Publications

My Google Scholar Page.

Theses

    Ph.D. Thesis: Semi-Supervised Kernel Learning for Pattern Classification, (Superviser: Prof. Hamid R. Rabiee).
    M.Sc. Thesis: Face Recognition in Low Quality Video (Supervisor: Prof. Hamid R. Rabiee).
    B.Sc. Project: Focused Crawling on Web, (Supervisor: Prof. Hassan Abolhassani).

Published Journal Papers

  1. M. H. Rohban, H. S. Abbasi, S. Singh, A. E. Carpenter “Capturing single-cell heterogeneity via data fusion improves image-based profiling,” Nature Communications, 2019.
  2. M. H. Rohban, S. Singh, X. Wu, J. B. Berthet, M. A. Bray, Y. Shrestha, X. Varelas, J. S. Boehm, A. E. Carpenter, “Systematic morphological profiling of human gene and allele function via Cell Painting”, eLife, 2017.
  3. M. A. Bray, S. M. Gustafsdottir, M. H. Rohban, S. Singh, V. Ljosa, K. L. Sokolnicki, J. A. Bittker, N. E. Bodycombe, V. Dank, T. P. Hasaka, C. S. Hon, M. M. Kemp, K. Li, D. Walpita, M. J. Wawer, T. R. Golub, S. L. Schreiber, P. A. Clemons, A. F. Shamji, A. E. Carpenter, “A dataset of images and morphological profiles of 30,000 small-molecule treatments using the Cell Painting assay,” GigaScience, 2017.
  4. J. C. Caicedo, S. Cooper, F. Heigwer, S. Warchal, P. Qiu, C. Molnar, A. S. Vasilevich, J. D. Barry, H. S. Bansal, O. Kraus, M. Wawer, L. Paavolainen, M. D. Herrmann, M. H. Rohban, J. Hung, H. Hennig, J. Concannon, I. Smith, P. A. Clemons, S. Singh, P. Rees, P. Horvath, R. G. Linington, A. E. Carpenter, “Data-analysis strategies for image-based cell profiling,” Nature Methods, 2017.
  5. M. H. Rohban, D. Motamedvaziri, V. Saligrama, “Sparse Signal Recovery under Poisson Statistics,” IEEE Transactions on Signal Processing, 2016.
  6. H. Asheri, H. R. Rabiee, M. H. Rohban, “Signal Extrapolation for Image and Video Error Concealment Using Gaussian Processes With Adaptive Nonstationary Kernels,” IEEE Signal Processing Letters, 19(10): 700-703, 2012.
  7. N. Pourdamghani, H. R. Rabiee, F. Faghri, M. H. Rohban, “Graph Based Semi- Supervised Human Pose Estimation : When The Output Space Comes to Help,” Pattern Recognition Letters, Vol. 33, Issue 12, P.P. 1529-1535, 2012.
  8. M. H. Rohban, H. R. Rabiee, “Supervised Neighborhood Graph Construction for Semi-Supervised Classification,” Pattern Recognition, Vol. 45, Issue 4, P.P. 1363-1372, 2012.

Published Conference Papers

  1. D. Motamedvaziri, V. Saligrama, M. H. Rohban, “Sparse Signal Recovery under Poisson Statistics for Online Marketing Applications,” International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2014.
  2. W. Ding, M. H. Rohban, P. Ishwar, V. Saligrama, “Efficient Distributed Topic Modeling with Provable Guarantees,” 17th International Conference on Artificial Intelligence and Statistics (AISTATS), 2014.
  3. W. Ding, P. Ishwar, M. H. Rohban, V. Saligrama, “Necessary and Sufficient Conditions for Novel Word Detection in Separable Topic Models,” NIPS Workshop on Topic Models, 2013.
  4. W. Ding, M. H. Rohban, P. Ishwar, V. Saligrama, “Topic Discovery through Data Dependent and Random Projections,” International Conference on Machine Learning (ICML), 2013.
  5. M. H. Rohban, P. Ishwar, B. Orten, W. C. Karl, V. Saligrama, “An Impossibility Result for High Dimensional Supervised Learning,” IEEE Information Theory Workshop (ITW), 2013.
  6. W. Ding, M. H. Rohban, P. Ishwar, V. Saligrama, “A New Geometric Approach to Latent Topic Modeling and Discovery,” International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013.
  7. D. Motamedvaziri, M. H. Rohban, V. Saligrama, “Sparse Recovery under Poisson Statistics,” 51st Allerton Conference on Communications, Control, and Computing , 2013.
  8. H. S. Ayatollahi Tabatabaii, H. R. Rabiee, M. H. Rohban, M. Salehi, “Incorporating Betweenness Centrality in Compressive Sensing for Congestion Detection,” International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013.
  9. A. Ghasemi, H. R. Rabiee, M. T. Manzuri, M. H. Rohban, “A Bayesian Approach to the Data Description Problem,” 26th International Conference on Artificial Intelligence (AAAI), 2012.
  10. A. Ghasemi, H. R. Rabiee, M. Fadaee, M. T. Manzuri, M. H. Rohban, “Active Learning from Positive and Unlabeled Data,” ICDM Workshop on Optimization Based Methods for Emerging Data Mining Problems (OEDM), 2011.
  11. A. Ghasemi, M. T. Manzuri, H. R. Rabiee, M. H. Rohban, S. Haghiri, “Active One-Class Learning by Kernel Density Estimation,” IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2011.
  12. M. Farajtabar, A. Shaban, H. R. Rabiee, M. H. Rohban, “Manifold Coarse Graining for Online Semi-Supervised Learning,” The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2011.
  13. M. Ghazvininejad, M. Mahdieh, H. R. Rabiee, P. K. Roshan, M. H. Rohban, “Iso-graph : Neighborhood Graph Construction Based on Geodesic Distance for Semi-Supervised Learning,” IEEE International Conference on Data Mining (ICDM), 2011.
  14. S. Khaleghian, H. R. Rabiee, and M. H. Rohban, “Face Recognition across Large Pose Variations via Boosted Tied Factor Analysis,” IEEE Workshop on Applications of Computer Vision (WACV), 2011.
  15. H. Asheri, A. Bayati, H. R. Rabiee, and M. H. Rohban, “Motion Vector Recovery with Gaussian Process Regression,” International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2011.
  16. H. Asheri, H. R. Rabiee, N. Pourdamghani, and M. H. Rohban, “A Gaussian Process Regression Framework for Spatial Error Concealment with Adaptive Kernels,” International Conference on Pattern Recognition (ICPR), 2010.
  17. M. H. Rohban, H. R. Rabiee, and A. Vahdat, “Face Virtual Pose Generation using Aligned Locally Linear Regression for Face Recognition,” IEEE International Conference on Image Processing (ICIP), Egypt, 2009.
  18. M. H. Rohban, H. R. Rabiee, and M. Khansari, “Face Virtual Pose Generation using Multi Resolution Subspaces,” International Symposium on Telecommunication (IST), Iran, 2008.

Posters and Talks

  1. Functional annotation of human genes and alleles using image-based profiling, EMBL Conference, From Functional Genomics to System Biology, Heidelberg, Germany, 2018 (Poster).
  2. Functionally characterizing genes and alleles by morphological profiling using Cell Painting Assay, Allen Institute for Cell Sciences, Seattle WA, 2016.
  3. Sparse Signal Recovery under Poisson Statistics, UP-STAT, Buffalo NY, 2016.
  4. Provable Efficient Topic Modeling under Separability Assumption, Invited Talk, University of California Irvine, 2014, Host : Prof. Anima Anandkumar.