## Status:

Associate Professor of Statistics, Department of Statistics

Lecturer, Merton College

Turing Fellow, The Alan Turing Institute

Senior Research Fellow, Institute for New Economic Thinking

## Personal website:

## ORCID iD:

https://orcid.org/0000-0002-8464-2152## Research groups:

## Address

University of Oxford

Andrew Wiles Building

Radcliffe Observatory Quarter

Woodstock Road

Oxford

OX2 6GG

## Research interests:

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

## Preferred address:

Department of Statistics

University of Oxford

24-29 St Giles'

Oxford OX1 3LB

United Kingdom

## Further details:

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.

## Teaching:

Foundations of Data Science

Probability and Statistics for Network Analysis

Statistical Programming

## Prizes, awards, and scholarships:

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

• Princeton University Graduate School Fellowship (2007-2008)

• Princeton University Starr Fellowship (2007-2008)

## Major / recent publications:

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)