Mihai Cucuringu

Associate Professor

Department of Statistics
Mathematical Institute
Oxford-Man Institute of Quantitative Finance
University of Oxford

Stipendiary Lecturer
Merton College

Turing Fellow
The Alan Turing Institute

[Homepage]      [Research]     [StatML in Finance]     [CV (2019)]      [Personal]     

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 an associate member of the Oxford-Man Institute of Quantitative Finance and a Stipendiary Lecturer in Statistics at Merton College, University of Oxford, and a Turing Fellow at The Alan Turing Institute in London.

Here is my Google Scholar page.

We have a weekly Statistics and Machine Learning in Finance (SMLFin) Seminar Series.

If you are interested in a DPhil in Statistics at Oxford, feel free to send me an email with your CV and research interests. See also the EPSRC CDTs: StatML-Modern Statistics and Statistical Machine Learning (info) and Mathematics of Random Systems: Analysis, Models and Algorithms (info). Please note that I will not be able to comment on individual research statements, due to an unhealthy number of such requests.

Bio: 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.

Program Co-chair of the 4th ACM Conference on AI in Finance (ICAIF 2023). Co-organizer for the workshop, ICAIF'23 Workshop on NLP and Network Analysis in Financial Applications. Previously organized ICAIF'21 Workshop, ICAIF'22 Workshop, and the workshop on Network Science in Financial Services at The Alan Turing Institute (2019).

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


  • University of Oxford:
    • Foundations of Data Science, Mathematical Institute, CDT in Mathematics of Random Systems (2019, 2020, 2021, 2022, 2023)
    • Statistical Programming, Department of Statistics (2019)
    • Probability and Statistics for Network Analysis, Department of Statistics (joint with Gesine Reinert) (2017, 2018, 2022)
  • UCLA:
    • Ordinary Differential Equations with Linear Algebra for Life Sciences Students, MATH 3C, Department of Mathematics (Spring 2016)
    • Topics in Data Science: Algorithms and Mathematical Foundations, MATH 191, Department of Mathematics (course description) (syllabus) (Fall 2015)
    • Graphs and Networks, MATH 191, Department of Mathematics (course description) (syllabus) (Winter 2015)
    • Mathematics of Finance, MATH 174E, Department of Mathematics (syllabus) (Spring 2014)
    • Probability for Life Sciences Students, MATH 3C, Department of Mathematics (syllabus) (Fall 2013)
  • Princeton:
    • Game Theory, MAT 308 / ECO 318, Departments of Mathematics and Economics (syllabus) (Spring 2011)

Publications and preprints

    [Last updated: Sept 2023. Please see Google Scholar, arXiv, and SSRN for an up-to-date list]. For finance-related papers only, see here.
  1. Mihai Cucuringu, Xiaowen Dong, Ning Zhang, Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering, (2024)
  2. Nikolas Michael, Mihai Cucuringu, Sam Howison, Options-driven Volatility Forecasting, SSRN (2024)
  3. Álvaro Cartea, Mihai Cucuringu, Mark Jennings, Chao Zhang, A Similarity-based Approach to Covariance Forecasting, SSRN (2023)
  4. Álvaro Cartea, Mihai Cucuringu, Qi Jin, Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies, SSRN [BibTeX] (2023)
  5. Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu, Robust Angular Synchronization via Directed Graph Neural Networks, to appear at ICLR 2024, arXiv (2023)
  6. Emmanuel Djanga, Mihai Cucuringu, and Chao Zhang, Cryptocurrency volatility forecasting using commonality in intraday volatility, ICAIF 2023, Association for Computing Machinery, New York, NY, USA (2023)
  7. Bogdan Sitaru, Anisoara Calinescu, Mihai Cucuringu, Order Flow Decomposition for Price Impact Analysis in Equity Limit Order Books, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN, (2023)
  8. Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren, Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN, (2023)
  9. Álvaro Cartea, Mihai Cucuringu, Qi Jin, Correlation Matrix Clustering for Statistical Arbitrage Portfolios, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN [BibTeX] (2023)
  10. Danni Shi, Jan-Peter Calliess, Mihai Cucuringu Multireference Alignment for Lead-Lag Detection in Multivariate Time Series and Equity Trading, ICAIF 2023, Association for Computing Machinery, New York, NY, USA, SSRN, (2023)
  11. Rama Cont, Mihai Cucuringu, Jonathan Kochems, Felix Prenzel, Limit Order Book Simulation with Generative Adversarial Networks, SSRN [BibTeX] (2023)
  12. Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu, Carsten Maple, Gesine Reinert, Andrew Elliott, SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation, arXiv [BibTeX] (2023)
  13. Anastasia Mantziou, Mihai Cucuringu, Victor Meirinhos, Gesine Reinert, The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights, to appear in Journal of Complex Networks, arXiv, [BibTeX] (2023)
  14. Deborah Miori, Mihai Cucuringu, DeFi: Modeling and Forecasting Trading Volume on Uniswap v3 Liquidity Pools, SSRN (2023)
  15. Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren, Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models (SSRN), (arXiv) (2023)
  16. Nikolas Michael, Mihai Cucuringu, Sam Howison, OFTER: An Online Pipeline for Time Series Forecasting, SSRN (2023)
  17. Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong, Graph Neural Networks for Forecasting Realized Volatility with Nonlinear Spillover Effects, SSRN (2023)
  18. Yutong Lu, Gesine Reinert, Mihai Cucuringu, Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets, SSRN, (arXiv) (2023)
  19. Milena Vuletić, Felix Prenzel, Mihai Cucuringu, Fin-GAN: Forecasting and Classifying Financial Time Series via Generative Adversarial Networks, SSRN, Quantitative Finance, [code FinGAN], [BibTeX] (2024)
  20. Deborah Miori, Mihai Cucuringu, DeFi: Data-Driven Characterisation of Uniswap V3 Ecosystem & an Ideal Crypto Law for Liquidity Pools, to appear in Digital Finance, SSRN (2023)
  21. Qiong Wu, Jian Li, Zhenming Liu, Yanhua Li, Mihai Cucuringu, Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models, Proceedings of the AAAI Conference on Artificial Intelligence AAAI 2023, [BibTeX] (2022)
  22. Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong, Graph-based Methods for Forecasting Realized Covariances, SSRN (2022)
  23. Stratis Limnios, Andrew Elliott, Mihai Cucuringu, Gesine Reinert, Random Walk based Conditional Generative Model for Temporal Networks with Attributes, NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022), [BibTeX] (2022)
  24. Yutong Lu, Gesine Reinert, Mihai Cucuringu, Trade Co-occurrence,Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets, SSRN, (arXiv), to appear in Quantitative Finance, (2024)
  25. Yixuan He, Michael Permultter, Gesine Reinert, Mihai Cucuringu, MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian, Learning on Graphs conference (LoG) 20202, [BibTeX] (2022)
  26. Deborah Miori, Mihai Cucuringu, Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes, (arXiv) ICAIF (2022)
  27. Felix Prenzel, Rama Cont, Mihai Cucuringu, Jonathan Kochems, Dynamic Calibration of Order Flow Models with Generative Adversarial Networks, ICAIF '22: 3rd ACM International Conference on AI in Finance, November 2022, pages 446--453 (Best Paper Award) (2022)
  28. Deborah Miori, Mihai Cucuringu, SEC Form 13F-HR: Statistical investigation of trading imbalances and profitability analysis, (arXiv) (2022)
  29. Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong, Graph similarity learning for change-point detection in dynamic networks, Machine Learning 2023, (arXiv) [BibTeX] (2023)
  30. Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert, DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series, (arXiv), Learning on Graphs conference (LoG) 2022, [BibTeX] (2022)
  31. Yixuan He, Xitong Zhang, Junjie Huang, Mihai Cucuringu, Gesine Reinert, PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural Networks for Signed and Directed Graphs, (arXiv) [BibTeX] (code) (2022)
  32. Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, Mihai Cucuringu, GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks, International Conference on Machine Learning (ICML), PMLR 162:8581-8612, arxiv.2202.00211, (arXiv) [BibTeX] (2022)
  33. Rama Cont, Mihai Cucuringu, Chao Zhang, Renyuan Xu, Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation (arXiv), SSRN, [Code (GitHub)] (2022)
  34. Chao Zhang, Yihuang Zhang, Mihai Cucuringu, Zhongmin Qian, Volatility forecasting with machine learning and intraday commonality, to appear in Journal of Financial Econometrics (2022)
  35. Nikolas Michael, Mihai Cucuringu, Sam Howison, Option Volume Imbalance as a predictor for equity market returns, arXiv 2201.09319 (2022)
  36. Rama Cont, Mihai Cucuringu, Chao Zhang, Cross-impact of order flow imbalance in equity markets Quantitative Finance, 0: 1-21 (2023)
  37. Jeub, Lucas GS, Giovanni Colavizza, Xiaowen Dong, Marya Bazzi, and Mihai Cucuringu, Local2Global: Scaling global representation learning on graphs via local training, (arXiv), Machine Learning 112, 1663-1692 (2023) [BibTeX] (2023). Short version in KDD workshop on Deep Learning on Graphs: Method and Applications (DLG-KDD '21) (arXiv), (code)
  38. Stefanos Bennett, Mihai Cucuringu and Gesine Reinert, Lead-lag detection and network clustering for multivariate time series with an application to the US equity market, Machine Learning 111, 4497-4538 (2022); [BibTeX] (2022). Workshop version: KDD Workshop on mining and learning from time series (2021) KDD MiLeTs (2021)
  39. Yixuan He, Gesine Reinert, Songchao Wang, Mihai Cucuringu, SSSNET: Semi-Supervised Signed Network Clustering, SIAM International Conference on Data Mining (SDM22), (arXiv) [BibTeX](2022)
  40. Rama Cont, Mihai Cucuringu, Vacslav Glukhov, Felix Prenzel, Analysis and modeling of client order flow in limit order markets, Quantitative Finance, 23:2, 187-205, [BibTeX] (2023)
  41. Chao Zhang, Zihao Zhang, Mihai Cucuringu, Stefan Zohren, A Universal End-to-End Approach to Portfolio Optimization via Deep Learning, arXiv 2111.09170 (2021)
  42. Qiong Wu, Christopher G. Brinton, Zheng Zhang, Andrea Pizzoferrato, Zhenming Liu, Mihai Cucuringu, Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing, International Conference on AI in Finance (ICAIF 2021), [BibTex] (2021)
  43. Y. He, G. Reinert, M. Cucuringu, DIGRAC: Digraph Clustering with Flow Imbalance, (arXiv), Learning on Graphs conference (LoG) 2022, [BibTeX] (2022)
  44. J. Albers, M. Cucuringu, S. Howison, A. Y. Shestopaloff, Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets, Applied Mathematical Finance, 28:5, 395-448, (arXiv), [BibTex] (2021)
  45. M. Cucuringu, H. Tyagi, An extension of the angular synchronization problem to the heterogeneous setting, (arXiv), Foundations of Data Science, 4(1):71-122, 2022, [BibTeX]
  46. M. Cucuringu, A. V. Singh, D. Sulem, H. Tyagi, Regularized spectral methods for clustering signed networks, Journal of Machine Learning Research (JMLR), 22(264):1-79, 2021. [BibTeX] (2021)
  47. W. G. Underwood, A. Elliott, M. Cucuringu, Motif-Based Spectral Clustering of Weighted Directed Networks, Applied Network Science 5, 62 [BibTeX] (2020)
  48. A. Elliott, A. Chiu, M. Bazzi, G. Reinert, M. Cucuringu, Core-periphery structure in directed networks, Proceedings of the Royal Society A 476, no. 2241 [BibTeX] (2020)
  49. O.M. Crook, M. Cucuringu, T. Hurst, C.B. Schonlieb, M. Thorpe, K.C. Zygalakis, A Linear Transportation Lp Distance for Pattern Recognition, to appear in Pattern Recognition, (arXiv:2009.11262) [BibTeX] (2020)
  50. S. L. Chau, M. Cucuringu, D. Sejdinovic, Spectral Ranking with Covariates, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), (arXiv) [BibTeX] (2022)
  51. S. Chretien, M. Cucuringu, G. Lecue, L. Neirac, Learning with Semi-Definite Programming: new statistical bounds based on fixed point analysis and excess risk curvature, Journal of Machine Learning Research (JMLR), 22(230):1-64, 2021 [BibTeX] (2021)
  52. M. Cucuringu, H. Li, H. Sun, L. Zanetti, "Hermitian matrices for clustering directed graphs: insights and applications", (arXiv), In International Conference on Artificial Intelligence and Statistics (AISTATS 2020), pp. 983-992, PMLR [BibTeX] (2020)
  53. M. Cucuringu, H. Tyagi, "Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping", (arXiv), [code], Journal of Machine Learning Research (JMLR), 21(32):1-77, [BibTeX] 2020
  54. A. d'Aspremont, M. Cucuringu, H. Tyagi, Ranking and synchronization from pairwise measurements via SVD, Journal of Machine Learning Research (JMLR), 22(19):1-63 [BibTeX] (2021)
  55. M. Cucuringu, A. Pizzoferrato, Y. van Gennip, An MBO scheme for clustering and semi-supervised clustering of signed networks, (arXiv), Communications in Mathematical Sciences, Vol 19, No. 1 [BibTeX] (2021)
  56. A. Tsakalidis, M. Bazzi, M. Cucuringu, P. Basile, B. McGillivray, Mining the UK Web Archive for Semantic Change Detection In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019) (pp. 1212-1221) [BibTeX] (2019)
  57. M. Cucuringu, P. Davies, A. Glielmo, H. Tyagi, "SPONGE: A generalized eigenproblem for clustering signed networks", AISTATS 2019 (code) [BibTeX] (2019)
  58. A. Elliott, M. Cucuringu, M. M. Luaces, P. Reidy, G. Reinert, Anomaly detection in networks with application to financial transaction networks, (arXiv) [BibTeX] (2018)
  59. A. Tsokos, S. Narayanan, I. Kosmidis, G. Baio, M. Cucuringu, G. Whitaker and F. J. Király, "Modeling outcomes of soccer matches", (arXiv), Machine Learning 108, 77-95 (2019) [BibTeX] (2019)
  60. M. Cucuringu, H. Tyagi, "On denoising modulo 1 samples of a function", (arXiv), [code], AISTATS 2018 [BibTeX] (2018)
  61. 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 [BibTeX] (2017)
  62. M. Cucuringu, C. Marshak, D. Montag, P. Rombach, "Rank Aggregation for Course Sequence Discovery", Complex Networks [BibTeX] (2017)
  63. 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. [BibTeX] (2016)
  64. 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) [BibTeX] (2016)
  65. 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 [BibTeX] (2016)
  66. 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) [BibTeX] (2015)
  67. M. Cucuringu, "Synchronization over Z2 and community detection in multiplex signed networks with constraints", Journal of Complex Networks, 3 (3):469-506 [BibTeX] (2015)
  68. 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 [BibTeX] (2014)
  69. 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 [BibTeX] (2012)
  70. M. Cucuringu, V. Blondel, P. Van Dooren, "Extracting spatial information from networks with low-order eigenvectors", Physical Review E 87, 032803 [BibTeX] (2013)
  71. 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 [BibTeX] (2012)
  72. M. Cucuringu, M. W. Mahoney, "Localization on low-order eigenvectors of data matrices", Technical Report (arXiv) [BibTeX] (2011)
  73. 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 [BibTeX] (2011)
  74. F. Blanchet-Sadri, M. Cucuringu, "Counting primitive partial words", Journal of Automata, Languages and Combinatorics 15 3/4, 199-227 [BibTeX] (2010)
  75. M. Cucuringu, J. Puente, and D. Shue, "Model Selection in Undirected Graphical Models with Elastic Net ", Technical Report (arXiv) [BibTeX] (2010)
  76. 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 [BibTeX] (2010)
  77. M. Cucuringu, R. Strichartz, "Infinitesimal Resistance Metrics on Sierpinski Gasket Type Fractals", Analysis, Vol. 28, Issue 3, page 319-331 [BibTeX] (2008)
  78. M. Cucuringu, R. Strichartz, "Self-Similar Energy Forms on the Sierpinski Gasket with Twists", Potential Analysis, Volume 27, Issue 1, pp. 45-60 [BibTeX] (2007)

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

  • The OMI Machine Learning and Quantitative Finance Conference, Oxford, June 2023
  • Foundations of Computational Mathematics (FoCM 2023), Foundations of Data Science and Machine Learning, Paris, June 2023
  • AAAI-2023 Workshop on Multimodal AI For Financial Forecasting, Feb 2023
  • Umeå University, Joint Statistical Seminar, Department of Statistics & Department of Mathematics, Sept 2022
  • OxML 2022 - Oxford Machine Learning Summer School, AI for Global Goals, August 2022
  • Edinburgh MAC-MIGS Workshop on Learning Complex Structures on Graphs, July 2022
  • 11th World Congress of the Bachelier Finance Society, Machine Learning in Finance I, Jun 2022
  • One World Mathematics of INformation, Data, and Signals (1W-MINDS) Seminar, April 2022
  • Financial Computing and Analytics Seminar, University College London (UCL), Feb 2022
  • Statistics and Data Science Seminar, School of Mathematical Sciences, Queen Mary, University of London, December, 2021
  • Young Researchers Workshop, Plenary Speaker, Romanian Society of Probability and Statistics, Nov 2021
  • Numerical Analysis Seminar, University of Strathclyde, November 2021
  • Keynote Talk, KDD Workshop on Machine Learning in Finance, August 2021
  • Networks Seminar, Mathematical Institute, Oxford, June 2021
  • Oxford-Warwick-Edinburgh Network Science Workshop, February 2021
  • Joint Mathematics Meetings AMS-MAA, AMS Special Session on Applied Combinatorial Methods, January 2021
  • 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

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/

© 2023 Mihai Cucuringu

Last update: September, 2020

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