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  Journal Publications
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  1. D. Ovadia, A. Segal and N. Rabin, Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels transform, Scientific Reports, vol. 14, issue 1, pp. 4134, 2024.

  2. I. Muller, Z. Ovadia-Blechman, N. Moyal, N. Darchi, O. Hoffer, M. Halak, N. Rabin. Combining Thermal Imaging and Machine Learning to Noninvasively Characterize Palm Perfusion During Local Blood Pressure Changes. Biomedical Signal Processing and Control, vol. 92, pp. 106109,  2024.

  3. O. Hoffer, R. Y. Brzezinski, A. Ganim, P. Shalom, Z. Ovadia-Blechman, L. Ben-Baruch, N. Lewis, R. Peled, C. Shimon, N. Naftali-Shani, E. Katz, Y. Zimmer, N. Rabin, Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm, Journal of Biophotonics, pp. e202300486, Accepted. Jan. 2024.

  4. R. Shuhendler, N. Rabin, Dynamic Artist-based Embeddings with Application to Playlist Generation, Engineering Applications of Artificial Intelligence, vol. 129, pp. 107604, 2024.

  5. Z. Ovadia-Blechman, Y. Hauptman, N. Rabin, G. Wiezman, O. Hoffer, D. Gertz, B. Gavish, L. Gavish, Morphological Features of the Photoplethysmographic Signal: A New Approach to Characterize the Microcirculatory Response to Photobiomodulation, Frontiers in Physiology, vol. 14, pp. 1175470, 2023.

  6. V. Simon, N. Rabin, H. Chalutz-Ben Gal
    Utilizing data driven methods to identify gender bias in LinkedIn profiles.
    Information Processing & Management, vol. 60, issue 5, pp. 103423, 2023    .

  7. N.Rabin, Á. Fernández and D. Fishelov, Multiscale Extensions for Enhancing Coarse Grid Computations.Journal of Computational and Applied Mathematics, vol. 427, pp. 115116, 2023

  8. A. Ratnovsky, S. Malayev, S. Ratnovsky, S. Naftali and N. Rabin, EMG-based Speech Recognition Using Dimensionality Reduction Methods, Journal of Ambient Intelligence and Humanized Computing, vol. 14, pp. 597-607, 2023.

  9. Y. Bregman, Y. Radzyner, Y. Ben Horin, M. Kahlon and N. Rabin, Machine learning based earthquake-explosion discrimination for Sea of Galilee Seismic events of July 2018, Pure and Applied Geophysics, accepted, Aug. 2022.

  10. A. Knish and N. Rabin, Thermal heat distribution features for hand identification, Expert Systems with Applications, vol. 203, pp. 117462, 2022.

  11. I. Niv, Y. Bregman and N. Rabin, Identification of Mine Explosions Using Manifold Learning Techniques, IEEE Transactions on Geoscience and Remote Sensing vol. 60, pp. 1-13, 2022

  12. R. Y. Brzezinski, N. Rabin, N. Lewis, R. Peled, A. Kerpel, A. M. Tsur, O. Gendelman, N. Naftali-Shani, I. Gringauz, H. Amital, A. Leibowitz, H. Mayan, I. Ben-Zvi, E. Heler, 
    L. Shechtman, O. Rogovski, S. Shenhar-Tsarfaty, E. Konen, E. M. Marom, A. Ironi, G. Rahav, Y. Zimmer, E. Grossman, Z. Ovadia-Blechman, J. Leor and O. Hoffer, Automated processing of thermal imaging to detect COVID-19. Scientific Reports, vol. 11, pp. 17489, 2021.

  13. Z. Ovadia-Blechman, O. Hoffer, M. Halak, K. Adrai, Y. Zimmer, D. Silverberg and N. Rabin, Assessment of blood distribution in response to post-surgical steal syndrome: A novel technique based on Thermo-Anatomical Segmentation, Journal of Biomechanics, vol. 119, pp. 110304, 2021.

  14. R. Y. Brzezinski, L. Levin‐Kotler, N. Rabin, Z. Ovadia‐Blechman, Y. Zimmer, A. Sternfeld, J. Molad-Finchelman, R. Unis, N. Lewis, O. Tepper‐Shaihov, N. Naftali‐Shani, N. Balint-Lahat, M. Safran, Z. Ben‐Ari, E. Grossman, J. Leor and O. Hoffer, Automated thermal imaging for the detection of fatty liver disease, Scientific Reports, vol. 10, issue 1, pp. 1—11, 2020.

  15. Y. Bregman, O. Lindenbaum and N. Rabin, Array Based Earthquakes-Explosion Discrimination Using Diffusion Maps, Pure and Applied Geophysics, Accepted March 2020.

  16. L. Gavish, O. Hoffer, N. Rabin, M. Halak, S. Shkilevich, Y. Shayovitz, G. Weizman, O. Haim, B. Gavish, D. Gertz and  Z.Ovadia‐Blechman, Microcirculatory Response to Photobiomodulation—Why Some Respond and Others Do Not: A Randomized Controlled Study, Lasers in Surgery and Medicine, vol. 52, issue 9, pp. 863—872, 2020. 

  17. Á. Fernández, N. Rabin, D. Fishelov and J. R. Dorronsoro, Auto-adaptive multi-scale Laplacian Pyramids for modeling non-uniform data, Engineering Applications of Artificial Intelligence, vol. 93, pp. 103682, 2020. 

  18. M. Golan, G. Singer, N. Rabin and  D. Kleper, Integrating actual time usage into the assessment of examination time extensions provided to disabled college engineering students, Assessment & Evaluation in Higher Education, vol. 45, issue 7, pp. 988--1000, 2020

  19. G. Singer, M. Golan, N. Rabin and D. Kleper, Evaluation of the effect of learning disabilities and accommodations on the prediction of the stability of academic behavior of undergraduate engineering students using decision trees, European Journal of Engineering Education,  vol. 45, issue 4, pp. 614--630, 2020.

  20. Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniquesN. Rabin, M. Kahlon, S. Malayev and A. Ratnovsky, Expert Systems with Applications, vol. 149, pp. 113281, 2020.

  21. R. Y. Brzezinski, Z. Ovadia-Blechman, N. Lewis, N. Rabin, Y. Zimmer, L. Levin-Kotler, O. Tepper-Shaihov, N. Naftali-Shani, O. Tsoref, E. Grossman, J. Leor and O. Hoffer, Non-invasive Thermal Imaging of Cardiac Remodeling in Mice, Biomedical Optics Express, vol. 10, issue 12, pp.6189--6203, 2019.

  22. O. Lindenbaum, N. Rabin, Y. Bregman and A. Averbuch, Seismic Event Discrimination Using Deep CCA, IEEE Geoscience and Remote Sensing Letters, vol. 17, issue 11, pp. 1856--1860, 2019. 

  23. N. Rabin, M. Golan, G. Singer and D. Kleper, Modeling and Analysis of Students' Performance Trajectories using Diffusion Maps and Kernel Two-Sample Tests, Engineering Applications of Artificial Intelligence, vol. 85, pp. 492--503, 2019.

  24. N. Rabin and D. Fishelov, Two Directional Laplacian Pyramids with Application to Data Imputation, Advances in Computational Mathematics, vol. 45, issue 4, 2123–2146, 2019. 

  25. Y. Bregman and N. Rabin, Aftershock Identification Using Diffusion Maps, Seismological Research Letters, Special Focus Section on Machine Learning in Seismology,  vol. 90 no, 2A, pp. 539-545, 2018. 

  26. Z. Ovadia-Blechman, A. Gritzman, M. Shuvi, B. Gavish, V. Aharonson and N. Rabin, The Response of Peripheral Microcirculation to Gravity-Induced Changes, Clinical Biomechanics, vol. 56, pp. 19-25, 2018. 

  27. O. Lindenbaum, Y. Bregman, N. Rabin and A. Averbuch, Multi-view Kernels for Low-dimensional Modeling of Seismic Events, IEEE Transactions on Geoscience and Remote Sensing, vol. 56, issue 6, pp. 3300-3310, 2018.

  28. N. Rabin and D. Fishelov, Multi-scale Kernels for Nystrom Based Extension Schemes, Applied Mathematics and Computation, vol. 319, pp. 165-177, 2018.

  29. S. Heilbrunn, N. Rabin and S. Rozenes, Detecting Mutual Configurations of Applied Planning Strategies and Performances in Small and Medium Sized Businesses with Kernel Based Machine Learning Methods, Applied Soft Computing, vol. 66, pp. 1211-1225, 2017.

  30. N. Rabin, Y. Bregman, O. Lindenbaum, Y. Ben-Horin and A.  Averbuch, Earthquake-Explosion Discrimination using Diffusion Maps, Geophysical Journal International, vol. 2017, issue 3, pp. 1484-1492, 2016.

  31. Z. Ovadia-Blechman, A. Meilin, N. Rabin, M. Eldar and D. Castel,  Noninvasive Monitoring of Peripheral Microcirculatory Hemodynamics Under Varying Degrees of Hypoxia, Respiratory Physiology & Neurobiology, vol. 216, pp. 23-27, 2015.

  32. E. Chiavazzo, C. W. Gear, C. J. Dsilva, N. Rabin and I. G. Kevrekidis,  Reduced Models in Chemical Kinetics via Nonlinear Data Mining, Processes, vol. 2, issue 1, pp. 112-140, 2014. 

  33. Á. Fernández, N. Rabin, R. R. Coifman and J. Eckstein, Diffusion Methods for Aligning Medical Datasets: Location Prediction in CT Scan Images, Medical Image Analysis, vol. 18, issue 2, pp. 425-432, 2014. 

  34. C. J. Dsilva, R. Talmon, N. Rabin, R.R. Coifman and I.G. Kevrekidis, Nonlinear Intrinsic Variables and State Reconstruction in Multiscale Simulations, Journal of Chemical Physics, vol. 139, issue 18, pp. 11B608-1, 2013.

  35. K. P. Stanton, F. Parisi, F. Strino, N. Rabin, P. Asp and Y. Kluger, Arpeggio: Harmonic Compression of ChIP-seq Data Reveals Protein Chromatin Interaction Signatures, Nucleic Acids Research, vol. 41, issue 16, pp. e-161, 2013. 

  36. A. Averbuch, N. Rabin, A. Schclar and V. Zheludev, Dimensionality Reduction for Detection of Moving Vehicles, Pattern Analysis and Applications, vol. 15, issue 1, pp. 19-27, 2012. 

  37. A. Schclar, A. Averbuch, N. Rabin, V. Zheludev and K. Hochman, A Diffusion Framework for Detection of Moving Vehicles, Digital Signal Processing, vol. 20, issue 1, pp. 111-122, 2010. 

  38. A. Averbuch, V.A. Zheludev, N. Rabin and A. Schclar, Wavelet-based Acoustic Detection of Moving Vehicles, Multidimensional Systems and Signal Processing, vol. 20, issue 1, pp. 55-80, 2009. 

  39. A. Averbuch, B. Epstein, N. Rabin, E. Turkel, Edge-Enhancement Postprocessing using Artificial Dissipation, IEEE Trans. on Image Processing, vol. 15, issue 6, 2006.

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  Conference Proceedings

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  1. B. Hen, Á. Fernández and N. Rabin, Improving Laplacian Pyramids Regression with Localization in Frequency and Time, ESANN 22, Bruges, Belgium, Oct 5-7, 2022.

  2. N. Rabin, Multi-Directional Laplacian Pyramids for Completion of Missing Data Entries, Proceedings of the 28th European Symposium on Artificial Neural Networks, ESANN 20, Bruges, Belgium, Oct 2-4,2020 (virtual).

  3. N. Rabin and D. Fishelov, A Multi-scale Approach for Data Imputation, 2018 ICSEE – IEEE International Conference on the Science of Engineering, Eilat, Israel, Dec.  12-14 2018.

  4. O. Lindenbaum, N. Rabin, Y. Bregman and A. Averbuch, Multi-Channel Fusion for Seismic Event Detection and Classification, 2016 ICSEE – IEEE International Conference on the Science of Engineering, Eilat, Israel, Nov.16-18 2016, pp.  1-5, 2016.

  5. Á. Fernández,  N. Rabin,  D. Fishelov and J. Dorronsoro, Auto-adaptive Laplacian Pyramids, In the proceedings of the 24th European Symposium on Artificial Neural Networks, ESANN 16, Bruges, Belgium, April 27-29,2016, pp.  59-64, 2016.

  6. I.  Shapiro,  N. Rabin,  I.  Opher  and  I.  Lapidot, Clustering  Short  Push-to-Talk Segments,
    INTERSPEECH 2015, 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, Sep.6-10, 2015, pp.  3031-3035, 2015.

  7. N. Rabin and  R. R. Coifman,  Heterogeneous  Datasets  Representations and Learning using Diffusion Maps and Laplacian Pyramids, Proceedings of the 2012 SIAM International Conference on Data Mining, Anaheim, California, USA, April 26-28, 2012, pp.  189-199, 2012.

  8. N. Rabin and A. Averbuch, Detection of Anomaly Trends in Dynamically Evolving Systems, Proceedings of the AAAI Fall Symposium on Manifold Learning and its Applications, Arlington, Virginia, Nov.  11-13 2010, pp.44-49, 2010.

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  Book Chapters

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In: Gervasi O. et. al. (eds)  ComputationalScience and Its Applications - ICCSA 2017. ICCSA 2017 Lecture Notesin Computer Science, vol. 10404, Springer, Cham, pp. 284-297.

 

  • N. Rabin and Y. Bregman, Machine Learning for the Geosciences

In: Machine Learning for Data Science Handbook: Data Mining & Knowledge Discovery Handbook, Edited by Lior Rokach, Oded Maimon, Erez Shmueli, pp. 779-800, 2023.

             

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​  Technical Reports

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  1. M. Golan, G. Singer, N. Rabin and D. Kleper, Evaluating the effectiveness of adjustments given to engineering students, National Institute for Testing and Evaluations research report, ISBN:978-965-502-202-5, Nov, 2016.

  2. N. Rabin and R. R. Coifman, Modeling zonal electricity prices by anisotropic diffusion embeddings,
    Yale University Research Report #1457, 2012.

  3. A. Averbuch, T. Kärkkäinen, P. Neittaanmäki, P. Nieminen, N. Rabin and V.  Zheludev, Applications  of  Dimension  Reduction,  Classification,  and Neural Prediction for Industrial Process Data,
    Reports of the Department of Mathematical Information Technology, Series C, Software and Computational  Engineering  No.   C  4/2010,  University  of  Jyväskylä,  Jyväskylä, Finland, 2010.

  4. P. Nieminen, N. Rabin, T. Karkkainen, A. Averbuch and S. Ayramo,
    Robust Clustering and Neural Network Training with Dimension Reduction for Industrial Use,
    Reports of the Department of Mathematical Information  Technology,  Series C,  Software  and  Computational  Engineering  No.C 3/2010, University of Jyväskylä, Jyväskylä, Finland, 2010.

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  Thesis

     

  • Ph.D. Thesis : Data mining in dynamically evolving systems via diffusion methodologies, March 2010

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  • M.Sc. Thesis : Edge-Enhancement postprocessing using artificial dissipation, 2002.

       

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