My Google Scholar Profile

Ph.D. Thesis

• S. Li, “Optimization for high-dimensional analysis and estimation in signal processing and machine learning,” Ph.D. thesis, Colorado School of Mines, May 2020.

Preprints

• R. Grotheer, S. Li, A. Ma, D. Needell, J. Qin, “Iterative singular tube hard thresholding algorithms for tensor completion,” preprint, 2023.

• S. Li and M. Wakin, “Recovery guarantees for time-varying pairwise comparison matrices with non-transitivity,” preprint, 2021.

• S. Li, P. Nayeri, and M. Wakin, “Digital beamforming robust to time-varying carrier frequency offset,” preprint, 2021. (code)

Journal Papers

• S. Li, G. Tang, and M. B. Wakin, “Landscape correspondence of empirical and population risks in the eigendecomposition problem,” IEEE Transactions on Signal Processing, vol. 70, pp. 2985–2999, 2022. (authors’ copy)

• R. Grotheer, S. Li, A. Ma, D. Needell, J. Qin, “Iterative hard thresholding for low CP-rank tensor models,” Linear and Multilinear Algebra, pp. 1–17, 2021. (authors’ copy, code)

• S. Li, D. Gaydos, P. Nayeri, and M. Wakin, “Adaptive interference cancellation using atomic norm minimization and denoising,” IEEE Antennas and Wireless Propagation Letters, vol. 19, no. 12, pp. 2349–2353, 2020.

• S. Li, H. Mansour, and M. B. Wakin, “Recovery analysis of damped spectrally sparse signals and its relation to MUSIC,” Information and Inference: A Journal of the IMA, 2020. (authors’ copy, code)

• J. Qin, S. Li, D. Needell, A. Ma, R. Grotheer, C. Huang, and N. Durgin, “Stochastic greedy algorithms for multiple measurement vectors,” Inverse Problems and Imaging, vol. 15, no. 1, pp. 79–107, 2020.

• S. Li, Q. Li, Z. Zhu, G. Tang, and M. B. Wakin, “The global geometry of centralized and distributed low-rank matrix recovery without regularization,” IEEE Signal Processing Letters, vol. 27, pp. 1400–1404, 2020. (authors’ copy)

• S. Li, M. B. Wakin, and G. Tang, “Atomic norm denoising for complex exponentials with unknown waveform modulations,” IEEE Transactions on Information Theory, vol. 66, no. 6, pp. 3893–3913, 2020. (authors’ copy)

• S. Li, D. Yang, G. Tang and M. B. Wakin, “Atomic norm minimization for modal analysis from random and compressed samples,” IEEE Transactions on Signal Processing, vol. 66, no. 7, pp. 1817–1831, 2018. (authors’ copy, code)

Conference/Workshop Papers – Machine Learning

• S. Li, W. Swartworth, M. Takac, D. Needell, and R. M. Gower, “SP2: A second order stochastic Polyak method,” to appear in The Eleventh International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023.

• S. Li and Q. Li, “Local and global convergence of general Burer-Monteiro tensor optimizations,” The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), vol. 36, no. 9, pp. 10266-10274, Vancouver, Canada, February 2022.

• S. Li, G. Tang, and M. B. Wakin, “The landscape of non-convex empirical risk with degenerate population risk,” The Thirty-third Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019. (authors’ copy, code)

• S. Li, Q. Li, G. Tang, and M. B. Wakin, “Geometry correspondence between empirical and population games,” The Bridging Game Theory and Deep Learning Workshop NeurIPS 2019 (Smooth Games Optimization and Machine Learning Series), Vancouver, Canada, December 2019.

• S. Li, Y. Xie, Q. Li, and G. Tang, “Cubic regularization for differentiable games,” The Bridging Game Theory and Deep Learning Workshop NeurIPS 2019 (Smooth Games Optimization and Machine Learning Series), Vancouver, Canada, December 2019.

Conference/Workshop Papers – Signal Processing

• R. Grotheer, S. Li, A. Ma, D. Needell, J. Qin, “Stochastic natural thresholding algorithms,” to appear in The 57th Asilomar Conference on Signals, Systems and Computers (ACSSC), California, USA, October 2023.

• S. Li, D. Needell, and W. Swartworth, “An untrained one-layer convolutional network-based method for line spectral estimation,” The 55th Asilomar Conference on Signals, Systems and Computers (ACSSC), California, USA, November 2021.

• S. Li, S. Becker, and M. Wakin, “Nuclear norm based spectrum estimation for molecular dynamic simulations,” The 54th Asilomar Conference on Signals, Systems and Computers (ACSSC), California, USA, November 2020.

• S. Li, D. Gaydos, P. Nayeri, and M. Wakin, “Adaptive interference cancellation using atomic norm minimization,” 2020 International Applied Computational Electromagnetics Society (ACES) Symposium, Monterey, California, USA, March 2020.

• R. Grotheer, S. Li, A. Ma, D. Needell, J. Qin, “Stochastic iterative hard thresholding for low-Tucker-rank tensor recovery,” Proc. Information Theory and Applications, La Jolla, California, February 2020. (authors’ copy, code)

• N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin, “Jointly sparse signal recovery with prior info,” The 53rd Asilomar Conference on Signals, Systems and Computers (ACSSC), California, USA, November 2019.

• N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin, “Fast hyperspectral diffuse optical imaging method with joint sparsity,” The 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, July 2019.

• S. Li, G. Tang and M. B. Wakin, ‘‘Simultaneous blind deconvolution and phase retrieval with tensor iterative hard thresholding," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 2019.

• N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin, “Sparse randomized kaczmarz for support recovery of jointly sparse corrupted multiple measurement vectors,” Research in Data Science, Proc. WiSDM (ICERM), Providence, RI, USA, 2018.

• N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin, “Compressed anomaly detection with multiple mixed observations,” Research in Data Science, Proc. WiSDM (ICERM), Providence, RI, USA, 2018. (code)

• Y. Xie, S. Li, G. Tang, and M. B. Wakin, “Radar signal demixing via convex optimization,” The 22nd International Conference on Digital Signal Processing (DSP), London, UK, August 2017. (authors’ copy)

• S. Li, D. Yang and M. B. Wakin, “Atomic norm minimization for modal analysis with random spatial compression,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, March 2017.

• Q. Li, S. Li, Hassan Mansour, Michael B. Wakin, Dehui Yang and Z. Zhu, “Jazz: A companion to MUSIC for frequency estimation with missing data,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, March 2017.

• S. Li, D. Yang and M. B. Wakin, “Atomic norm minimization for modal analysis,” IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Seattle, WA, USA, July 2016.

• Q. Li, S. Li, H. Bai, X. Li and L. Chang, “Joint rank and positive semidefinite constrained optimization for projection matrix,” IEEE International Conference on Industrial Engineering Applications (ICIEA), Hangzhou, China, June 2014.

• S. Li, Q. Li, G. Li, X. He and L. Chang, “Iteratively reweighted least squares for block-sparse recovery,” IEEE International Conference on Industrial Engineering Applications (ICIEA), Hangzhou, China, June 2014.

• S. Li, Q. Li, G. Li, L. Chang and X. He, “Simultaneous sensing matrix and sparsifying dictionary optimization for block-sparse compressive sensing,” IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (ICMASS), Hangzhou, China, December 2013.

• Q. Li, Z. Zhu, G. Li, L. Chang and S. Li, “Robust projection matrix optimization from the MSE view for compressive sensing systems,” IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Kunming, China, August 2013.

• S. Li, Z. Zhu, G. Li, L. Chang and Q. Li, “Projection matrix optimization for block-sparse compressive sensing,” IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Kunming, China, August 2013.