Mihai Cucuringu

Associate Professor

Department of Statistics
Mathematical Institute
University of Oxford

Non-Tutorial Fellow
Merton College

Turing Fellow
The Alan Turing Institute

           

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I am an Associate Professor in the Department of Statistics, and an Affiliate Faculty in the Mathematical Institute at University of Oxford. I am also a Non-Tutorial Fellow (Stipendiary Lecturer in Statistics) at Merton College, University of Oxford, a Turing Fellow at The Alan Turing Institute in London, and a Senior Research Fellow at the Institute for New Economic Thinking at the Oxford Martin School.

Here is my Google Scholar page.

I co-organize the TADS Seminar (Theory and Algorithms in Data Science), hosted at the Alan Turing Institute. Please let me know if you would like to give a talk.

If you are interested in a DPhil in Statistics/Maths at Oxford, feel free to email me to discuss this. See also the two newly announced EPSRC CDTs: StatML-Modern Statistics and Statistical Machine Learning and Mathematics of Random Systems: Analysis, Models and Algorithms (info).

I finished my Ph.D in Applied and Computational Mathematics (PACM) at Princeton University in 2012, where I was extremely fortunate to be advised by Amit Singer. My thesis was on the low-rank matrix completion problem and several distance geometry problems with applications to sensor network localization and three-dimensional structuring of molecules.

During 2017-2018 I was a Turing Research Fellow within the Department of Statistics + Mathematical Institute at University of Oxford and The Alan Turing Institute in London. Throughout 2013-2016 I was a CAM Assistant Adjunct Professor in the Department of Mathematics at UCLA, hosted by Andrea Bertozzi. 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.


Research interests

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


Ongoing projects With funding
  • network analysis for fraud detection (Turing project info); Accenture and Turing alliance for Data Science (Andrew Elliott, PDRA at Turing, Aug. 2017-)
  • news sentiment analysis: propagation of information in financial networks and predictive graph analytics (Turing project info) & clustering in signed networks (Andrea Pizzoferrato, PDRA at Turing & Imperial College, Nov. 2017-May 2019)
  • network correlation structure in multivariate time series (Luis Ospina, PDRA at Turing, Oct 2017-May 2018)


Education

  • 2009 - 2012: PhD, Applied and Computational Mathematics (PACM), Princeton University
  • 2007 - 2009: Master of Arts, Applied and Computational Mathematics (PACM), Princeton University
  • 2003 - 2007: Bachelor of Arts, Hiram College, OH (Summa Cum Laude)
    • B.A in Mathematics, B.A in Computer Science, B.A in Economics
  • Fall 2005: Budapest Semester in Mathematics, Budapest, Hungary
  • 1999 -2003: "Gheorghe Munteanu Murgoci" High School, Braila, Romania 


Teaching
  • University of Oxford:
    • Probability and Statistics for Network Analysis, Department of Statistics (joint with Gesine Reinert) (2017, 2018)
  • UCLA:
    • Instructor: Ordinary Differential Equations with Linear Algebra for Life Sciences Students, MATH 3C, Department of Mathematics (Spring 2016)
    • Instructor: Topics in Data Science: Algorithms and Mathematical Foundations, MATH 191, Department of Mathematics (course description) (syllabus) (Fall 2015)
    • Instructor: Graphs and Networks, MATH 191, Department of Mathematics (course description) (syllabus) (Winter 2015)
    • Instructor: Mathematics of Finance, MATH 174E, Department of Mathematics (syllabus) (Spring 2014)
    • Instructor: Probability for Life Sciences Students, MATH 3C, Department of Mathematics (syllabus) (Fall 2013)
  • Princeton:
    • Instructor: Game Theory, MAT 308 / ECO 318, Departments of Mathematics and Economics (syllabus) (Spring 2011)
    • Teaching assistant: Graph Theory, MAT/COS 306 (Prof. Paul Seymour, Spring 2009)
    • Teaching assistant: Combinatorics, MAT 307 (Prof. Jan Vondrak, Spring 2008)

Publications and preprints

  1. A. d'Aspremont, M. Cucuringu, H. Tyagi, Ranking and synchronization from pairwise measurements via SVD, (arXiv) (2019)
  2. M. Cucuringu, A. Pizzoferrato, Y. van Gennip, An MBO scheme for clustering and semi-supervised clustering of signed networks, (arXiv) (2019)
  3. A. Elliott, M. Cucuringu, M. M. Luaces, P. Reidy, and G. Reinert, Anomaly detection in networks with application to financial transaction networks, (arXiv) (2018)
  4. M. Cucuringu, P. Davies, A. Glielmo, H. Tyagi, "SPONGE: A generalized eigenproblem for clustering signed networks", [code], AISTATS 2019
  5. M. Cucuringu, H. Tyagi, "Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping", (arXiv), [code], (2018) accepted to Journal of Machine Learning Research (JMLR)
  6. A. Tsokos, S. Narayanan, I. Kosmidis, G. Baio, M. Cucuringu, G. Whitaker and F. J. Király, "Modeling outcomes of soccer matches", (arXiv), Machine Learning 2018
  7. M. Cucuringu, H. Tyagi, "On denoising modulo 1 samples of a function", (arXiv), [code], AISTATS 2018
  8. 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)
  9. M. Cucuringu, C. Marshak, D. Montag, P. Rombach, "Rank Aggregation for Course Sequence Discovery", Complex Networks (2017)
  10. M. Cucuringu, "Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP Synchronization", IEEE Transactions on Network Science and Engineering, 3 (1): 58-79, (2016). Compact version here.
  11. M. Cucuringu, I. Koutis, S. Chawla, G. Miller, and R. Peng, "Simple and Scalable Constrained Clustering: A Generalized Spectral Method", AISTATS 2016 (Artificial Intelligence and Statistics Conference) (2016)
  12. 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)
  13. M. Cucuringu, J. Woodworth, "Point Localization and Density Estimation from Ordinal kNN Graphs Using Synchronization", 2015 IEEE Machine Learning for Signal Processing Workshop (Short version) (2015)
  14. M. Cucuringu, "Synchronization over Z2 and community detection in multiplex signed networks with constraints", Journal of Complex Networks, 3 (3):469-506 (2015)
  15. S. H. Lee, M. Cucuringu, M. A. Porter, "Density-Based and Transport-Based Core-Periphery Structures in Networks", Physical Review E, Vol. 89, No. 3: 032810 (2014)
  16. 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)
  17. M. Cucuringu, V. Blondel, P. Van Dooren, "Extracting spatial information from networks with low-order eigenvectors", Physical Review E 87, 032803 (2013)
  18. 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)
  19. M. Cucuringu, M. W. Mahoney, "Localization on low-order eigenvectors of data matrices", Technical Report (2011) (arXiv)
  20. F. Blanchet-Sadri, E. Allen, C. Byrum, M. Cucuringu and R. Mercas, "Counting Bordered Partial Words by Critical Positions", The Electronic Journal of Combinatorics, Vol. 18 (2011)
  21. F. Blanchet-Sadri, M. Cucuringu, "Counting primitive partial words", Journal of Automata, Languages and Combinatorics 15 3/4, 199-227 (2010)
  22. M. Cucuringu, J. Puente, and D. Shue, "Model Selection in Undirected Graphical Models with Elastic Net ", Technical Report (2010) (arXiv)
  23. 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)
  24. M. Cucuringu, R. Strichartz, "Infinitesimal Resistance Metrics on Sierpinski Gasket Type Fractals", Analysis, Vol. 28, Issue 3, page 319-331 (2008)
  25. M. Cucuringu, R. Strichartz, "Self-Similar Energy Forms on the Sierpinski Gasket with Twists", Potential Analysis, Volume 27, Issue 1, pp. 45-60 (2007)

Ph.D. Thesis: Graph Realization and Low-Rank Matrix Completion, Princeton University, 2012


Co-Authors: Francine Blanchet-Sadri, Vincent Blondel, Sanjay Chawla David Cowburn, Kunal Chaudhury, Radek Erban, Sang Hoon Lee, Yiannis Koutis, Yaron Lipman, Michael Mahoney, Robert Mercas, Mason Porter, Michaela (Puck) Rombach, Amit Singer, Robert Strichartz, Hemant Tyagi Paul Van Dooren, Joseph Woodworth


Traveling/Talks
  • The Ninth Congress of Romanian Mathematicians, June 2019, Galati, Romania
  • The 28th Biennial Numerical Analysis Conference, minisymposia on "Matrix methods for Networks", University of Strathclyde, June 2019
  • Mathematical Finance Internal Seminar, Mathematical Institute, Oxford, June 2019
  • Universite Catholique de Louvain, Department of Mathematical Engineering, May 2019
  • Workshop on Mathematical Signal and Image Analysis, Raitenhaslach, Germany, April 2019
  • Qatar Computing Research Institute, Doha, Qatar, April 2019
  • University of Warwick, Department of Statistics, OxWaSP Mini-Symposia, November 2018
  • University of Nottingham, School of Mathematical Sciences, Algebra and Analysis Seminar, November 2018
  • Organizer of the session "Exploiting structure in constrained optimization", within the cluster "Learning: Machine Learning, Big Data, Cloud Computing, and Huge-Scale Optimization", 23rd International Symposium on Mathematical Programming (ISMP 2018), Bordeaux, France, July 2018
  • The Statistical Seminar, CREST (Center for Research in Economics and Statistics), Paris, June 2018
  • University of Edinburgh, LFCS Seminar, School of Informatics, May 2018
  • Complex Networks 2017, Lyon, November 2017
  • University of Bath, Conference on Scientific Computation and Differential Equations (SciCADE 2017), mini-symposium talk in the session "Nonlocal partial differential equations and graph-based techniques for imaging", September 2017
  • University of Bucharest, Conference on Recent Advances in Artificial Intelligence, RAAI 2017, June 2017
  • Applied Stochastic Models and Data Analysis (ASMDA 2017), talk at the "Optimisation for machine learning" session, London, June 6-9, 2017
  • University of Cambridge, Statistics Seminar, Cambridge, May 19, 2017
  • University of Warwick, Partial Differential Equations for Large Data, Workshop, May 10-12, 2017
  • Optimization and Statistical Learning, OSL 2017, Les Houches, France, April 9-14, 2017
  • University College London, Statistical Science Seminar, March 2017
  • Alan Turing Institute, Fellow Short Talks, Feb 2017
  • Alan Turing Institute, Turing meets Crick Event
  • University of Oxford, Numerical Analysis Seminar, January 2017
  • SIAM Conference on Uncertainty Quantification, invited talk, in the minisymposium "Model reduction in stochastic dynamical systems", EPFL, Lausanne, Switzerland, April 2016


Contact information

Email:
mihai [dot] cucuringu [at] stats [dot] ox [dot] ac [dot] uk
            mihai [dot] cucuringu [at] gmail [dot] com
Address: British Library
                96 Euston Road
                London NW1 2DB, United Kingdom
Address: Department of Statistics
                University of Oxford, Oxford
                24-29 St Giles'
                Oxford OX1 3LB, United Kingdom
Homepage: http://www.stats.ox.ac.uk/~cucuring/


© 2018 Mihai Cucuringu

Last update: September, 2018

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