Office: EEB 430
Email: soltanol@usc.edu
Tel: 213-740-4456
I am an associate professor in the Ming Hsieh Department of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engeineering (ISE) at the University of Southern California where I hold an Andrew and Erna Viterbi Early Career Chair. I am also the inaugural director of the USC Center on AI Foundations for Science (AIF4S). Prior to joining USC I spent a year as a postdoc in the AMPLAB at UC Berkeley mentored by Ben Recht and Martin Wainwright. I obtained my Ph.D. in Electrical Engineering from Stanford in 2014 advised by Emmanuel Candes.
My research focuses on developing the mathematical foundations of learning from signals and data spanning optimization, machine learning, signal processing, high dimensional probability/statistics, computational imaging and artificial intelligence. Over the last few years, I've been developing and analyzing algorithms for non-convex optimization with provable guarantees of convergence to generalizable global optima including those arising in deep learning. A particular focus on the application side has been on developing AI for scientific applications in areas such as computational imaging, medical imaging and wireless systems. Here is a copy of my CV.
On the weekends you're most likely to find me cycling on the Pacific coast ranging from Malibu/Santa Monica mountains to Palos Verdes.
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All publications unless specified are based on alphabetic ordering of the names.
- Phase retrieval via Wirtinger Flow: Theory and Algorithms. (website)
E. J. Candes, X. Li, and M. Soltanolkotabi. - Phase retrieval from coded diffraction patterns. (website)
E. J. Candes, X. Li, and M. Soltanolkotabi. - Robust Subspace Clustering. (website)
M. Soltanolkotabi, E. Elhamifar, and E. J. Candes. To appear in Annals of Statistics. - A Geometric Analysis of Subspace Clustering with Outliers. (code)
M. Soltanolkotabi and E. J. Candes. Annals of Statistics 40(4), 2195--2238, 2012. - Discussion of "Latent Variable Graphical Model Selection via Convex Optimization".
E. J. Candes and M. Soltanolkotabi. Annals of Statistics 40(2), 1997--2004, 2012. - A Unified Approach to Sparse Signal Processing.(during undergrad)
F. Marvasti, A. Amini, F. Haddadi, M. Soltanolkotabi, B. Khalaj, A. Aldroubi, S. Sanei and J. Chambers. EURASIP Journal on Advances in Signal Processing. - From robust subspace clustering to full-rank matrix completion.(Extended abstract-complete version will be available soon as part of a review article)
E. J. Candes, L. Mackey, and M. Soltanolkotabi. - Errorless Codes for Over-loaded Wireless CDMA with Active User Detection.
P. Pad, M. Soltanolkotabi, S. Hadikhanlou, A. Enayati and F. Marvasti. Proc. of the International Conference on Communications, ICC 2009, Dresden, Germany. - OFDM Channel Estimation based on Adaptive Thresholding for Sparse Signal Detection.
M. Soltanolkotabi, A. Amini and F. Marvasti. Proc. of European Signal Processing Conference, EUSIPCO 2009. - Throughput Capacity of a Multi-channel Multi- hop Mobile Ad-hoc Network.
M. Soltanolkotabi and F. Ashtiani. Proc. of International Conference on Telecommunications, ICT 2009. - A Practical Sparse Channel Estimation for Current OFDM Standards.
M. Soltanolkotabi, M. Soltanalian, A. Amini and F. Marvasti. Proc. of International Conference on Telecommunications, ICT 2009. - Salt and Pepper Noise Removal for Images.
S. Feizi-Khankandi, S. Zahed Pour, M. Soltanolkotabi, A. Amini and F. Marvasti. Proc. of International Conference on Telecommunications, ICT 2008.
Publications
Journals
Preprints
Conferences (during undergrad)
- Robust Subspace Clustering.
- Stanford Biostatistics Seminar, February 2014.
- ICML Spectral Learning Workshop, June 2013.
- Asilomar Conference on Signals, Systems, and Computers.
- MURI annual meeting, Princeton, October 2012.
- Information Theory and Applications workshop, Feb. 2013. - A Geometric Analysis of Subspace Clustering with Outliers.
- Berkeley Robotics Lab, Febuary 2012.
- High-Dimensional Phenomena in Statistics and Machine Learning Seminar, Georgia Tech., July 2012.
- Workshop on Modern Massive Data Sets (MMDS), Stanford, July 2012.
Talks
Instructor:
EE 364a: Convex Optimization, Summer 2011.TA:
Stanford CS:- CS 229: Machine Learning, Fall 2012.
Stanford EE:
- EE 278: Statistical Signal Processing, Summer 2010.
Stanford Math:
- Math 104: Linear Algebra, Winter 2012.
Stanford Statistics:
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I am currently collaborating with Nava College Prepatory Academy
a school in Compton to help improve K-12 math/STEM education. I will be posting more details about
these activities including curriculum development, lecture notes, excersize/puzzles, data modules etc. on this website (please check back for updates).
I have also partnered with VAST: Viterbi Adopt-a-School Adopt-a-Teacher (VAST) and STEM educational outreach programs at USC Viterbi School of Engineering to engage in various educational outreach activities in local middle/high schools close to USC e.g. see VAST's reporting on one such activity here and stay tunned for more updates.