# Prof. Mihai Cucuringu

Associate Professor of Statistics, Department of Statistics

Lecturer, Merton College

Turing Fellow, The Alan Turing Institute

Senior Research Fellow, Institute for New Economic Thinking

University of Oxford

Andrew Wiles Building

Radcliffe Observatory Quarter

Woodstock Road

Oxford

OX2 6GG

Latest publications/preprints can be found on Google Scholar.

- 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” (2018), arxiv: 1803.03669. Conference version appeared at AISTATS 2017
- M. Cucuringu, R. Erban, "ADM-CLE approach for detecting slow variables in continuous time Markov chains and dynamic data", SIAM Journal on Scientific Computing, 39(1), B76-B101 (2017)
- 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, A. Singer, D. Cowburn, "Eigenvector Synchronization, Graph Rigidity and the Molecule Problem", Information and Inference: A Journal of the IMA, 1 (1), pp. 2167 (2012)
- 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)

Foundations of Data Science

Probability and Statistics for Network Analysis

Statistical Programming

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. During 2017-2018 I was a Turing Research Fellow within the Department of Statistics + Mathematical Institute at University of Oxford and The Alan Turing Institutein London. Throughout 2013-2016 I was a CAM Assistant Adjunct Professor in the Department of Mathematics at UCLA. I spent Fall 2014 as a Research Fellow at the Simons Institute for Theory of Computing at UC Berkeley, in the program Algorithmic Spectral Graph Theory, and Spring 2014 as a Research Fellow at ICERM, at Brown University, in the Network Science and Graph Algorithms program. During 2012-2013 I was Associate Quantitative Researcher (Quant) in the Statistical Arbitrage - Quantitative Trading Group, at Bank of America Merrill Lynch, New York.

Department of Statistics

University of Oxford

24-29 St Giles'

Oxford OX1 3LB

United Kingdom

• Alan Turing Institute Research Fellowship Grant (2017-2020)

• Princeton University Graduate School Fellowship (2007-2008)

• Princeton University Starr Fellowship (2007-2008)

machine learning; networks; data science; time series; financial applications

I am interested in the development and mathematical & statistical analysis of algorithms for data science, network analysis, and certain computationally-hard inverse problems on large graphs, with applications to 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 (SDP) relaxation algorithms and applications, group synchronization, ranking, clustering, phase unwrapping
- network analysis: community and core-periphery structure, network time series, anomaly detection
- 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