Program Chair
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 and Decentralised Finance Research Group (DeFOx) 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.
Co-organizer for the workshop, ICAIF'23 Workshop on NLP and Network Analysis in Financial Applications - check out the Call for Papers. 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
- 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
- statistical analysis of financial data, statistical arbitrage, market microstructure, limit order books, risk models ; SMLFin Seminar Series
- nonlinear dimensionality reduction and diffusion maps, intrinsic slow variables in dynamic data
- low-rank matrix completion, distance geometry problems, rigidity theory, sensor network localization and 3D structuring of molecules
Ongoing projects
- clustering signed & directed networks and time series data
- ML-based methods for forecasting realized volatility and covariances
- ranking & lead-lag detection in nonlinear time series; connections to multireference alignemnt
- extensions of group synchronization
- synthetic data generation (networks, limit order books, financial time series)
- statistics and machine learning in finance; weekly SMLFin Seminar Series
- decentralised finance; weekly Decentralised Finance Research Group (DeFOx) Seminar Series
- change-point detection in network time series
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
- 1999 -2003: "Gheorghe Munteanu Murgoci" High School, Braila, Romania
Teaching
- 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)
- Instructor: 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.
- Álvaro Cartea, Mihai Cucuringu, Mark Jennings, Chao Zhang, A Similarity-based Approach to Covariance Forecasting, SSRN (2023)
- Álvaro Cartea, Mihai Cucuringu, Qi Jin, Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies, SSRN [BibTeX] (2023)
- Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu, Robust Angular Synchronization via Directed Graph Neural Networks, to appear at ICLR 2024, arXiv (2023)
- 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)
- 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)
- 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)
- 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)
- Danni Shi, Mihai Cucuringu, Jan-Peter Calliess, 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)
- Rama Cont, Mihai Cucuringu, Jonathan Kochems, Felix Prenzel, Limit Order Book Simulation with Generative Adversarial Networks, SSRN [BibTeX] (2023)
- 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)
- 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)
- Deborah Miori, Mihai Cucuringu, DeFi: Modeling and Forecasting Trading Volume on Uniswap v3 Liquidity Pools, SSRN (2023)
- Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff, Stefan Zohren, Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models (SSRN), (arXiv) (2023)
- Nikolas Michael, Mihai Cucuringu, Sam Howison, OFTER: An Online Pipeline for Time Series Forecasting, SSRN (2023)
- Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong, Graph Neural Networks for Forecasting Realized Volatility with Nonlinear Spillover Effects, SSRN (2023)
- 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)
- 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)
- 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)
- 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)
- Chao Zhang, Xingyue Pu, Mihai Cucuringu, Xiaowen Dong, Graph-based Methods for Forecasting Realized Covariances, SSRN (2022)
- 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)
- Yutong Lu, Gesine Reinert, Mihai Cucuringu, Trade Co-occurrence,Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets, SSRN, (arXiv) (2022)
- 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)
- Deborah Miori, Mihai Cucuringu, Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes, (arXiv) ICAIF (2022)
- 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)
- Deborah Miori, Mihai Cucuringu, SEC Form 13F-HR: Statistical investigation of trading imbalances and profitability analysis, (arXiv) (2022)
- Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong, Graph similarity learning for change-point detection in dynamic networks, Machine Learning 2023, (arXiv) [BibTeX] (2023)
- 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)
- 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)
- 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)
- Rama Cont, Mihai Cucuringu, Chao Zhang, Renyuan Xu, Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation (arXiv), SSRN, [Code (GitHub)] (2022)
- Chao Zhang, Yihuang Zhang, Mihai Cucuringu, Zhongmin Qian, Volatility forecasting with machine learning and intraday commonality, to appear in Journal of Financial Econometrics (2022)
- Nikolas Michael, Mihai Cucuringu, Sam Howison, Option Volume Imbalance as a predictor for equity market returns, arXiv 2201.09319 (2022)
- Rama Cont, Mihai Cucuringu, Chao Zhang, Cross-impact of order flow imbalance in equity markets Quantitative Finance, 0: 1-21 (2023)
- 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)
- 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)
- Yixuan He, Gesine Reinert, Songchao Wang, Mihai Cucuringu, SSSNET: Semi-Supervised Signed Network Clustering, SIAM International Conference on Data Mining (SDM22), (arXiv) [BibTeX](2022)
- 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)
- Chao Zhang, Zihao Zhang, Mihai Cucuringu, Stefan Zohren,
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning, arXiv 2111.09170 (2021)
- 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)
- Y. He, G. Reinert, M. Cucuringu, DIGRAC: Digraph Clustering with Flow Imbalance, (arXiv), Learning on Graphs conference (LoG) 2022, [BibTeX] (2022)
- 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)
- 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]
- 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)
- W. G. Underwood, A. Elliott, M. Cucuringu, Motif-Based Spectral Clustering of Weighted Directed Networks, Applied Network Science 5, 62 [BibTeX] (2020)
- 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)
- 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)
- 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)
- 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)
- 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)
- 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
- 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)
- 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)
- 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)
- M. Cucuringu, P. Davies, A. Glielmo, H. Tyagi, "SPONGE: A generalized eigenproblem for clustering signed networks", AISTATS 2019 (code) [BibTeX] (2019)
- A. Elliott, M. Cucuringu, M. M. Luaces, P. Reidy, G. Reinert, Anomaly detection in networks with application to financial transaction networks, (arXiv) [BibTeX] (2018)
- 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)
- M. Cucuringu, H. Tyagi, "On denoising modulo 1 samples of a function", (arXiv), [code], AISTATS 2018 [BibTeX] (2018)
- 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)
- M. Cucuringu, C. Marshak, D. Montag, P. Rombach, "Rank Aggregation for Course Sequence Discovery", Complex Networks [BibTeX] (2017)
- 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)
- 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)
- 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)
- 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)
- M. Cucuringu, "Synchronization over Z_{2} and community detection in multiplex signed networks with constraints", Journal of Complex Networks, 3 (3):469-506 [BibTeX] (2015)
- 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)
- 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)
- M. Cucuringu, V. Blondel, P. Van Dooren, "Extracting spatial information from networks with low-order eigenvectors", Physical Review E 87, 032803 [BibTeX] (2013)
- 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)
- M. Cucuringu, M. W. Mahoney, "Localization on low-order eigenvectors of data matrices", Technical Report (arXiv) [BibTeX] (2011)
- 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)
- F. Blanchet-Sadri, M. Cucuringu, "Counting primitive partial words", Journal of Automata, Languages and Combinatorics 15 3/4, 199-227 [BibTeX] (2010)
- M. Cucuringu, J. Puente, and D. Shue, "Model Selection in Undirected Graphical Models with Elastic Net ", Technical Report (arXiv) [BibTeX] (2010)
- 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)
- M. Cucuringu, R. Strichartz, "Infinitesimal Resistance Metrics on Sierpinski Gasket Type Fractals", Analysis, Vol. 28, Issue 3, page 319-331 [BibTeX] (2008)
- 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
Traveling/Talks
- 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
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/
© 2023 Mihai Cucuringu
Last update: September, 2020
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