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Igor Yanovsky

Data Scientist

About

Bio

Igor Yanovsky received his B.S. and M.A. degrees in 2002 and his Ph.D. in Applied Mathematics in 2008, all from the University of California, Los Angeles (UCLA), CA, USA. Since 2008, he has been with the NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, where he is currently a Data Scientist. From 2011 to 2021, he was associated with the Joint Institute for Regional Earth System Science and Engineering at UCLA. Since 2021, he has been an Associate Project Scientist in the Department of Mathematics at UCLA. His work supports both current and future mission formulations, enhancing the design and implementation of scientific missions by improving the accuracy and efficiency of data analysis methods. His research interests include developing computational algorithms for solving inverse problems in data science, remote sensing, and image processing.

Education

  • Ph.D., Applied Mathematics, University of California, Los Angeles (2008)
  • M.A., Applied Mathematics, University of California, Los Angeles (2002)
  • B.S., Applied Mathematics, University of California, Los Angeles (2002)

Research Interests

Data Science, Remote Sensing, Image Processing, Applied Mathematics, Optimization, Computational Mathematics, Computational Physics, Computational Biology

Experience

Research Community Service

Member of the Scientific Review Committee, IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2025), 2025.

Member of the Scientific Committee, IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024), 2024.

Guest Editor, "Advanced Super-Resolution Methods in Remote Sensing" Special Issue in Remote Sensing journal, 2021-2023.

Guest Editor, "Image Super-Resolution in Remote Sensing" Special Issue in Remote Sensing journal, 2018-2020.

Organizer, Four minisymposia on "Inverse Problems and Image Analysis in Remote Sensing Science", SIAM Conference on Imaging Science, 2012.

Member of the Scientific Committee, International Symposium on Visual Computing, 2009 - 2018.

Served as a reviewer for multiple scientific journals and conferences.

Achievements

Awards & Recognitions

  • JPL Voyager Award (2025)
  • JPL Voyager Award (2023)
  • JPL Team Award (2022)
  • JPL Award | Discovery Award (2020)
  • JPL Team Award (2019)
  • JPL Voyager Award (2018)
  • JPL Voyager Award (2018)
  • JPL Team Award (2017)
  • JPL Team Award (2016)
  • JPL Team Award (2014)

Publications

  1. A. B. Akins, A. B. Tanner, A. Colliander, N. Schlegel, K. Boudad, I. Yanovsky, S. T. Brown, S. Misra, A Sparse Synthetic Aperture Radiometer Constellation Concept for Remote Sensing of Antarctic Ice Sheet Temperature. IEEE Trans. Geosci. Remote Sens., vol. 63, pp. 1-21, Art no. 5300421, 2025.  https://doi.org/10.1109/TGRS.2025.3534466
  2. I. Yanovsky, D. Posselt, L. Wu, S. Hristova-Veleva, Quantifying Uncertainty in Atmospheric Winds Retrieved from Optical Flow: Dependence on Weather Regime. J. Appl. Meteor. Clim., 63(10):1113–1135, 2024. https://doi.org/10.1175/JAMC-D-23-0169.1
  3. H. Nguyen, D. Posselt, I. Yanovsky, L. Wu, and S. Hristova-Veleva, Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs, Atmos. Meas. Tech., 17, 3103–3119, 2024. https://doi.org/10.5194/amt-17-3103-2024
  4. I. Yanovsky, T. S. Pagano, E. M. Manning, S. E. Broberg, B. M. Sutin, Quantifying Uncertainties in Atmospheric Infrared Sounder (AIRS) Spatial Response Functions. Proc. SPIE 13143, Earth Observing Systems XXIX, 131430F, 2024. https://doi.org/10.1117/12.3029935
  5. A. Akins, A. Tanner, A. Colliander, N. Schlegel, I. Yanovsky, K. Boudad, S. Misra, S. Brown, STASIS: A Concept for Sparse Interferometric Radiometry of the Antarctic Ice Sheet. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 5-8. https://doi.org/10.1109/IGARSS53475.2024.10641833
  6. X. Zeng, H. Su, S. Hristova-Veleva, D. J. Posselt, R. Atlas, S. T. Brown, R. D. Dixon, E. Fetzer, T. J. Galarneau Jr., M. Hardesty, J. H. Jiang, P. P. Kangaslahti, A. Ouyed, T. S. Pagano, O. Reitebuch, R. Roca, A. Stoffelen, S. Tucker, A. Wilson, L. Wu, I. Yanovsky, Vientos—A New Satellite Mission Concept for 3D Wind Measurements by Combining Passive Water Vapor Sounders with Doppler Wind Lidar. Bull. Amer. Meteor. Soc., 105(2), E357-E369, 2024. https://doi.org/10.1175/BAMS-D-22-0283.1
  7. A. Akins, A. Tanner, N. Schlegel, A. Colliander, I. Yanovsky, S. Misra, S. Brown, Building Seasonal Maps of Antarctica’s Temperature with Repeat-Pass Microwave Interferometry. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 352-355. https://doi.org/10.1109/IGARSS52108.2023.10282007
  8. J. Barnett, A. Bertozzi, L. Vese and I. Yanovsky, Incorporating Texture Features into Optical Flow for Atmospheric Wind Velocity Estimation. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 3784-3787. https://doi.org/10.1109/IGARSS52108.2023.10283045
  9. I. Yanovsky, D. Posselt, L. Wu, S. Hristova-Veleva, H. Nguyen, B. Lambrigtsen and X. Zeng, Atmospheric Motion Vector Retrieval Using the Total Variation-Based Optical Flow Method. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 3780-3783. https://doi.org/10.1109/IGARSS52108.2023.10282495
  10. I. Yanovsky, A. Tanner and A. Akins, Reconstruction of Ice Sheet Temperature Maps Using a Sparsity-Based Image Deconvolution Method. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 5661-5664. https://doi.org/10.1109/IGARSS52108.2023.10283184
  11. J. R. Barnett, A. Bertozzi, L. A. Vese, I. Yanovsky, Texture-based optical flow for wind velocity estimation from water vapor data, Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430H, 2023. https://doi.org/10.1117/12.2663008
  12. B. Lambrigtsen, P. Kangaslahti, O. Montes, N. Niamsuwan, D. Posselt, J. Roman, M. Schreier, A. Tanner, L. Wu and I. Yanovsky, A Geostationary Microwave Sounder: Design, Implementation and Performance. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 623-640, 2022. https://doi.org/10.1109/JSTARS.2021.3132238
  13. I. Yanovsky, T. Pagano, E. Manning, S. Broberg, H. Aumann and L. Vese, Learning Spatial Response Functions from Large Multi-Sensor AIRS and MODIS Datasets. SPIE Optics and Photonics, Earth Observing Systems XXVI, Vol. 11829, pp. 52-60, 2021. https://doi.org/10.1117/12.2593331
  14. I. Yanovsky and J. Qin, Spatio-Temporal Super-Resolution Reconstruction of Remote Sensing Data. IEEE International Geoscience and Remote Sensing Symposium, pp. 2907-2910, 2021. https://doi.org/10.1109/IGARSS47720.2021.9553433
  15. I. Yanovsky, T. Pagano, E. Manning, S. Broberg, H. Aumann and L. Vese, AIRS Point Spread Function Reconstruction using AIRS and MODIS Data. IEEE International Geoscience and Remote Sensing Symposium, pp. 7868-7871, 2021. https://doi.org/10.1109/IGARSS47720.2021.9555019
  16. J. Qin and I. Yanovsky, An Effective Super-Resolution Reconstruction Method for Geometrically Deformed Image Sequences. IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, Virtual, Florence, Italy, November 16-20, 2020. https://doi.org/10.1109/MicroRad49612.2020.9342611
  17. I. Yanovsky, J. Qin and B. Lambrigtsen, Spatio-Temporal Resolution Enhancement for Geostationary Microwave Data. IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, Virtual, Florence, Italy, November 16-20, 2020. https://doi.org/10.1109/MicroRad49612.2020.9342539
  18. I. Yanovsky, B. Holt and F. Ayoub, Deriving Velocity Fields of Submesoscale Eddies using Multi-Sensor Imagery. IEEE International Geoscience and Remote Sensing Symposium, pp. 1921-1924, September 26 – October 2, 2020. https://doi.org/10.1109/IGARSS39084.2020.9323797
  19. J. Qin and I. Yanovsky, Robust Super-Resolution Image Reconstruction Method for Geometrically Deformed Remote Sensing Images. IEEE International Geoscience and Remote Sensing Symposium, pp. 8054-8057, Valencia, Spain, 2018. https://doi.org/10.1109/IGARSS.2018.8518056
  20. C. Marshak, I. Yanovsky and L. Vese, Energy Minimization for Cirrus and Cumulus Cloud Separation in Atmospheric Images. IEEE International Geoscience and Remote Sensing Symposium, pp. 1191-1194, Valencia, Spain, 2018. https://doi.org/10.1109/IGARSS.2018.8517940
  21. I. Yanovsky, Y. Wen, A. Behrangi, M. Schreier and B. Lambrigtsen, Validating Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data. IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, pp. 86-90, Cambridge, Massachusetts, 2018.
  22. I. Yanovsky and B. Lambrigtsen, Temporal Super-Resolution of Microwave Remote Sensing Images. IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, pp. 110-115, Cambridge, Massachusetts, 2018.
  23. I. Yanovsky and K. Dragomiretskiy, Variational Destriping in Remote Sensing Imagery: Total Variation with L1 Fidelity. Remote Sensing, vol. 10, no. 2, 300, 2018. https://doi.org/10.3390/rs10020300
  24. I. Yanovsky, A. Behrangi, Y. Wen, M. Schreier, V. Dang and B. Lambrigtsen, Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data. Remote Sensing, vol. 9, no. 11, 1097, 2017. https://doi.org/10.3390/rs9111097
  25. I. Yanovsky, A. Behrangi, M. Schreier, V. Dang, B. Wen and B. Lambrigtsen, Fusion of Microwave and Infrared Data for Enhancing its Spatial Resolution. IEEE International Geoscience and Remote Sensing Symposium, pp. 2625-2628, Fort Worth, Texas, 2017.
  26. K. Dragomiretskiy and I. Yanovsky, Destriping Pushbroom Satellite Imaging Systems with Total Variation-L1/-L2 Method. IEEE International Geoscience and Remote Sensing Symposium, pp. 496-499, Fort Worth, Texas, 2017.
  27. I. Yanovsky and B. Lambrigtsen, Sparsity-based Approaches for Multispectral Super-Resolution of Tropical Cyclone Imagery. IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, pp. 139-144, Espoo, Finland, 2016.
  28. I. Yanovsky and B. Lambrigtsen, Temporal Resolution Enhancement of Image Sequences Capturing Evolving Weather Phenomena. IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, pp. 155-160, Espoo, Finland, 2016.
  29. I. Yanovsky and B. Lambrigtsen, Multispectral Super-Resolution of Tropical Cyclone Imagery using Sparsity-based Approaches. International Journal of Remote Sensing, vol. 37, no. 11, pp. 2494-2509, 2016. https://doi.org/10.1080/01431161.2016.1177245
  30. I. Yanovsky and B. Lambrigtsen, Enhancing the Temporal Resolution of Image Sequences Capturing Evolving Weather Phenomena. Remote Sensing Letters, vol. 7, no. 3, pp. 239-248, 2016. https://doi.org/10.1080/2150704X.2015.1128130
  31. J. Qin, I. Yanovsky and W. Yin, Efficient Simultaneous Image Deconvolution and Upsampling Algorithm for Low Resolution Microwave Sounder Data. Journal of Applied Remote Sensing, vol. 9, no. 1, pp. 095035/1-15, 2015. https://doi.org/10.1117/1.JRS.9.095035
  32. I. Yanovsky, B. Lambrigtsen, A. Tanner and L. Vese, Efficient Deconvolution and Super-Resolution Methods in Microwave Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 9, pp. 4273-4283, 2015. https://doi.org/10.1109/JSTARS.2015.2424451
  33. I. Yanovsky and A.B. Davis, Separation of a Cirrus Layer and Broken Cumulus Clouds in Multispectral Images. IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2275-2285, 2015. https://doi.org/10.1109/TGRS.2014.2352319
  34. B. Gutman, Y. Wang, I. Yanovsky, X. Hua, A. Toga, C. Jack, M. Weiner, P. Thompson, Empowering Imaging Biomarkers of Alzheimer's Disease, Neurobiology of Aging, vol. 36, S69-S80, 2015.
  35. I. Yanovsky, A.B. Davis, V.M. Jovanovic, Separation of Two Cloud Layers in Multispectral Imager Data, IEEE International Geoscience and Remote Sensing Symposium, pp. 1627-1630, 2014.
  36. I. Yanovsky, A. Tanner, B. Lambrigtsen, Efficient Deconvolution and Spatial Resolution Enhancement from Continuous and Oversampled Observations in Microwave Imagery, IEEE Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, pp. 151-156, 2014.
  37. B. Gutman, X. Hua, P. Rajagopalan, Y. Chou, Y. Wang, I. Yanovsky, A. Toga, C. Jack, M. Weiner, P. Thompson, Maximizing Power to Track Alzheimer's Disease and MCI Progression by LDA-Based Weighting of Longitudinal Ventricular Surface Features, NeuroImage, vol. 70, pp. 386-401, 2013.
  38. J. Ye, I. Yanovsky, B. Dong, R. Gandlin, A. Brandt, S. Osher, Multigrid narrow band surface reconstruction via level set functions, In Advances in Visual Computing, pp. 61-70, Springer, 2012. https://doi.org/10.1007/978-3-642-33179-4_7
  39. D. Lee, I. Dinov, B. Dong, B. Gutman, I. Yanovsky, A. Toga, CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms, Computer Methods and Programs in Biomedicine, vol. 106, no. 3, pp. 175-187, 2012. https://doi.org/10.1016/j.cmpb.2010.10.013
  40. X. Hua, B. Gutman, C. Boyle, P. Rajagopalan, A. Leow, I. Yanovsky, A. Kumar, A. Toga, C. Jack, N. Schuff, G. Alexander, K. Chen, E. Reiman, M. Weiner, P. Thompson, Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry, NeuroImage, vol. 57, no 1, pp. 5-14, 2011.
  41. P. Lu, P. Thompson, A. Leow, G. Lee, A. Lee, I. Yanovsky, N. Parikshak, T. Khoo, S. Wu, D. Geschwind, G. Bartzokis, Apolipoprotein E genotype is associated with temporal and hippocampal atrophy rates in healthy elderly adults: A tensor-based morphometry study, Journal of Alzheimer’s Disease, vol. 23, no. 3, pp. 433-442, 2011.
  42. X. Hua, S. Lee, D. Hibar, I. Yanovsky, A. Leow, A. Toga, C. Jack, M. Bernstein, E. Reiman, D. Harvey, J. Kornak, N. Schuff, G. Alexander, M. Weiner, P. Thompson, Mapping Alzheimer's disease progression in 1309 MRI scans: Power estimates for different inter-scan intervals, NeuroImage, vol. 51, no. 1, pp. 63-75, 2010.
  43. A. Ho, X. Hua, S. Lee, A. Leow, I. Yanovsky, B. Gutman, I. Dinov, N. Lepore, J. Stein, A. Toga, C. Jack, M. Bernstein, E. Reiman, D. Harvey, J. Kornak, N. Schuff, G. Alexander, M. Weiner, P. Thompson, Comparing 3 T and 1.5 T MRI for tracking Alzheimer's disease progression with tensor-based morphometry, Human Brain Mapping, vol. 31, no. 4, pp. 499-514, 2010.
  44. X. Hua, S. Lee, I. Yanovsky, A. Leow, Y. Chou, A. Ho, B. Gutman, A. Toga, C. Jack, M. Bernstein, E. Reiman, D. Harvey, J. Kornak, N. Schuff, G. Alexander, M. Weiner, P. Thompson, Optimizing Power to Track Brain Degeneration in Alzheimer's Disease and Mild Cognitive Impairment with Tensor-Based Morphometry: An ADNI Study of 515 Subjects, NeuroImage, vol 48, no. 4, 668-681, 2009.
  45. I. Yanovsky, P. Thompson, S. Lee, S. Osher, A. Leow, Comparing Registration Methods for Mapping Brain Change using Tensor-Based Morphometry, Medical Image Analysis, vol. 13, no. 5, 679-700, 2009. https://doi.org/10.1016/j.media.2009.06.002
  46. A. D. Leow, I. Yanovsky, N. Parikshak, X. Hua, S. Lee, A. W. Toga, C. R. Jack, M. A. Bernstein, P. J. Britson, J. L. Gunter, C. P. Ward, B. Borowski, L. M. Shaw, J. Q. Trojanowski, A. S. Fleisher, D. Harvey, J. Kornak, N. Schuff, G. E. Alexander, M. W. Weiner, P. M. Thompson, Alzheimer’s Disease Neuroimaging Initiative: A One-year Follow up Study Using Tensor-based Morphometry Correlating Degenerative Rates, Biomarkers and Cognition, NeuroImage, vol. 45, no. 3, pp. 645-655, 2009. https://doi.org/10.1016/j.neuroimage.2009.01.004
  47. X. Hua, I. Yanovsky, A. Leow, S. Lee, A. Ho, N. Parikshak, A. Toga, C. Jack, M. Weiner, P. Thompson, Tensor-based morphometry as surrogate marker for Alzheimer's disease and mild cognitive impairment: Optimizing Statistical Power, Organization for Human Brain Mapping, 2009.
  48. I. Yanovsky, P. Thompson, S. Osher, A. Leow, Multimodal Unbiased Image Matching via Mutual Information, Computational Imaging VI, IS&T/SPIE 20th Annual Symposium on Electronic Imaging, Vol. 6814, San Jose, California, January 27-31, 2008. https://doi.org/10.1117/12.775762
  49. I. Yanovsky, P. Thompson, S. Osher, X. Hua, D. Shattuck, A. Toga, A. Leow, Validating Unbiased Registration on Longitudinal MRI Scans from the ADNI, IEEE International Symposium on Biomedical Imaging, pp. 1091-1094, 2008.
  50. I. Yanovsky, P. Thompson, S. Osher, A. Leow, Asymmetric and Symmetric Unbiased Image Registration: Statistical Assessment of Performance, Mathematical Methods in Biomedical Image Analysis, 2008.
  51. I. Yanovsky, C. Le Guyader, A. Leow, P. Thompson, L. Vese, Nonlinear Elastic Registration with Unbiased Regularization in Three Dimensions, The Midas Journal, id. 549, pp. 56-67, http://hdl.handle.net/10380/1360, 2008.
  52. I. Yanovsky, C. Le Guyader, A. Leow, A. Toga, P. Thompson, L. Vese, Unbiased Volumetric Registration via Nonlinear Elastic Regularization, Second MICCAI Workshop on Mathematical Foundations of Computational Anatomy, New York, NY, HAL Open Science, 13 pp., 2008. https://inria.hal.science/inria-00629762v1/file/mfca08_1_1.pdf
  53. I. Yanovsky, P. Thompson, S. Osher, L. Vese, A. Leow, Multiphase Segmentation of Deformation using Logarithmic Priors, IEEE Computer Society Workshop on Image Registration and Fusion, pp. 1-6, Minneapolis, Minnesota, June 23, 2007. https://doi.org/10.1109/CVPR.2007.383431
  54. I. Yanovsky, P. Thompson, A. Klunder, A. Toga, A. Leow, Local Volume Change Maps in Nonrigid Registration: When Are Computed Changes Real?, International Conference on Medical Image Computing and Computer Assisted Intervention, Workshop on Statistical Registration: Pair-wise and Group-wise Alignment and Atlas Formation, Brisbane, Australia, November 2, 2007.
  55. A. Leow, I. Yanovsky, M. Chiang, A. Lee, A. Klunder, A. Lu, J. Becker, S. Davis, A. Toga, P. Thompson, Statistical Properties of Jacobian Maps and the Realization of Unbiased Large-Deformation Nonlinear Image Registration, IEEE Trans. Med. Imaging, vol. 26, no. 6, pp. 822-832, 2007. https://doi.org/10.1109/TMI.2007.892646
  56. I. Yanovsky, M. Chiang, P. Thompson, A. Klunder, J. Becker, S. Davis, A. Toga, A. Leow, Quantifying Deformation Using Information Theory: The Log-Unbiased Nonlinear Registration, IEEE International Symposium on Biomedical Imaging, pp. 13-16, Barcelona, Spain, March 8-11, 2007.
  57. I. Yanovsky, P. Thompson, S. Osher, A. Leow, Topology Preserving Log-Unbiased Nonlinear Image Registration: Theory and Implementation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, 2007. https://doi.org/10.1109/CVPR.2007.383144
  58. I. Yanovsky, S. Osher, P. Thompson, A. Leow, Log-Unbiased Large-Deformation Image Registration, Computer Vision Theory and Applications, vol. 1, pp. 272-279, 2007.