Professor Mihai Cucuringu

Associate Professor of Statistics

Biographical Sketch

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 and the Institute for New Economic Thinking. Prior to this

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

Research Interests

I am interested in the development and mathematical & statistical analysis of algorithms that extract information from massive noisy data sets, network analysis, and certain computationally-hard inverse problems on large graphs. Applications include 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 relaxation algorithms and applications to ranking, clustering, group synchronization, phase unwrapping
networks, community and core-periphery structure, network time series
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

Publications

Turner, E. and Cucuringu, M. (2023) “Graph Denoising Networks: A Deep Learning Framework for Equity Portfolio Construction”, in PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023, pp. 193–201.

Djanga, E., Cucuringu, M. and Zhang, C. (2023) “Cryptocurrency volatility forecasting using commonality in intraday volatility”, in PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023, pp. 436–444.

Chau, S., Cucuringu, M. and Sejdinovic, D. (2023) “Spectral Ranking with Covariates”, in. Springer Nature, pp. 70–86.

He, Y., Permultter, M., Reinert, G. and Cucuringu, M. (2022) “MSGNN: a spectral graph neural network based on a novel magnetic signed Laplacian”, in Proceedings of the First Learning on Graphs Conference (LoG 2022). Journal of Machine Learning Research, pp. 40:1 – 40:39.

Clarkson, J., Cucuringu, M., Elliott, A. and Reinert, G. (2022) “DAMNETS: a deep autoregressive model for generating Markovian network time series”, in Proceedings of the First Learning on Graphs Conference. Journal of Machine Learning Research, pp. 23:1 – 23:19.

Bennett, S., Cucuringu, M. and Reinert, G. (2022) “Lead–lag detection and network clustering for multivariate time series with an application to the US equity market”, Machine Learning, 111(12), pp. 4497–4538.

Prenzel, F., Cont, R., Cucuringu, M. and Kochems, J. (2022) “Dynamic calibration of order flow models with generative adversarial networks”, in ICAIF ’22: 3rd ACM International Conference on AI in Finance. Association for Computing Machinery.

Limnios, S., Elliott, A., Cucuringu, M. and Reinert, G. (2022) “Random walk based conditional generative model for temporal networks with attributes ”, in Advances in Neural Information Processing Systems 36 (NeurIPS 2022). Neural Information Processing Systems Foundation.

He, Y., Gan, Q., Wipf, D., Reinert, G., Yan, J. and Cucuringu, M. (2022) “GNNRank: learning global rankings from pairwise comparisons via directed graph neural networks”, in Proceedings of the 39th International Conference on Machine Learning. Journal of Machine Learning Research, pp. 8581–8612.

Lu, Y., Reinert, G. and Cucuringu, M. (2022) “Trade co-occurrence, trade flow decomposition, and conditional order imbalance in equity markets.”