Mark A. Davenport
Professor and Associate Chair for Graduate Affairs
School of Electrical & Computer Engineering
Center for Signal and Information Processing
Center for Machine Learning
Georgia Institute of Technology
Contact information
CV || Biography || Academic genealogy
I am a Professor and the Associate Chair for Graduate Affairs in the School of Electrical & Computer Engineering at the Georgia Institute of Technology. I am a member of the Center for Machine Learning and the Center for Signal and Information Processing.
My research interests lie in the intersection of the fields of signal processing, high-dimensional statistics, and machine learning. I am particularly interested in the fundamental role that low-dimensional models and optimization play in these fields.
News
- Received the Richard M. Bass Outstanding Teacher Award in 2022
- Received the 2020 Outstanding Young Engineering Alumni Award from Rice University
- Santhosh Karnik's paper “Fast multitaper spectral estimation” wins Best Student Paper Award at SampTA 2019
- Received the 2019 Presidential Early Career Award for Scientists and Engineers (PECASE)
Selected recent papers
- A. D. McRae, J. Romberg, and M. A. Davenport, “Optimal convex lifted sparse phase retrieval and PCA with an atomic matrix norm regularizer,” to appear in IEEE Trans. on Information Theory, 2023. [arXiv]
- N. Ahad, M. A. Davenport, and Y. Xie, “Data-adaptive symmetric CUSUM for sequential change detection,” arxiv:2210.17353, October 2022.
- C. DeLude, R. S. Sharma, S. Karnik, C. Hood, M. A. Davenport, and J. Romberg, “Iterative broadband source localization” arxiv:2210.11669, October 2022.
- P. Guan, J. Jin, J. Romberg, and M. A. Davenport, “Loop unrolled shallow equilibrium regularizer (LUSER): A memory-efficient inverse problem solver” arxiv:2210.04987, October 2022.
- M. R. O'Shaughnessy, D. S. Schiff, L. R. Varshney, C. J. Rozell, and M. A. Davenport, “What governs attitudes toward artificial intelligence adoption and governance?” to appear in Science and Public Policy, 2023.
- S. Karnik, J. Romberg, and M. A. Davenport, “Thomson’s multitaper method revisited.” IEEE Trans. on Information Theory, 68(7), pp. 4864-4891, July 2022.
- C. DeLude, S. Karnik, M. A. Davenport, and J. Romberg, “Broadband beamforming via linear embedding,” arXiv:2206.07143, June 2022.
- A. D. McRae, S. Karnik, M. A. Davenport, and V. Muthukumar, “Harmless interpolation in regression and classification with structured features,” in Proc. Int. Conf. on Artificial Intelligence and Statistics (AISTATS), Online, March 2022.
- N. Ahad, E. L. Dyer, K. B. Hengen, Y. Xie, and M. A. Davenport, “Learning Sinkhorn divergences for supervised change point detection,” arXiv:2202.04000, February 2022.
- N. Nadagouda, A. Xu, and M. A. Davenport, “Active metric learning and classification using similarity queries,” arXiv:2202.01953, February 2022.