Boumediene Hamzi
Scholar

Boumediene Hamzi

Google Scholar ID: 2goK6M0AAAAJ
Department of Computing and Mathematical Sciences, Caltech & The Alan Turing Institute
Kernel MethodsMachine LearningAlgorithmic Information TheoryDynamical SystemsControl Theory
Citations & Impact
All-time
Citations
1,075
 
H-index
19
 
i10-index
32
 
Publications
20
 
Co-authors
68
list available
Contact
Resume (English only)
Research Experience
  • Currently Senior Scientist at Caltech's Department of Computing and Mathematical Sciences.
  • Affiliate Fellow at the Data Science Institute, Imperial College London.
  • External Researcher at the Alan Turing Institute (London, UK).
  • Twice awarded Marie Curie Fellowships—at Imperial College London, and at Yıldız Technical University and Koç University (Istanbul, Turkey).
  • Former Temporary Assistant Professor at University of Paris-Sud/Orsay.
  • Former INRIA Research Fellow.
  • Former Research Assistant Professor at UC Davis (CA).
  • Held research positions at the Mathematical Sciences Research Institute (Berkeley), Duke University, and the Fields Institute (Toronto).
  • Most recently served as Visiting Professor at Johns Hopkins University.
  • Co-leads the 'Machine Learning and Dynamical Systems' Research Interest Group (RIG) at the Alan Turing Institute with Prof. Robert Mackay.
  • Co-organizer of the 'One World Seminar Series on the Mathematics of Machine Learning'.
Background
  • A mathematician embodying a modern-day peripatetic, having extensively traversed the global academic world.
  • Central research question: How can complex systems be effectively analyzed?
  • Research approaches span three key areas: Dynamical Systems Theory (DST), Machine Learning (ML), and Algorithmic Information Theory (AIT).
  • Early research focused on the 'Dynamical Theory of Control', integrating dynamical systems theory and control theory, especially for systems with bifurcations.
  • Current research interests lie at the intersection of Machine Learning, Dynamical Systems, and Algorithmic Information Theory.