About me
I am Lecturer in Financial Mathematics at the School of Mathematical Sciences of the Queen Mary University of London. My research sits at the interface of numerical analysis and practical derivative pricing and risk management, combining a background in finite element methods with an interest in machine learning.
My work focuses on efficient methods for option pricing and counterparty credit risk, including neural network and Chebyshev interpolation techniques for high-dimensional parametric problems. I improved and implemented a Chebyshev interpolation method for implied volatilities, which was adopted into NAG's production numerical library, used across the financial industry. I also enjoy designing and teaching courses in financial mathematics and hands-on machine learning.
My Publications
2026:
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Numerical Methods for Lambda Quantiles: Robust Evaluation and Portfolio Optimization
with Ilaria Peri, published in SIAM Journal on Financial Mathematics.
2025:
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Function approximations for counterparty credit exposure calculations
with Kathrin Glau, and Domagoj Demeterfi, preprint on arXiv:2507.09004v1.
2024:
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Neural network expression rates and applications of the deep parametric PDE method in counterparty credit risk
with Kathrin Glau, published open access in Annals of Operations Research,
2022:
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The deep parametric PDE method: application to option pricing
with Kathrin Glau, published open access in Applied Mathematics and Computation,
Sample implementation on GitHub; -
Efficient valuation of callable bonds: The dynamic Chebyshev method
with Kathrin Glau and Mikhail Soloveitchik, Blog-post published by QuantMinds International. -
Forecasting the Olympic medal distribution – A socioeconomic machine learning model
with Christoph Schlembach, Sascha Schmidt, Dominik Schreyer, published in Technological Forecasting and Social Change, arXiv:2012.04378; -
Hybrid Discretizations in Solid Mechanics for Non-linear and Non-smooth Problems
with Hamid Reza Bayat, Julian Krämer, Stefanie Reese, Barbara Wohlmuth, Christian Wieners, chapter of Non-standard Discretisation Methods in Solid Mechanics (part of the Springer Lecture Notes in Applied and Computational Mechanics series).
2021:
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Machine Learning for Business Students: An Experiential Learning Approach
with Allen Higgins, Yossi Lichtenstein, published open access in ITiCSE '21: Proceedings of the 26th ACM Conference on Innivation and Teaching in Computer Science Education;
2020:
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An approach to construct a three-dimensional isogeometric model from µ-CT scan data with an application to the bridge of a violin
with Klaus Achterhold, Sandra Marschke, Franz Pfeiffer, Wolfgang Ring, published in in Computer Aided Geometric Design, hal-02360922v2;
2019:
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Biorthogonal splines for optimal weak patch-coupling in isogeometric analysis with applications to finite deformation elasticity
with Alexander Seitz, Mert Deniz Alaydin, Barbara Wohlmuth, Alexander Popp, published in Computer Methods in Applied Mechanics and Engineering, arXiv:1806.11535; -
A hybrid isogeometric approach on multi-patches with applications to Kirchhoff plates and eigenvalue problems
with Thomas Horger, Alessandro Reali, Barbara Wohlmuth, published in Computer Methods in Applied Mechanics and Engineering, arXiv:1701.06353; -
Isogeometric shape optimization for nonlinear ultrasound focusing
with Markus Muhr, Vanja Nikolić, Barbara Wohlmuth, published in AIMS Evolution Equations and Control Theory, arXiv:1712.05228;
2018
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Numerical evaluation of discontinuous and nonconforming
finite element methods in nonlinear solid mechanics
with Hamid Reza Bayat, Julian Krämer, Stephan Wulfinghoff, Stefanie Reese, Barbara Wohlmuth, Christian Wieners, published in Computational Mechanics;
2017
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Hybrid Finite Element Methods for Non-linear and Non-smooth Problems in Solid Mechanics
PhD Thesis, Technical University Munich; -
Reduced basis isogeometric mortar approximations for eigenvalue problems in vibroacoustics
with Thomas Horger, Barbara Wohlmuth, published by Springer in Model Reduction of Parametrized Systems, arXiv:1606.01163;
2016
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A hybrid weakly nonconforming discretization for linear elasticity
with Julian Krämer, Christian Wieners, Barbara Wohlmuth, published in Proceedings in Applied Mathematics and Mechanics;
2015
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Contact and mesh-tying using mortar methods
extended abstract published in Oberwolfach Reports; -
The Influence of Quadrature Errors on Isogeometric Mortar Methods
with Ericka Brivadis, Annalisa Buffa, Barbara Wohlmuth, published by Springer in Isogeometric Analysis and Applications 2014, arXiv:1504.05390; -
Trace and flux a priori error estimates in finite element approximations of Signorini-type problems
with Olaf Steinbach, Barbara Wohlmuth, published in IMA Journal of Numerical Analysis, arXiv:1409.0563; -
Isogeometric mortar methods
with Ericka Brivadis, Annalisa Buffa, Barbara Wohlmuth, published in Computer Methods in Applied Mechanics and Engineering, arXiv:1407.8313;
My Activities
2026
- Gave an invited talk at the International Conference on Computational Finance 2026 in Oxford.
- Published From Pixels to Populations, an interactive website and activity for students; Hosted a launch event with 36 secondary school students
2025 and earlier
- 2025: Congratulations to my PhD student Domagoj Demeterfi for defending his thesis Function Approximations in Credit Exposure Simulations: Theory and Numerics. Thanks to the examiners, Kees Oosterlee and Fang Fang.
- 2024: Published a prediction of the medal table at the 2024 Olympic games computed for an article in the Economist;
- 2023: Visited UCD Michael Smurfit Business School to teach Machine Learning for Business.
- 2023: Teaching Mathematical Tools for Asset Management at QMUL;
- 2022: Gave talks at the Joint Risk and Stochastics and Financial Mathematics Seminar at LSE and the Probablity and Applications Seminar at QMUL;
- 2022: Start teaching the Masterclass in Business Analytics at QMUL;
- 2022: Gave an invited talk at the 6th London-Paris Bachelier Workshop in Paris;
- 2022: Taught Machine Learning for Business for the fourth time at the University College Dublin (online);
- 2021: Presented at the Mathematical Sciences Seminar at Birkbeck University of London;
- 2021: Starting my new position as Lecturer in Financial Mathematics at Queen Mary University of London;
- 2021: Presenting at the German Probability and Statistic Days Mannheim;
- 2021: Taught Machine Learning for Business for the third time at the University College Dublin. Due to the current pandemic, the course was held online;
- 2021: Presented the deep parametric PDE method at the SIAM Conference on Financial Mathematics and Engineering;
- 2021: Gave an invited to talk at The Quantitative Finance Conference;
- 2020: The fast computation of implied volatilities using Chebyshev interpolation is now available in the NAG library;
- 2020: Teaching Machine Learning for Business for the second time at the University College Dublin. Due to the current pandemic, the course was held online;
- 2020: Started work on the EPSRC-funded research project Deep Learning Reduced Basis Method for High-Dimensional Parametric Partial Differential Equations in Finance with PI Kathrin Glau;
- 2019: Teaching a new block course Machine Learning for Business at the University College Dublin (joint with Yossi Lichtenstein and thankfully organised by Allen Higgins);
- 2019: Presented efficient numerical techniques for parametric problems in option pricing at the International Conference on Computational Finance;
- 2019: Presented Kathrin's interactive art project creating the universe at Tate Exchange, Tate Modern, London;
- 2018: Kick-Off for our new industry funded project on the efficient computation of implied volatilities;
- 2018: Started my work at the School of Mathematics at the Queen Mary University of London;
- 2017: Successfully defended my PhD Thesis;
- 2017: Visited Alessandro Reali at the University of Pavia;
- 2016: Presented Isogeometric mortar methods in vibroacoustics at the USACM Conference on Isogeometric Analysis and Meshfree Methods;
- 2015: Presented Contact and mesh-tying using mortar methods at the Oberwolfach-Workshop Computational Engineering;
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