معرفی
Hao Ni is Professor of Mathematics at University College London (UCL), where she leads the UCL Rough Path Theory and Machine Learning Group and serves as co-director of the EPSRC Centre for Doctoral Training in Collaborative Computational Modelling at the Interface (CCMI). As a co-investigator of the EPSRC Program grant on 'Unparameterised multi-modal data, high order signatures, and the mathematics of data science,' she bridges theoretical mathematics with practical machine learning applications. Previously, she was an Associate Professor at UCL (2016-2022) and held postdoctoral positions at Oxford-Man Institute of Quantitative Finance and Brown University.
- DPhil in Mathematics, University of Oxford (2012)
- MSc in Mathematics, University of Oxford (2009)
- BSc in Mathematics, Southeast University (2008)
Ni's research centers on stochastic analysis and machine learning, with a focus on rough path theory as a mathematical framework for modeling complex multi-modal data streams. She develops high-quality generative models for synthetic time series generation with applications spanning computer vision, healthcare, biology, and quantitative finance. Her work on signature-based methods has produced innovative approaches for skeleton-based action recognition, sepsis prediction, and financial data analysis. The signature of a path, originating from rough path theory, serves as a principled feature for sequential data that often boosts performance when combined with state-of-the-art machine learning methods.
Ni's publication record reveals a strong trajectory in developing mathematical foundations for data science, with recent work extending signature methods to Riemannian manifolds, creating novel GAN architectures for time series generation, and applying these techniques to real-world problems in healthcare and finance. Her research increasingly bridges pure mathematics with practical applications, particularly in generative modeling and time series analysis.
- Turing Fellow at the Alan Turing Institute (2016-2024)
- Co-investigator of EPSRC Program grant on Unparameterised multi-modal data
- Member of London Mathematical Society
As an academic leader, Ni has secured significant research funding through EPSRC and Turing Institute collaborations. She actively promotes equality, diversity, and inclusion by co-founding the WINDSMATH seminar series (2022), which showcases cutting-edge research by women and non-binary scholars in mathematics and data science with over 750 global subscribers. Her group organizes seminars, workshops, and conferences while developing open-source code repositories for real-world applications. Ni also organizes digital data hackathons focused on trustworthy synthetic time series generation through deepintomlf.ai.
Ni leads the UCL Rough Path and Machine Learning Research Group, which develops mathematical and numerical toolsets based on rough path theory to advance machine learning research on analyzing multi-modal complex data. The group's research spans expected signature theory, synthetic time series generation, human-computer interfaces, and molecule learning, with applications in finance, healthcare, and computer vision.
