YOUSEFI, SIAMAK

Assistant Professor
Department of Ophthalmology
Department of Genetics, Genomics, and Informatics

Office: STE 726 930 MADISON BUILDING
930 MADISON AVENUE
MEMPHIS TN 381632243
Tel: (901) 448-7831
syousef1@uthsc.edu
https://syousefy.wixsite.com/yousefilab

Education

Postdoctoral Fellowship in Computational Ophthalmology, University of California San Diego, 2013-2016

Postdoctoral Fellowship in Brain Computer Interface (BCI), University of California Los Angeles, 2012-2013

PhD in Electrical Engineering, University of Texas Dallas, 2008-2012

Bachelor in Electrical Engineering, Shiraz University, Iran, 2000

Research Interests

Applications of Artificial Intelligence (AI) in Vision and Ophthalmology

Big Ophthalmic Data Mining

Structural, Functional, and Molecular Biomarkers of Glaucoma

Transcriptome Biomarkers of Retinal Ganglion Cell (RGC) Subtypes

Single-cell RNA-seq Data Analysis

Patterns of Structural and Functional Defect in Glaucoma Phenotypes

Corneal Data Analysis

News

  • Hassan Kabiri will join DM2L as a new research fellow in May 
  • Siamak was invited as a speaker for the 26th annual meeting of the "Glaucoma Foundation" on "optic nerve rescue and restoration" taking place at NY in June 2019. He will be talking about "artificial intelligence and future directions in ophthalmology". 
  • Siamak was invited by Emerging Imaging Technologies in Neuroscience (EITN) Study Section to serve as a reviewer in July 2019
  • Siamak to give a talk on "single-cell RNA-Seq analysis of retinal ganglion cells" at "Midsouth Computational Biology and Informatics Society (MCBIOS)", Birmingham, Alabama, in March 2019
  • Siamak to give a talk on "identification of clinically relevant biomarkers of glaucoma on fundus images using deep learning" at "Memphis Data Summit" in March 2019
  • Thanks to NIH for funding our R21 grant. We are now looking for enthusiastic postdocs to join us; computer scientists, data scientists, Biostatisticians, or those in related areas
  • Siamak was invited by NIH''s Biomedical Computing and Health Informatics (BCHI) and Emerging Imaging Technologies in Neuroscience (EITN) Study Sections to serve as a grant reviewer in February and March 2019
  • Siamak will be co-organizing ARVO''s "Artificial intelligence in ocular medicine: Seeing into the future" event taking place June 13 to July 1, 2019. More details at https://www.arvo.org/education/ai-online-event/
  • Article accepted: Promise of optical coherence tomography angiography in predicting glaucoma progression, JAMA Ophthalmology, 2019
  • Accepted as poster in ARVO 2019: Siamak Yousefi, Tobias Elze, Louis Pasquale, and Michael Boland, Glaucoma monitoring using an artificial intelligence enabled map, Association for Research in Vision and Ophthalmology (ARVO), 2019
  • Accepted as oral presentation in ARVO 2019: Mohammad Norouzifard, Ali Nemati, Reinhard Klette, Hamid GholamHossieni, Kouros Nouri-Mahdavi, and Siamak Yousefi, A hybrid machine learning model to detect glaucoma using retinal nerve fiber layer thickness measurements, Association for Research in Vision and Ophthalmology (ARVO), 2019
  • Accepted as poster ARVO 2019: Kouros Nouri-Mahdavi, Alessandro Rabiolo, Vahid Mohammadzadeh, Joseph Caprioli, and Siamak Yousefi, Machine Learning for Prediction of Visual Field Progression, Association for Research in Vision and Ophthalmology (ARVO), 2019
  • Accepted as poster ARVO 2019: Y. Arai, H. Takahashi, S. Yousefi, S. Inoda, H. Tampo, S. Sakamoto, Y. Matsui, H. Kawashima, and Y. Yanagi, “Estimation of best corrected visual acuity from optical coherence tomography images using deep learning”, Association for Research in Vision and Ophthalmology (ARVO), 2019
  • Accepted as poster ARVO 2019: Lu Lu, M. Hook, F. Xu, S. Yousefi, J. Yue, and R. Williams, “Identification of genes and miRNA influential on early and late glaucoma pathogenesis in DBA/2J mice”, Association for Research in Vision and Ophthalmology (ARVO), 2019

Laboratory

 Data Mining and Machine Learning (DM2L) Laboratory

Members

  • Siamak Yousefi, Director
  • Mohammad Norouzifard, PhD student at AUT working joinlty at DM2L
  • Tarus Dukes, Undergraduate Student, Research Assistant
  • Golnoush-Sadat Mahmoudi-Nezhad, Medical Student, Research Assistant, Former Lab Member
  • Edward De Guzman, Former Lab Member 

Selected Publications

  1. S. Yousefi, "Promise of optical coherence tomography angiography in predicting glaucoma progression", JAMA Ophthalmology, 10.1001/jamaophthalmol.2019.0467, 2019.
  2. S. Yousefi, T. Elze, L. Pasquale, and M. Boland, “Glaucoma monitoring using manifold learning and unsupervised clustering”, 10.1109/IVCNZ.2018.8634733, IEEE Image and Vision Computing New Zealand (IVCNZ), 2018.
  3. M. Norouzifard, A. Nemati, H. GholamHosseini, R. Klette, K. Nouri-Mahdavi, and S. Yousefi, “Automated glaucoma diagnosis using deep and transfer learning: Proposal of a system for clinical testing”, IEEE Image and Vision Computing New Zealand (IVCNZ), 10.1109/IVCNZ.2018.8634671, 2018.
  4. S.Yousefi, E. Yousefi, H. Takahashi, T. Hayashi, H. Tampo, S. Inoda, Y. Arai, and P. Asbell, "Keratoconus severity identification using unsupervised machine learning", PLoS ONE, Special Issue on Machine Learning in Health and Biomedicine, 13(11), 2018.
  5. S. Yousefi, H. Sakai, H. Murata, Y. Fujino, D. Garway-Heath, R. Weinreb, and R. Asaoka, “Rates of visual field loss in primary open-angle glaucoma and primary angle-closure glaucoma: Asymmetric patterns”, Investigative Ophthalmology and Visual Science (IOVS), 59 (15), 5717-5725, 2018.
  6. S. Yousefi, T. Kiwaki, Y. Zheng, H. Suigara, R. Asaoka, H. Murata, H. Lemij, and K. Yamanishi, “Detection of longitudinal visual field progression in glaucoma using machine learning”, American Journal of Ophthalmology (AJO), vol. 193, pp. 71-79, 2018.
  7. S. Yousefi, G.S. Mahmoudi Nezhad, S. Pourahmad, K.A. Vermeer, and H.G. Lemij,“ Distribution and rates of visual field loss across different disease stages in primary open-angle glaucoma,” Ophthalmology Glaucoma, vol. 1, no. 1, pp. 52-60, 2018.
  8. H. Sugiura, T. Kiwaki, S. Yousefi, H. Murata, R. Asaoka, and K. Yamanishi, “Estimating Glaucomatous Visual Sensitivity from Retinal Thickness by Using Pattern-Based Regularization and Visualization”, Proceedings of the 24th ACM International Conference on Knowledge Discovery and Data Mining (KDD), pp. 783-792, 2018.
  9. S. Yousefi, H. Sakai, Murata H, Fujino Y, Garway-Heath D, Weinreb R, and Asaoka R, “Asymmetric Patterns of Visual Field Defect in Primary Open-Angle and Primary Angle-Closure Glaucoma,” Investigative Ophthalmology and Visual Science (IOVS), vol. 59, no. 3, pp. 1279-1287, 2018.
  10. S. Yousefi, M. Balasubramanian, M.H. Goldbaum, F.A. Medeiros, L.M. Zangwill, R.N. Weinreb, J.M. Liebmann, C.A. Girkin, and C. Bowd, “Unsupervised Gaussian mixture model with expectation maximization for detecting glaucomatous progression in standard automated perimetry visual fields,“ Translational Vision Science and Technology (TVST), vol. 5, no. 3, pp. 1-19, 2016.
  11. A. Yarmohammadi, L.M. Zangwill, A. Diniz-Filho, M. H. Suh, P. Isabel Manalastas, S. Yousefi, A. Belghith, L.J. Saunders, F.A. Medeiros, D. Huang, R.N. Weinreb, “Optical coherence tomography angiography vessel density in healthy, glaucoma suspect, and glaucoma eyes,” Investigative Ophthalmology & Visual Science (IOVS), vol. 57, Issue 9, pp. 451-459, 2016.
  12. M. Hee Suh, L.M. Zangwill, P. Isabel C Manalastas, A. Belghith, A. Yarmohammadi, F.A. Medeiros, A. Diniz-Filho, L.J. Saunders, S. Yousefi, R.N. Weinreb, “Optical Coherence Tomography Angiography Vessel Density in Glaucomatous Eyes with Focal Lamina Cribrosa Defects,” Journal of ophthalmology, 2016.
  13. A. Yarmohammadi, L.M. Zangwill, A. Diniz-Filho, M. Hee Suh, S. Yousefi, L.J. Saunders, A. Belghith, P. Isabel C Manalastas, F.A. Medeiros, R.N. Weinreb, “Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma,” Journal of Ophthalmology, 2016.
  14. S. Yousefi, M.H. Goldbaum, E.S. Varnousfaderani, A. Belghith, T-P. Jung, F.A. Medeiros, L.M. Zangwill, R.N. Weinreb, J.M. Liebmann, C.A. Girkin, and C. Bowd, “Detecting glaucomatous change in visual fields: Analysis with an optimization framework,” Journal of Biomedical Informatics (JBI), vol. 58, pp. 96-103, 2015.
  15. S. Yousefi, A. Wein, K. Kowalski, A. Richardson, and L. Srinivasan, “Smoothness as a failure mode of Bayesian mixture models in brain-machine interface,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 1, pp. 128-137, Jan. 2015.
  16. S. Yousefi, M.H. Goldbaum, M. Balasubramanian, F.A. Medeiros, L.M. Zangwill, J.M. Liebmann, C.A. Girkin, R.N. Weinreb, and C. Bowd, “Learning from data: Recognizing glaucomatous defect patterns and detecting progression from visual field measurements,” IEEE Transactions on Biomedical Engineering (TBME), vol. 61, no. 7, pp. 2112-2124, July 2014.
  17. R. Pourreza-Shahri, S. Yousefi, and N. Kehtarnavaz, “Optimization method to reduce blocking artifacts in JPEG images,” Journal of Electronic Imaging, vol. 23, no. 6, pp. 0630231-11, Nov. 2014.
  18. S. Yousefi, M.H. Goldbaum, M. Balasubramanian, T.P Jung, R.N. Weinreb, F.A. Medeiros, L.M. Zangwill, J.M. Liebmann, C.A. Girkin, and C. Bowd, “Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points,” IEEE Transactions on Biomedical Engineering (TBME), vol. 61, issue 4, pp. 1143-1154, 2014. (Featured Article)
  19. R. Pourreza-Shahri, S. Yousefi, and N. Kehtarnavaz, “A Gradient-based optimization approach for Reduction of blocking artifacts in JPEG compressed images,” Signal Processing: Image Communication, vol. 29, issue 10, pp. 1079-1091, 2014.
  20. C. Bowd, M.H. Goldbaum, M. Balasubramanian, I. Lee, G. Jang, S.Yousefi, L.M. Zangwill, C.A. Girkin, J.M. Liebmann, R.N. Weinreb, “Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified By Unsupervised Machine Learning Classifiers,” PLOS ONE, vol.9, issue 1, pp. 1-8, 2014.
  21. S. Yousefi, N. Kehtarnavaz, Y. Cao, “Computationally tractable stochastic image modeling based on symmetric Markov mesh random fields,” IEEE Transactions on Image Processing (TIP), vol. 22, no. 6, pp. 2192-2206, 2013.
  22. Q. R. Razlighi, N. Kehtarnavaz and S. Yousefi, “Evaluating similarity measures of brain image registration,” International Journal of Visual Communication and Image Representation (IJVCIR), vol. 24, no. 7, pp. 997-987, 2013.
  23. S. Yousefi, N. Kehtarnavaz, A. Gholipour, “Improved labeling of subcortical brain structures in atlas-based segmentation of magnetic resonance images,” IEEE Transactions on Biomedical Engineering (TBME), vol. 59, no. 7, pp. 1808-1817, 2012.
  24. S. Yousefi, N. Kehtarnavaz, Y. Cao, R. Razlighi, “Bilateral Markov Mesh Random Field and its Application to Image Restoration,” International Journal of Visual Communication and Image Representation (IJVCIR), vol. 23, pp. 1051-1059, 2012.
  25. S. Yousefi, B. Kim and N. Kehtarnavaz, “Automated pipeline to obtain porosity features from cervical tissue second harmonic generation images,” International Journal of Medical Implants and Devices, vol.5, no. 2, pp. 73-73, May 2011.
  26. S. Yousefi and N. Kehtarnavaz, Generating symmetric causal Markov random fields, IEE Electronics Letters, vol. 47, pp. 1224-1225, Oct. 2011.
  27. S. Yousefi, M. Rahman and N. Kehtarnavaz, “A new auto-focus sharpness function for digital and smart-phone cameras,” IEEE Transactions on Consumer Electronics (TCE), vol. 57, pp. 1003-1009,  2011.
  28. A. Gholipour, N. Kehtarnavaz, S. Yousefi, K. Gopinath, R. Briggs, “Symmetric deformable image registration via optimization of information theoretic measures,” Journal of Image and Vision Computing (JIVC), vol. 28, pp. 965-975, June 2010.
  29. M. H. Sedaaghi and S. Yousefi, “Morphological watermarking,” IEE Electronics Letters, vol.41, pp. 587-589, May 2005.