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Personal profile

Quote

“Conjecturally, in the depths of the human brain runs an immensely powerful, simple, efficient and task- and signal-independent learning algorithm. It is my ultimate aim to use mathematics to uncover and develop such algorithms.”

Research profile

Jim Portegies is an Assistant Professor in the Applied Analysis group of the Centre for Analysis, Scientific computing and Applications (CASA) at Eindhoven University of Technology (TU/e). Jim Portegies works in the mathematical fields of analysis, measure theory and geometry, and applies techniques from these fields to problems in machine-learning and artificial intelligence. He has applied techniques from spectral geometry to prove guarantees on performance of nonlinear dimensionality reduction algorithms. Currently, he is investigating how to design algorithms that mimic how humans and animals learn.  

Despite the large number of recent advances in artificial intelligence, humans still outperform machines in many tasks. The central question of how to design machines that learn like humans is still wide open. The answer may lie in universal learning algorithms. Such algorithms are simple, efficient and can be applied to a broad variety of signals and tasks and are conjectured to exist in the depths of the human brain.

Academic background

Jim Portegies obtained his MSc in Industrial and Applied Mathematics and Applied Physics from the TU/e in 2009. He spent the 2007-2008 academic year as an exchange student at the University of Bonn, Germany. He received his PhD in Mathematics from the Courant Institute of Mathematical Sciences in New York. In the Fall of 2013, he spent a semester at NYU Shanghai, in Shanghai, China. After completing his PhD in 2014, he spent two years a postdoc at the Max Planck Institute for Mathematics in the Sciences in Leipzig, Germany until he returned to the TU/e as an assistant professor in Mathematics in 2016. Jim is a member of the TU/e Young Academy of Engineering. 

Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

Gromov-Hausdorff Convergence Mathematics
trimers Physics & Astronomy
harmonics Physics & Astronomy
Nonlinear Eigenvalue Mathematics
Eigenvalue Mathematics
Ricci Curvature Mathematics
Riemannian Manifold Mathematics
Semicontinuity Mathematics

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Research Output 2008 2018

  • 28 Citations
  • 9 Article
  • 2 Report
  • 1 Conference contribution
  • 1 Special issue

Continuity of nonlinear eigenvalues in CD (K, ∞) spaces with respect to measured Gromov–Hausdorff convergence

Ambrosio, L., Honda, S. & Portegies, J. W., 1 Apr 2018, In : Calculus of Variations and Partial Differential Equations. 57, 2, 34

Research output: Contribution to journalArticleAcademicpeer-review

Gromov-Hausdorff Convergence
Nonlinear Eigenvalue
K-space
Eigenvalue
Metric Measure Space

Ergo learning

Portegies, J. W., Sep 2018, In : Nieuw Archief voor Wiskunde. 5th Series, Volume 19, 3, p. 199-205

Research output: Contribution to journalSpecial issueAcademic

SciSports: Learning football kinematics through two-dimensional tracking data

Babic, A., Bansal, H., Finocchio, G., Golak, J., Peletier, M., Portegies, J., Stegehuis, C., Tyagi, A., Vincze, R. & Yoo, W. W., 14 Aug 2018, In : arXiv.org,e-Print Archive, Mathematics. 2018, 24 p., 1808.04550

Research output: Contribution to journalArticleAcademic

Open Access
File
Kinematics
Trajectories
Discriminators
Kalman filters
Industry
1 Citations

From typical sequences to typical genotypes

Tal, O., Tran, T. D. & Portegies, J., 2017, In : Journal of Theoretical Biology. 419, April 2017, p. 159-183 25 p.

Research output: Contribution to journalArticleAcademicpeer-review

Genotype
Classifiers
Entropy
entropy
genotype
1 Citations

Intrinsic flat and Gromov-Hausdorff convergence of manifolds with Ricci curvature bounded below

Matveev, R. & Portegies, J. W., 1 Jul 2017, In : The Journal of Geometric Analysis. 27, 3, p. 1855-1873 19 p.

Research output: Contribution to journalArticleAcademicpeer-review

Open Access
File
Gromov-Hausdorff Convergence
Ricci Curvature
Hausdorff Measure
Riemannian Manifold
Multiplicity

Courses

Analysis 1

1/09/12 → …

Course

Analysis 2

1/09/12 → …

Course

Linear algebra and applications

1/09/17 → …

Course

Student theses

Active learning in VAE latent space

Author: Tonnaer, L., 25 Sep 2017

Supervisor: Menkovski, V. (Supervisor 1), Portegies, J. W. (Supervisor 2) & Holenderski, M. (Supervisor 2)

Student thesis: Master

File

Disease dynamics in the framework of interacting particle systems: the position dependent SIS-model

Author: van den Berg, N., Mar 2018

Supervisor: Tse, O. (Supervisor 1) & Portegies, J. (Supervisor 2)

Student thesis: Bachelor

File

Efimov trimers in a harmonic potential

Author: Portegies, J., 31 Aug 2009

Supervisor: Kokkelmans, S. (Supervisor 1), de Graaf, J. (Supervisor 2) & Slot, J. (Supervisor 2)

Student thesis: Master

File

Learning geometry using PCA and diffusion maps

Author: Wemmenhove, A. (., Apr 2018

Supervisor: Portegies, J. (Supervisor 1)

Student thesis: Bachelor

File