Prof. Eldad Haber
Professor, Department of Earth and Ocean Sciences, University of British Columbia, Canada
Profile
ACADEMIC HOST
Prof. Haim Avron
Department of Applied Mathematics
Lowy Distinguished Guest Professor
1.3.27-1.6.27
Education
- B.Sc. in Mathematics and Geophysics, Tel Aviv University, Israel (1990–1993)
- Ph.D. in Geophysics, Institute of Applied Mathematics, University of British Columbia, Canada (1994–1998)
Professional Experience
- Professor, Department of Earth and Ocean Sciences, University of British Columbia (2014–present)
- Canadian Industrial Research Chair in Computational Geoscience, University of British Columbia (2009–2020; Associate Professor from 2009 to 2014, Professor from 2014 to 2020)
- Associate Professor, Department of Mathematics and Computer Science, Emory University (2007–2009)
- Assistant Professor, Department of Mathematics and Computer Science, Emory University (2002–2007)
- Research Scientist, Schlumberger, EMI (2000–2002)
- Research Scientist and Postdoctoral Fellow, Department of Computer Science, University of British Columbia (1998–2000)
Awards and Fellowships
- SIAM Fellow, Class of 2022
- Department of Energy Career Award (2005)
- NSERC Chair in Computational Geoscience (2010–2020)
Editorial Activities
- Associate Editor, SIAM Journal on Scientific Computing (2008–2017)
- Advisory Board Member, Inverse Problems (2009–2018)
- Advisory Board Member, Canadian Society of Computational Science and Engineering (2023–present)
- Guest Editor, Mathematical Journal of Computer Vision (2019–2020)
Research Interests
- Machine learning and generative modeling
- Machine learning in computational biology
- Machine learning and generative AI in geoscience
- Computational methods for inverse problems
- Computational methods in partial differential equations and optimization
- Computational electromagnetics
Faculty
Exact Sciences
Framework of visit
Lowy Distinguished Guest ProfessorsPeriod of visit
2026-2027Selected Publications
- Haber, E., and D. Oldenburg. “Joint Inversion: A Structural Approach.” Inverse Problems 13 (1997): 63–77.
- Haber, E., U. Ascher, and D. Oldenburg. “On Optimization Techniques for Solving Nonlinear Ill-Posed Problems.” Inverse Problems 16 (2000): 1263–128.
- Haber, E., and L. Ruthotto. “Stable Architectures for Deep Neural Networks.” Inverse Problems (2017).
- Ruthotto, L., and E. Haber. “Deep Neural Networks Motivated by Partial Differential Equations.” Journal of Mathematical Imaging and Vision (2019).
- Eliasof, M., E. Haber, and E. Treister. “PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations.” Advances in Neural Information Processing Systems 34 (2021).
- Omar, S. I., C. Keasar, A. J. Ben-Sasson, and E. Haber. “Protein Design Using Physics Informed Neural Networks.” Biomolecules 13, no. 3 (2023): 457.
- Ahamed, S., and E. Haber. “DAWN-FM: Data-Aware and Noise-Informed Flow Matching for Solving Inverse Problems.” Foundations of Data Science (2026).
- Haber, E., S. Ahamed, M. S. R. Siddiqui, N. Zakariaei, and M. Eliasof. “Iterative Flow Matching—Path Correction and Gradual Refinement for Enhanced Generative Modeling.” SIAM Journal on Scientific Computing (2026).
