עברית

Dr. Ofir Lindenbaum

Email
ofir.lindenbaum@biu.ac.il
Office
1103 room 328
Reception Hours
By appointment via email
Personal Website
https://www.eng.biu.ac.il/lindeno/
CV

Dr Ofir Lindenbaum CV 

About me

I am a senior lecturer (assistant professor) at Bar Ilan University, Faculty of Engineering. I completed a Postdoc at Yale University in the applied math department, working with Prof. Ronald Coifman and Prof. Yuval Kluger. I completed my Ph.D. at the Department of Electrical Engineering at Tel Aviv University under the supervision of Prof. Arie Yeredor and Prof. Amir Averbuch.  My research is primarily focused on developing novel machine-learning methods for scientific discovery. My interest includes computational biology, signal processing, music, and audio analysis, manifold learning, spectral methods for data mining, and dimensionality reduction. I am interested in theory and practice of the following challenges:

  • Unsupervised learning- extracting latent parameters from high dimensional or multi-modal data.
  • Interpretability- exploiting sparsity to learn robust interpretable models that lead to the identification of predictive variables.
  • Generative Models- synthesizing reliable artificial data to enhance the prevalence of rare sub-population in biological/dynamical processes.

Work Experience 

2021–Present Bar-Ilan University 
Assistant Professor at the Faculty of Engineering 

2018–2021 Yale University 
Gibbs Assistant Professor in Applied Mathematics 
Invented several supervised and unsupervised machine learning methods for identifying meaningful parameters from raw empirical measurements.
Numerous publications in top-tier conferences and Q1 Journals.

2012–2017 Tel Aviv University
Head Teaching Assistant

2008–2010 Zoran Microelectronics
Analog Circuit designer

2002–2004 Israeli Air Force
Flight Cadet

Education 

2017–2018 Yale University
Postdoctoral research, Host: Prof. Ronald R. Coifman

2012–2017 Tel Aviv University
Ph.D. in Electrical Engineering, GPA: 93
Advisors: Prof. Amir Averbuch and Prof. Arie Yeredor.

2010–2012 Tel Aviv University
M.Sc. in Electrical Engineering
Advisor: Prof. Arie Yeredor and Prof. Israel Cohen.

2006–2010 Technion - Israel Institute of Technology
B.Sc. in Electrical Engineering, B.Sc. in Physics (both Summa Cum Laude), GPA: 96

Teaching Experience

Fall 2018, 2020 Lecturer at Yale University
“Linear algebra with applications” (MATH 222)

Spring 2018–2020 Lecturer at Yale University 
Senior seminar: “Mathematical topics on networks” (MATH 480)

2012–2017 Head Teaching Assistant at Tel Aviv University
“Analog Circuits”

2013–2017 Head Teaching Assistant at Tel Aviv University
“Basic Electronics”

2012 Teaching Assistant at Tel Aviv University
“Random Signals and Noise”

2012–2016 Undergraduate Project Supervisor at Tel Aviv University
Guided 6 signal processing projects including 2 excellence project awards.

Scholarships and Awards

  • 2016 The Weinstein Research Institute for Signal Processing award for scientific paper publication
  • 2015 The David and Paulina Trotsky foundation award for outstanding Ph.D. students  
  • 2014 Tel Aviv university award for outstanding teaching assistants
  • 2011 The Weinstein Research Institute for Signal Processing award for outstanding graduate students
  • 2006–2010 President’s list, The Technion- Israel Institute of Technology

Grants

  • 2022 Data Science Institute grant 100K$ 
  • 2019–2020 Collaborator on several NIH grants in genomics

Academic Experience

  • 2022 – Present Reviewer: Transactions on Machine Learning Research (TMLR)
  • 2022 – Present Reviewer:  Transactions on Image Processing
  • 2020 – Present Reviewer:  International Conference on Learning Representations (ICLR)
  • 2018 – Present Reviewer:  Conference on Neural Information Processing Systems (NeurIPS)
  • 2019 – Present Reviewer:  International Conference on Machine Learning (ICML)
  • 2019 Reviewer:  SIAM Journal on Mathematics of Data Science
  • 2015 – 2018 Reviewer:  IEEE Transactions on signal processing
  • 2019 – 2021 Seminar organizer: Yale University- Program in applied mathematics
Research
  • Machine Learning
  • Deep Learning and Applications
  • Feature Extraction and Selection
  • Learning from Biomedical Data
Publications

Journal and Full Length Conference Papers

  1. J. Yang, O. Lindenbaum, Y. Kluger, A. Jaffe, “Multi-modal Differentiable Unsupervised Feature Selection.” The Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
  2. S. Jana, H. Li, Y. Yamada,O. Lindenbaum, “Support recovery with Projected Stochastic Gates: Theory and application for linear models.” Elsevier Signal Processing, 2023.
  3. J. Yang*,O. Lindenbaum*, Y. Kluger, “Locally Sparse Neural Networks for Tabular Biomedical Data.”International Conference on Machine Learning (ICML), 2022.
  4. O. Lindenbaum, M. Salhov, A. Averbuch, Y. Kluger, “ℓ0-based Sparse Canonical Correlation Analysis.” International Conference on Learning Representations (ICLR), 2022.
  5. U. Shaham*, O. Lindenbaum*, J. Svirsky, Y. Kluger, “Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features.” Elsevier, Neural Networks, 2022.
  6. O. Lindenbaum, S. Steinerberger, “Refined Least Squares for Support Recovery.” Elsevier, Signal Processing, 2022.
  7. Shelli F, O. Lindenbaum, et al., “HIV viral transcription and immune perturbations in the CNS of people with HIV despite ART.” JCI insigh, 2022.
  8. O. Lindenbaum*, U. Shaham*, J. Svirsky, E. Peterfreund, Y. Kluger, “Differentiable Unsupervised Feature Selection Based on a Gated Laplacian.” Conference on Neural Information Processing Systems (NeurIPS), 2021.
  9. J. Zhao, A. Jaffe, H. Li, O. Lindenbaum, X. Cheng, R. Flavell, Y. Kluger, “Detecting regions of differential abundance between scRNA-seq datasets.” Proceedings of the National Academy of Sciences (PNAS) , 118.22, 2021.
  10. O. Lindenbaum*, A. Sagiv*, G. Mishne, R Talmon, “Kernel-based parameter estimation of dynamical systems with unknown observation functions.” Chaos: An Interdisciplinary Journal of Nonlinear Science, 31.4: 043118, 2021.
  11. L. Irshaid, E. Weinberger, J. Garritano, R. Shallis, J. Patsenker, O. Lindenbaum, Y. Kluger, S. Katz, M. Xu. “Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.” Archives of Pathology and Laboratory Medicine, 2021.
  12. O. Lindenbaum, N. Nouri, Y. Kluger, S. H. Kleinstein, “Alignment free identification of clones in B cell receptor repertoires.” Nucleic Acid Research (NAR), 2021.
  13. O. Lindenbaum, S. Steinerberger, “Randomly Aggregated Least Squares for Support Recovery.” Elsevier Journal of Signal Processing, Vol. 180, 107858, 2021.
  14. H. Li*, O. Lindenbaum*, X. Cheng, A. Cloninger, “Variational Diffusion Autoencoders with Random Walk Sampling.” European Conference on Computer Vision (ECCV), 2020.
  15. Y.Yamada*, O. Lindenbaum*, S. Negahban, Y. Kluger., “Feature selection using Stochastic Gates.” International Conference on Machine Learning (ICML), 2020.
  16. E. Peterfreund*, O. Lindenbaum*, F. Dietrich, T. Bertalan, M. Gavish, I. G. Kevrekidis, R. R. Coifman, “LOcal Conformal Autoencoder for standardized data coordinates.” Proceedings of the National Academy of Sciences (PNAS), 117(49), pp.30918-30927, 2020.
  17. A. Jaffe, Y. Kluger, O. Lindenbaum, J. Patsenker, E. Peterfreund, S. Steinerberger. (alphabetical order) “The Spectral Underpinning of word2vec.” Frontiers in Applied Mathematics and Statistics, 6:593406, 2020.
  18. O. Lindenbaum, M. Salhov, A. Yeredor, A. Averbuch, “Gaussian Bandwidth Selection for Manifold Learning and Classification.” Data Mining and Knowledge Discovery, pp. 1-37, 2020.
  19. Y. Bregman, O. Lindenbaum, N. Rabin, “Array Based Earthquakes-Explosion Discrimination Using Diffusion Maps.” Pure and Applied Geophysics, pp. 1-16, 2020.
  20. O. Lindenbaum, A. Yeredor, M Salhov, A. Averbuch, “Multiview diffusion maps.” Information Fusion, vol. 55, pp. 127-149, 2020.
  21. O. Lindenbaum, N. Rabin, Y. Bregman, A. Averbuch, “Seismic Event Discrimination Using Deep CCA.” IEEE Geoscience and Remote Sensing Letters, 2019.
  22. M Salhov, O. Lindenbaum, Y Aizenbud, A Silberschatz, Y Shkolnisky, A Averbuch, “Multi-view kernel consensus for data analysis.” Applied and Computational Harmonics Analysis (ACHA), vol 49.1, pp. 208-228, 2019.
  23. O. Lindenbaum*, Jay S. Stanley III*, Guy Wolf, Smita Krishnaswamy, “Geometry-Based Data Generation.” Conference on Neural Information Processing Systems (NeurIPS), 2018 (spotlight 4% acceptance rate).
  24. O. Lindenbaum, Y. Bregman, N. Rabin, A. Averbuch., “Multi-View Kernels for Low-Dimensional Modeling of Seismic Events.” IEEE Transactions on Geoscience and Remote Sensing (TGRS), vol. 56.6, pp. 3300-3310, 2018.
  25. N. Rabin, Y. Bregman, O. Lindenbaum, Y. Ben-Horin, A. Averbuch, “Earthquake-explosion discrimination using diffusion maps.” Geophysical Journal International, vol. 207.3, pp. 1484-1492, 2018.
  26. O. Lindenbaum, A. Yeredor, I. Cohen, “Musical key extraction using diffusion maps.” Elsevier Journal of Signal Processing, vol. 117, pp. 198-207, 2015.
  27. O. Lindenbaum, A. Yeredor, R. Vitek, M. Mishali. “Blind separation of orthogonal mixtures of spatially-sparse sources with unknown sparsity levels and with temporal blocks.” Journal of Signal Processing Systems, 79(2), pp.167-178, 2015.

*Indicates equal contribution

Short Conference and Workshop Proceedings

  1. B. Battash, L. Wolf, O. Lindenbaum, “Revisiting the noise Model of SGD.” NeurIPS 2023 Workshop Heavy Tails in Machine Learning, 2023.
  2. T. Yampolsky, R. Talmon, O. Lindenbaum, “Domain and Modality Adaptation Using Multi-Kernel Matching.”European Signal Processing Conference (EUSIPCO)), 2023.
  3. J. Svirsky, O. Lindenbaum, “SG-VAD: Stochastic Gates Based Speech Activity Detection.” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023.
  4. O. Ophir, O. Shefi, O. Lindenbaum, “Neuronal Cell Type Classification Using Locally Sparse Networks.” IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023.
  5. O. Lindenbaum, A. Yeredor, A. Averbuch. “Clustering based on multiview diffusion maps.” IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016.
  6. O. Lindenbaum, N. Rabin, Y. Bregman, A. Averbuch, “Multi-channel fusion for seismic event detection and classification.” IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.
  7. O. Lindenbaum, A. Yeredor, A. Averbuch. “Bandwidth selection for kernel-based classification.” IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.
  8. A. Averbuch, M. Salhov, O. Lindenbaum, A. Silberschatz, Y. Shkolnisky, “Multi-view kernel-based data analysis.” IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016.
  9. O. Lindenbaum, M. Salhov, A. Yeredor, “Learning coupled embedding using multiview diffusion maps.”International Conference on Latent Variable Analysis and Signal Separation (LVA/SCA), 2015.
  10. O. Lindenbaum, A. Yeredor, R. Vitek, M. Mishali. “Blind separation of spatially-block-sparse sources from orthogonal mixtures.” IEEE. International Workshop on Machine Learning for Signal Processing (MLSP), 2013.
  11. O. Lindenbaum, S. Maskit, O. Kutiel, G. Nave, “Musical features extraction for audio-based search.” IEEE 26th Convention of Electrical and Electronics Engineers in Israel (IEEEI), 2010.

*Indicates equal contribution

Refereed Abstracts

  1. S. Farhadian, O. Lindenbaum, J. Zhao, R. Garcia-Milian, J. Chiarella, M. Chintanaphol, R. Calvi, Y. Kluger, and S.S. Spudich., “Single-cell genomic analysis of blood and csf t cells in hiv+ and hiv–adults.” Conference on Retroviruses and Opportunistic Infections, 2020.
  2. O. Lindenbaum*, E. Peterfreund*, F. Dietrich, T. Bertalan, M. Gavish, I. G. Kevrekidis, R. R. Coifman, “LOcal Conformal Autoencoder.” Deep Math, 2020.
  3. H. Li, O. Lindenbaum, X. Cheng, A. Cloninger, “Variational Diffusion Autoencoders.” Deep Math, 2019.
  4. L. Irshaid, E. Weinberger, J. Garritano, J. Patsenker, O. Lindenbaum, Y. Kluger, S. Katz, M. Xu. “Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.” Laboratory Investigation, 2019.
  5. O. Lindenbaum, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy, “Geometry-Based Data Generation.” ICML Workshop on Computational Biology, 2018.

Invited Talks

  • 2023 Weizmann institute, invited talk 
  • 2022 Bar Ilan University, Learning club, invited talk
  • 2021 Tel Aviv University, Applied Math, invited talk
  • 2021 Technion Israel Institute of Technology, Electrical and Computer Engineering, invited talk                                                
  • 2021 Yale University, Program in applied math, invited talk
  • 2020 Second Symposium on Machine Learning and Dynamical Systems online, invited talk 
  • 2018 Weizmann University, Faculty of Mathematics and Computer Science, invited Talk
  • 2016 University of California, Berkeley, Department of Electrical Engineering, invited Talk
  • 2016 Princeton University, Mathematics Program in Applied and Computational Mathematics, invited talk 
  • 2016 Yale University, Program in applied math, invited talk 
  • 2014 California Institute of Technology, invited talk
  • 2014 Yale University, Program in applied math, invited Talk

 Contributed Talks

  • 2023 Uncertainty in Artificial Intelligence (UAI), contributed talk
  • 2022 International Conference on Machine Learning (ICML), contributed talk
  • 2022 International Conference on Learning Representations(ICLR), contributed talk
  • 2021Conference on Neural Information Processing Systems(NeuRIPS), contributed talk
  • 2020 International Conference on Machine Learning (ICML), contributed talk
  • 2020 European Conference on Computer Vision (ECCV), contributed talk
  • 2018 Conference on Neural Information Processing Systems (Neurips), spotlight talk
  • 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), contributed talk
  • 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE) , contributed Talk 
  • 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), contributed Talk
  • 2010 IEEE Convention of Electrical and Electronics Engineers in Israel (IEEEI), contributed Talk
Media

פן, ליה. 2010. “האלגו-רית’ם של קותימן.” הארץ. November 8, 2010. https://www.haaretz.co.il/captain/net/2010-11-08/ty-article/0000017f-db1a-df0f-a17f-df5b68d40000.

Last Updated Date : 22/01/2024