Associate Professor of Statistics - Fellow at The Alan Turing Institute
Lecturer at Merton College
I finished my Ph.D. in Applied and Computational Mathematics (PACM) at Princeton University in 2012. I joined the Department of Statistics in 2018, and have also been an affiliated faculty at the Mathematical Institute and the Institute for New Economic Thinking. Prior to this
- 2017-2018 Turing Research Fellow, The Alan Turing Institute in London, and Department of Statistics and Mathematical Institute, University of Oxford
- 2013-2016 CAM Assistant Adjunct Professor, Department of Mathematics, UCLA
- Fall 2014 Research Fellow, Simons Institute for Theory of Computing, UC Berkeley, in the program Algorithmic Spectral Graph Theory
- Spring 2014 Research Fellow, ICERM, Brown University, in the program Network Science and Graph Algorithms
- 2012-2013 Associate Quantitative Researcher, Statistical Arbitrage, Quantitative Trading Group, Bank of America Merrill Lynch, New York
I am interested in the development and mathematical & statistical analysis of algorithms that extract information from massive noisy data sets, network analysis, and certain computationally-hard inverse problems on large graphs. Applications include various problems in machine learning, statistics, finance, and engineering, often with an eye towards extracting structure from time-dependent data which can be subsequently leveraged for prediction purposes. More specifically, I have considered problems that span
- spectral and semidefinite programming relaxation algorithms and applications to ranking, clustering, group synchronization, phase unwrapping
- networks, community and core-periphery structure, network time series
- nonlinear dimensionality reduction and diffusion maps, intrinsic slow variables in dynamic data
- statistical analysis of big financial data, statistical arbitrage, market microstructure, limit order books, risk models
- low-rank matrix completion, distance geometry problems, rigidity theory, sensor network localization and 3D structuring of molecules
- M. Cucuringu, H. Li, H. Sun, L, Zanetti, “Hermitian matrices for clustering directed graphs: insights and applications”, AISTATS 2020.
- M. Cucuringu, P. Davies, A. Glielmo, H.Tyagi, “SPONGE: A generalized eigenproblem for clustering signed networks”, AISTATS 2019.
- M. Cucuringu, H. Tyagi, “Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping”, Journal of Machine Learning Research 2020. Conference version appeared at AISTATS 2018.
- M. Cucuringu, M. P. Rombach, S. H. Lee, M. A. Porter, “Detection of Core-Periphery Structure in Networks Using Spectral Methods and Geodesic Paths”, European Journal of Applied Mathematics, Vol. 27, No. 6: 846-887 (2016).
- M. Cucuringu, “Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and Semidefinite Programming Synchronization”, IEEE Transactions on Network Science and Engineering, 3 (1): 58–79 (2016).
- M. Cucuringu, Y. Lipman, A. Singer, “Sensor Network Localization by Eigenvector Synchronization over the Euclidean Group”, ACM Transactions on Sensor Networks, 8(3), pp. 1-42 (2012).
- A. Singer, M. Cucuringu, “Uniqueness of Low-Rank Matrix Completion by Rigidity Theory”, SIAM Journal on Matrix Analysis and Applications, 31 (4), pp. 1621-1641 (2010).