8th Workshop on Geometry and Machine Learning
Thursday, June 13, 2024 | Athens, Greece
Organized by Alexander Munteanu and Jeff M. Phillips.
Machine learning (broadly defined) concerns techniques that can learn from and make predictions on data. Such algorithms are built to explore the useful pattern of the input data, which usually can be stated in terms of geometry (e.g., problems in high dimensional feature space). Hence computational geometry plays a crucial and natural role in machine learning. Importantly, geometric algorithms often come with quality guaranteed solutions when dealing with high-dimensional data. The computational geometry community has many researchers with the unique knowledge on high dimensional geometry, which could be utilized to have a great impact on machine learning or any data related fields.

This workshop is intended to provide a forum for those working in the fields of computational geometry, machine learning and the various theoretical and algorithmic challenges to promote their interaction and blending. To this end, the workshop will consist of an invited talk and several contributed talks. The invited talk will mainly serve as a tutorial about the applications of geometric algorithms in machine learning. Such interaction will stimulate those working in both fields, and we can expect that a synergy can promote many new interesting geometric problems, concepts and ideas, which will contribute to open up new vistas in computational geometry communities.
This workshop is being held as part of CG Week 2024 (June 10-14, 2024 in Athens, Greece) which also includes the International Symposium on Computational Geometry (SoCG).



Tentative schedule:
Contributed Talks
16:00-16:20 Hubert Wagner (University of Florida) Quantifying Geometric-Topological Interactions using Mixup Barcodes
with Nickolas Arustamyan, Matthew Wheeler, and Peter Bubenik
16:20-16:40 Jan Hula (CIIRC and University of Ostrava) Do Language Models See a Space?
with Jiri Janecek, David Mojzisek, and Mikolas Janota
16:45-17:05 Bala Krishnamoorthy (Washington State University) Box Filtration
with Enrique Alvarado and Prashant Gupta
17:05-17:25 Iolo Jones (Durham University) Diffusion Geometry for Machine Learning

Coffee Break
Invited Talk
17:45 - 18:35 Dimitrios Gunopulos (University of Athens) Learning from Data with Geometric Properties
Bio: Dimitrios Gunopulos got his PhD from Princeton University in 1995. He is currently a Professor and the Department Chair in the Department of Informatics and Telecommunications, University of Athens. He was a Postdoctoral Fellow at the Max-Planck-Institut for Informatics, a Researcher at the IBM Almaden Research Center, a Visiting Researcher at the University of Helsinki, a Professor at the Department of Computer Science and Engineering in the University of California Riverside, and a Visiting Researcher at Microsoft Research, Silicon Valley Lab. His research is in the areas of Fairness and Explainability in AI, Data Mining of Spatiotemporal Data, Database Indexing, Machine Learning, Peer-to-Peer systems, and Geometric Algorithms. He has co-authored over a hundred journal and conference papers, seven patents, and a book. He is the recipient of the 2020 IEEE ICDM Outstanding Service Award. His Erdos number is 2. His research has been supported by NSF (including an NSF CAREER award), the DoD, the Institute of Museum and Library Services, the European Commission, the Greek General Secretatiat of Research and Technology, AT&T, Nokia, a 2015 Yahoo Faculty Award and a 2017 Google Faculty Award. He has served as General co-Chair in SDM SIAM 2018, SDM SIAM 2017, HDMS 2011, and IEEE ICDM 2010, and as PC co-Chair in ACM SIGKDD 2023, IEEE ICDE 2020, ECML/PKDD 2011, IEEE ICDM 2008, ACM SIGKDD 2006, SSDBM 2003, and DMKD 2000. In several data mining or machine learning learning problems and applications we aim to learn from data that have geometric properties. A typical, widely encountered such example is analyzing and learning from user mobility data. Mobility data are being collected today at unprecedented rates and used in diverse learning problems, from route planning to traffic management. Focusing on the real-time analysis of GPS trajectory data we present recent advances and highlight research challenges in the problems of map building, travel time estimation, and multi-objective routing.
Contributed Talks
18:35-18:55 Lori Ziegelmeier (Macalester College) The Survival Analysis of Topological Features of Knowledge Networks
with Russell J. Funk, Jingyi Guan, Jason Owen-Smith, and Adam Schroeder
18:55-19:15 Alexander Munteanu (TU Dortmund) Improved learning via k-DTW: a novel dissimilarity measure for curves
with Amer Krivosija, Andre Nusser, and Chris Schwiegelshohn


Contributed Talks: Potential participants submitted potential contributed talks to be considered for a presentation, via an email to WoGeomML@gmail.com with an abstract (e.g., 2 pages) or preferably link to permanent, publically available version (e.g., at arXiv.org). The email should contain a title, list of authors, and should identify the name of the person presenting.
We received submissions for contributed talks until April 26, 2024.
Generous support provided by NSF CCF-2115677, PROFILNRW-2020-068, and DFG MU 4662/2-1.
The previous versions of this workshop were:
  • Workshop on Geometry and Machine Learning
  • 2nd Workshop on Geometry and Machine Learning
  • 3rd Workshop on Geometry and Machine Learning
  • 4th Workshop on Geometry and Machine Learning
  • 5th Workshop on Geometry and Machine Learning
  • 6th Workshop on Geometry and Machine Learning
  • 7th Workshop on Geometry and Machine Learning