Wenqi Zhu
Dphil in Mathematics
Status
Postgraduate Student
Research groups
Address
Mathematical Institute
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
University of Oxford
Andrew Wiles Building
Radcliffe Observatory Quarter
Woodstock Road
Oxford
OX2 6GG
Major / recent publications
- Wenqi Zhu, Coralia Cartis. Second-order methods for quartically-regularised cubic polynomials, with applications to high-order tensor methods (Link); Talk in SIAM Optimization 2023, invited mini-symposia talk in Optimization 2023, Aveiro.
- Congzheng Liu, Wenqi Zhu. Newsvendor Conditional Value-at-Risk Minimisation with a Non-Parametric Approach (Link); European Journal of Operational Research (2023).
- Wenqi Zhu, Coralia Cartis. Quartic Polynomial Sub-problem Solutions in Tensor Methods for Nonconvex Optimization (Link); NeurIPS Spotlight Papaer and Presentation for HOO Workshop (2022).
- Wenqi Zhu, Yuji Nakatsukasa. Approximations of Multivariate Functions in Irregular Domains via Vandermonde with Arnoldi (Link); In review with IMAJNA (2022), invited mini-symposia talk in 29th Biennial Numerical Analysis Meeting, 2023.
Further details
Prizes, awards, and scholarships
- DPhil in Mathematics Studentships, 2021-2025. Funded by INNOHK and CIMDA Centre partnership
- DTP studentship. Funded by UKRI-BBSRC and the partnership
- Industrial CASE Studentship, 2021-2025. Funded by Syngenta Group
- Catherine Hughes Fund Internship Award, 2021. Funded by Somerville College, Oxford
- M.Sc. Prize for Excellence, 2021. Awarded by MMSC Examining Committee, Oxford.
- Archibald Jackson Prize, 2021. Awarded by Somerville College, Oxford.
- St. Hugh’s College Scholarship, 2011-2014. Awarded by St. Hugh’s College, Oxford.
Teaching
- 2023: Numerical Linear Algebra (Class Tutor)
- 2021-22: C6.1 Numerical Linear Algebra (TA)
- 2021: C6.2 Continuous optimization (TA)
Research interests
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High-order nonconvex optimization
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High-dimensional functional approximation
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Algorithms on large data sets and randomized algorithms on low-rank approximation;
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Tensors methods and tensors decompositions.