaissance of AI, the main theme of the talk is on more traditional lines: namely, to stress the huge potential of algorithmic mathematics, and of respective computer algebra software, for applications in pure mathematics and related fields. For example, the Ramanujan Machine (Nature 590, 2021) creates mathematical conjectures using AI and computer automation. On the other hand, Cristian-Silviu Radu (RISC) developed a computer algebra algorithm which can be used to discover (and prove!) identities, which even Ramanujan would have appreciated to see. In the talk we discuss a variety of such examples from different areas: optimization of antenna radiation, special functions and Gauss' contiguous relations, linear Diophantine inequalitites and partitions of numbers, symbolic summation in quantum field theory, a.s.o.
14:30 - 15:00	Coffee & Tea
15:00 - 16:00	Yang-Hui He (London Institute for Mathematical Sciences & Merton College, Oxford University) Universes as Bigdata: Physics, Geometry and Machine-Learning The search for the Theory of Everything has led to superstring theory, which then led physics, first to algebraic/differential geometry/topology, and then to computational geometry, and now to data science. With a concrete playground of the geometric landscape, accumulated by the collaboration of physicists, mathematicians and computer scientists over the last 4 decades, we show how the latest techniques in machine-learning can help explore problems of interest to theoretical physics and to pure mathematics. At the core of our programme is the question: how can AI help us with mathematics?
16:15 - 17:30	Panel Discussion: led by Michael Joswig (Technische Universität Berlin)
18:30 -	Dinner in Town

09:00 - 10:00	Kathlén Kohn (KTH Royal Institute of Technology) Catalogs of minimal problems from mathematicians for computer vision engineers — How to make discrete data FAIR across disciplines? This talk will provide more questions than answers. We will discuss FAIR discrete data and how we should / can deal with such data in our everyday research. How should we store discrete data if there is no agreed-upon standard format? Where should the data be stored? Who should maintain the database? How can the data be found and used by researchers outside our area of expertise? I will discuss these questions following a self-critical example: Together with fellow mathematicians, we compiled two lists of point-line arrangements (with 140616 resp 74575 entries) that encode efficiently-solvable 3D-reconstruction problems. Those so-called ‘minimal problems’ are a key ingredient for computer vision engineers who develop 3D-reconstruction algorithms. It would be our dream if our lists of minimal problems would be used in practice by engineers, and for that an early reflection about the FAIRness of our data would have helped. The computer-vision part of this talk is based on joint work with Timothy Duff, Anton Leykin, and Tomas Pajdla.
10:00 - 10:30	Coffee & Tea
10:30 - 11:00	Antony Della Vecchia (Technische Universität Berlin) A FAIR File Format The mathematical research data initiative MaRDI aims at making mathematical data FAIR and spreading awareness of the importance of reproducibility. Due to the complexity of mathematical objects, storing data so it is FAIR has its challenges. We introduce a file format for data used in computer algebra computations, highlighting some design decisions and demonstrating some of the benefits with an emphasis on interoperability.
11:00 - 12:00	Hands-on Session on Reproducibility with Tobias Boege (Aalto University)
12:00 - 12:30	Lunch
12:30 - 13:30	Ad-hoc Presentations of Young Researchers: by Alheydis Geiger (MPI MiS), Alvaro Gutiérrez Cáceres (University of Bonn), Andreas Kretschmer (OvGU Magdeburg), Janike Oldekop (TU Berlin), Janin Heuer (TU Braunschweig) and Linus Sommer (Universität Leipzig)
13:30 - 14:00	Open Discussions in Workgroups

Participants

Carlos Améndola

TU Berlin

Tabea Bacher

MPI MiS

Mara Belotti

TU Berlin

Tobias Boege

Aalto University

Marie Brandenburg

MPI MiS

Laura Casabella

MPI MiS Leipzig

Xiangying Chen

Otto von Guericke Universität Magdeburg

haase christian

fu berlin

Ji Hoon Chun

Technische Universität Berlin

Jane Coons

St John's College, University of Oxford

Antony Della Vecchia

Technische Universität Berlin

Alvaro Diaz-Ruelas

MPI Mathematics in the Sciences

Nicholas Early

Max Planck Institute for Mathematics in the Sciences

Bettina Eick

TU Braunschweig

Claudia Fevola

Max Planck Institute for Mathematics in the Sciences

Alheydis Geiger

MPI MiS

Christiane Görgen

Universität Leipzig

Álvaro Gutiérrez Cáceres

University of Bonn

Yang-Hui He

London Institute for Mathematical Sciences & Merton College, Oxford University

Janin Heuer

TU Braunschweig

Benjamin Hollering

MPI MiS

Héctor Jardón Sánchez

Leipzig Universität

Michael Joswig

Technische Universität Berlin

Martina Juhnke

Universität Osnabrück

Thomas Kahle

Otto-von-Guericke Universität Magdeburg

Lars Kastner

Technische Universität Berlin

Leo Kayser

Max Planck Institute for Mathematics in the Sciences

Kathlén Kohn

KTH Royal Institute of Technology

Tabea Krause

University Leipzig

Andreas Kretschmer

OvGU Magdeburg

Duc Luu

MPI MIS

Eliana Maria

MPI MIS

Stefano Mereta

MPI MiS Leipzig

Henrik Jessen Munch

University of Padova

Benjamin Nill

OVGU Magdeburg

Janike Oldekop

TU Berlin

Andreas Paffenholz

TU Darmstadt

Marta Panizzut

MPI MiS Leipzig

Peter Paule

Research Institute for Symbolic Computation (RISC). Johannes Kepler University Linz

Ángel David Ríos Ortiz

MPI Leipzig

Kemal Rose

MPI mis

Günter Rote

Freie Universität Berlin

Raman Sanyal

Goethe-Universität Frankfurt / Freie Universität Berlin

Lisa Seccia

Max Planck Institute for Mathematics in the Sciences (MPI MiS)

Javier Sendra-Arranz

Max Planck Institute for Mathematics in the Sciences

Elima Shehu

Osnabrück University & The Max Planck Institute for Mathematics in the Sciences

Linus Sommer

Universität Leipzig

Mandy Stritzke

Technische Universität Braunschweig

Christian Stump

Ruhr-Universität Bochum

Bernd Sturmfels

Max Planck Institute for Mathematics in the Sciences

Bertrand Teguia

MPI MiS, Leipzig

Gennesaret Kharistio Tjusila

Technische Universität Berlin

Martin Wahl

Universität Bielefeld

Lena Weis

Technische Universität Berlin

Maximilian Wiesmann

MPI MiS

Claudia Yun

MPI MiS

Scientific Organizers

Christiane Görgen

Max Planck Institute for Mathematics in the Sciences and Universität Leipzig

Martina Juhnke-Kubitzke

Universität Osnabrück

Thomas Kahle

Otto-von-Guericke-Universität

Lars Kastner

Technische Universität Berlin

Raman Sanyal

Goethe Universität Frankfurt and Freie Universität Berlin

Christian Stump

Ruhr-Universität Bochum

Administrative Contact

Tabea Bacher

Max-Planck-Institut für Mathematik in den Naturwissenschaften Contact via Mail