Research

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JOURNAL

  1. Multichannel Sleep Spindle Detection using Sparse Low-Rank Optimization. (ScienceDirect) (Preprint) (bioRxiv) (Code
  2. A. Parekh, I. W. Selesnick, R. S. Osorio, A. W. Varga, D. M. Rapaport and I. Ayappa.  
    Journal of Neuroscience Methods, 288:1-16, Aug. 2017.

  3. Improved Sparse Low-Rank Matrix Estimation. (ScienceDirect) (arXiv) (Preprint) (Code)
  4. A. Parekh and I. W. Selesnick.
    Signal Processing, 139:62-69, Oct. 2017. 

  5. Reduced Slow-Wave Sleep is Associated with High Cerebrospinal Fluid Aβ42 Levels in Cognitively Normal Elderly (SLEEP) (Oxford Academic)
  6. A.W. Varga, M. E. Wohlleber, S. Gimenez, S. Romero, J.F. Alonso, E.L. Ducca, K. Kam, C. Lewis, E. Tanzi, S. Tweardy, A. Kishi, A. Parekh, E. Fischer, T. Gumb, D. Alcolea, J. Fortea, A. Lleo, K. Blennow, H. Zetterberg, L. Mosconi, L. Glodzik, E. Pirraglia, O. Burschtin, M. de Leon, D.M. Rapoport, S-en. Lu, I. Ayappa and R.S. Osorio.  
    Journal of Sleep and Sleep Disorders Research, 39(11):2041-2048, Nov. 2016. 

  7. Effects of Aging on Slow Wave Sleep Dynamics and Human Spatial Navigational Memory Consolidation. (ScienceDirect)
  8. A. W. Varga, E. Ducca, A. Kishi, E. Fischer, A. Parekh, V. Koushyk, P. Yau, T. Gumb, D. Leibert, M. Wohlleber, O. Burschtin, A. Convit, D. M. Rapoport, R. Osorio and I. Ayappa.
    Neurobiology of Aging, 42:142-149, Jun. 2016. 


  9. Enhanced Low-Rank Matrix Approximation. (IEEE) (arXiv) (Code) (Poster)
    A. Parekh and I. W. Selesnick. 

    IEEE
    Signal Processing Letters, 23(4):493-497, Apr. 2016. 

  10. Convex Denoising Using Non-convex Tight Frame Regularization. (IEEE) (Preprint) (arXiv) (Code) (Poster)
  11. A. Parekh and I. W. Selesnick.
    IEEE Signal Processing Letters, 22(10): 1786-1790, Oct. 2015. 

  12. Detection of K-complexes and Sleep Spindles (DETOKS) using Sparse Optimization. (Preprint) (ScienceDirect) (Code)
  13. A. Parekh, I. W. Selesnick, D. M. Rapoport, and I. Ayappa.
    Journal of Neuroscience Methods, 251:37-46, Aug. 2015.

  14. Convex 1-D Total Variation Denoising with Non-convex Regularization. (IEEE) (Code)
  15. I. W. Selesnick, A. Parekh, and I. Bayram.
    IEEE Signal Processing Letters, 22(2):141-144, Feb. 2015. 

CONFERENCE

  1. Reduced Spindle Frequency and Density in Stage 2 NREM Sleep is Associated with Increased CSF P-tau in Cognitively Normal Elderly. (Oxford Academic
    R. A. Sharma, K. Kam, A. Parekh, S. Uribe-Cano, S. Tweardy, O.M. Bubu, I. Ayappa, D.M. Rapaport, A.W. Varga, R.S. Osorio
    Journal of Sleep and Sleep Disorders Research, 40(suppl_1):A281, Apr. 2017. 

  2. Free Breathing & Ungated Cardiac Cine MRI using Joint Smoothness Regularization on Image and Patch Manifolds. 
    A. Parekh, S. Poddar, X. Bi, D. Wang and M. Jacob. 
    Proceedings of ISMRM, Hawaii, USA, Apr. 2017. 

  3. Bird Body and Wing-Beat Radar Doppler Signature Separation using Sparse Optimization. (IEEE)
  4. M. Farshchian, I. Selesnick and A. Parekh.
    Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Sep. 2016.

  5. Reduced Spindle Frequency is Associated with Increased Cerebrospinal P-Tau in Cognitively Normal Elderly. (Poster
    M. Wohlleber, A. Parekh, T. Gumb, A.W. Varga, and R. Osorio. 
    International Conference on Sleep Spindling, Budapest, Hungary, May 2016.

  6. Convex Fused Lasso Denoising with Non-Convex Regularization and its use for Pulse Detection. (IEEE) (Preprint) (arXiv) (Code)
  7. A. Parekh and I. W. Selesnick. 
    IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Dec. 2015. 

  8. CSF Aβ42 Levels May Increase Due to Age-Dependent Slow-Wave Sleep Loss Prior to Amyloid Deposition in Humans. (Alzheimer's & Dementia)
    R.S. Osorio, M. Wohlleber, S. Giménez, S. Romero, E.L. Ducca, T. Gumb, A. Parekh, A. Varga, O. Burschtin, I. Ayappa, D.M. Rapoport, M. de Leon. 
    Alzheimer's & Dementia: The Journal of Alzheimer's Association, 11(7): P848, Aug. 2015.

  9. Too Relaxed? Tightly Relaxed Non-Convex Sparse Regularization (Project Page) (Extended Abstract)

    A. Parekh 
    and I. W. Selesnick. 
    Proceedings of Signal Processing with Adaptive Sparse Structured Representations (SPARS), Jul. 2015
     

  10. Reduced Slow-Wave Sleep is Associated with Increased Cerebrospinal Fluid AB42 levels prior to Amyloid Deposition in Humans (SLEEP)
    R.S. Osorio, M. Wohlleber, E.L. Ducca, T. Gumb, A. Parekh, K. Barclay, A.W. Varga, O. Burschtin, I. Ayappa, D.M. Rapaport and M. de Leon
    Journal of Sleep and Sleep Disorders Research, 38:A403, Jun. 2015.

  11. Effects of Aging on Slow Wave Sleep Disruption and Reduced Overnight Consolidation of Spatial Navigational Memory. (SLEEP)
  12. A. W. Varga, E. Ducca, D. Leibert, J. Lim, A. Kishi, V. Koushyk, T. Gumb, A. Parekh, R. Osorio, O. Burschtin, D. M. Rapoport, and I. Ayappa
    Journal of Sleep and Sleep Disorders Research, 38:A95, Jun. 2015. 

  13. Sleep Spindle Detection using Time-Frequency Sparsity. (IEEE) (Preprint) (Project Page) (Code)
  14. A. Parekh, I. W. Selesnick, D. M. Rapoport, and I. Ayappa. 
    IEEE 
    Signal Processing in Medicine and Biology Symposium (SPMB), Dec. 2014.

THESIS
  1. Bypassing the Limits of L1 Regularization: Convex Sparse Signal Processing using Non-Convex Regularization. (PDF) (Slides)
  2. A. Parekh. 
    Dept. of Mathematics, New York University, Sep. 2016. 

TALKS

  1. Bypassing the limits of L1 regularization: Convex non-convex optimization for signal processing. 16th New Mexico Analysis Seminar, Las Cruces, New Mexico, May 2017.
  2. Improved Sparse Low-Rank Matrix Estimation. February Fourier Talks, University of Maryland, Feb. 2017. (poster)
  3. Enhanced Sparse Low-Rank Matrix Approximation. SIAM Conference on Imaging Science, Albuquerque, New Mexico, May 2016
  4. Enhanced Low-Rank Matrix Approximation. SIAM Conference on Imaging Science, Albuquerque, New Mexico, May 2016. (poster). 
  5. Enhanced Low-Rank Matrix Approximation. February Fourier Talks, University of Maryland, Feb. 2016. (poster)
  6. Convex Fused Lasso Denoising with Non-Convex Regularization and its use for Pulse Detection. Temple University, Philadelphia, Dec. 2015.
  7. Convex Low-Rank Matrix Estimation using Non-convex Regularization. The Graduate Center, CUNY, New York, Sep. 2015. 
  8. Too relaxed? Tightly regularized non-convex sparse regularization. International Symposium on Optimization (ISMP), Pittsburgh, Jul. 2015.  
  9. Sparse Signal Estimation using Tightly-Relaxed Non-convex Regularization. The Graduate Center, CUNY, New York, Mar. 2015 
  10. Too relaxed? Tightly regularized non-convex sparse regularization. February Fourier Talks, University of Maryland, Feb. 2015. (poster)
  11. Sparse signal separation and estimation using convex optimization. Indian Institute of Technology Bombay, India. Jan. 2015. 
  12. Sleep spindle detection using time-frequency sparsity. Temple University, Philadelphia,  Dec. 2014.