Affiliation: My name is Daniel Bartl and I am currently a Presidential Young Professor at the National University of Singapore, appointed jointly between the Department of Mathematics and the Department of Statistics & Data Science.
Research interests: Data Science (high-dimensional Probability & Statistics, robust Optimization), Optimal Transport, Mathematical Finance
Papers (see also google scholar):
On the structure of marginals in high dimensions with S. Mendelson
preprint
The geometry of the adapted Bures-Wasserstein space with B. Acciaio, A. Grass, S. Hou, G. Pammer
preprint
Robust, sub-Gaussian mean estimators in metric spaces with G. Lugosi, R. Oliveira, Z. Rico
preprint
Do we really need the Rademacher complexities? with S. Mendelson
preprint
The Wasserstein Space of Stochastic Processes in Continuous Time with M. Beiglboeck, G. Pammer, S. Schrott, X. Zhang
preprint
Fast Wasserstein rates for estimating probability distributions of probabilistic graphical models with S. Eckstein
Annals of Statistics, 2026+
Numerical method for nonlinear Kolmogorov PDEs via sensitivity analysis with A. Neufeld, K. Park
Applied Mathematics and Optimization, 2026+
Uniform mean estimation via generic chaining with S. Mendelson
Advances in Mathematics, 2026+
Optimal nonparametric estimation of the expected shortfall risk with S. Eckstein
SIAM Journal on Financial Mathematics, 2026+
Sensitivity of robust optimization problems under drift and volatility uncertainty with A. Neufeld, K. Park
Finance and Stochastics, 2026+
The Wasserstein space of stochastic processes with M. Beiglboeck, G. Pammer
Journal of the European Mathematical Society, 28(1):393-454, 2026
A uniform Dvoretzky-Kiefer-Wolfowitz inequality with S. Mendelson
Probability Theory and Related Fields, (193):335-374, 2025
Structure preservation via the Wasserstein distance with S. Mendelson
Journal of Functional Analysis, 288(7):110810, 2025
Empirical approximation of the gaussian distribution in R^d with S. Mendelson
Advances in Mathematics, 460:110041, 2025
Optimal non-gaussian Dvoretzky-Milman embeddings with S. Mendelson
International Mathematics Research Notices, (10):8459-8480, 2024
Sensitivity of multiperiod optimization problems with respect to the adapted Wasserstein distance with J. Wiesel
Siam Journal on Financial Mathematics, 14(2):704-720, 2023
Random embeddings with an almost Gaussian distortion with S. Mendelson
Advances in Mathematics, 400:108261, 2022
On Monte-Carlo methods in convex stochastic optimization with S. Mendelson
Annals of Applied Probability, 32(4):3146-3198, 2022
Limits of random walks with distributionally robust transition probabilities with S. Eckstein, M. Kupper
Electronic Communications in Probability, 26(28):1-13, 2021
Duality theory for robust utility maximization with M. Kupper, A. Neufeld
Finance and Stochastics, 25(3):469-503, 2021
Sensitivity analysis of Wasserstein distributionally robust optimization problems with S. Drapeau, J. Obloj, J. Wiesel
Proceedings of the Royal Society A, 477:2256, 2021
Mathematics of Operations Research, 48(4):2129-2155, 2023
Estimating processes in adapted Wasserstein distance with J. Backhoff, M. Beiglboeck, J. Wiesel
Annals of Applied Probability, 32(1):529-550, 2022
Functional inequalities for forward and backward diffusions with L. Tangpi
Electronic Journal of Probability, 25(94):1-22, 2020
All adapted topologies are equal with J. Backhoff, M. Beiglboeck, M. Eder
Probability Theory and Related Fields, 78(3):1125–1172, 2020
Adapted Wasserstein distances and stability in mathematical finance with J. Backhoff, M. Beiglboeck, M. Eder
Finance and Stochastics, 24(3):601-632, 2020
Stochastic integration and differential equations for typical paths with M. Kupper, A. Neufeld
Electronic Journal of Probability, 24(97):1-21, 2019
Robust expected utility maximization with medial limits with P. Cheridito, M. Kupper
Journal of Mathematical Analysis and Applications, 471(1-2):752-775, 2019
Pathwise superhedging on prediction sets with M. Kupper, A. Neufeld
Finance and Stochastics, 24(1):215-248, 2020
Marginal and dependence uncertainty: bounds, optimal transport, and sharpness with M. Kupper, T. Lux, A. Papapantoleon
Siam Journal on Control and Optimization, 60(1), 410-434, 2022
Computational aspects of robust optimized certainty equivalents and option pricing with S. Drapeau, L. Tangpi
Mathematical Finance, 30(1):287-309, 2020
Duality for pathwise superhedging in continuous time with M. Kupper, D. Prömel, L. Tangpi
Finance and Stochastics, 23(3):697-728, 2019
A pointwise bipolar theorem with M. Kupper
Proceedings of the American Mathematical Society, 147(4):1483-1495, 2019
Stochastic Processes and their Applications,130(2):785-805, 2020
Annals of Applied Probability, 29(1):577-612, 2019
Awards:
Prize of the Austrian Mathematical Society
Elected member of European Mathematical Society - Young Academy, 2024-2027
Prize for best PhD thesis (German Mathematical Society - probability group)
Bruti-Liberaty prize (Bachelier Finance society)
Prize for best PhD thesis (Department of Mathematics and Statistics, Univ. Konstanz)
Excellence Award (VFVH)
Funding:
Sole PI of PYP grant ``Robust Statistical Learning from Complex Data", 500.000 SGD
Sole PI of FWF grant P34743 (transferred to M. Beiglboeck due to relocation), ~400.000 Euro
Sole PI of FWF grant ESP31, ~300.000 Euro
Recent activities:
Co-organizer "Annual Summer School on Mathematical Aspects of Data Science" 2026
Co-organizer "Quantitative Finance 3-week programm (summerschool, workshop, conference)" 2026
Co-organizer "Probabilistic Mass Transport - from Schrödinger to Stochastic Analysis" 2026
Co-organizer "Optimal Transport for Complex Data" 2025
Recent teaching:
Minicourse "Geometry and Complexity in Learning" at AI in Scientific Computing
Lecturer "Project in Quantitative Finance and FinTech", QF4204, AY26/27 NUS
Lecturer "Financial Derivatives: Modelling and Computation", QF5209, AY26/27 NUS
Lecturer "Stochastic Analysis in Mathematical Finance", MA5248, AY26/26 NUS
Lecturer "Financial Derivatives: Modelling and Computation", QF5209, AY25/26 NUS
Lecturer "High-dimensional probability theory for Data Science", Uni. Vienna
Tutorials "Stochastics for Engineers", Uni. Klagenfurt
Minicourse "Introduction to mathematics of statistical learning theory" at Vienna school of mathematics
Minicourse "Introduction to geometric aspects of statistical learning theory" at NTU Singapore 5 weeks
Recent talks + travel:
2026
German Mathematical Society, Annual Meeting (talk)
Sydney Workshop in Financial Mathematics and Stochastic Control (talk)
RICAM Special Semester Mathematics of AI (minicourse)
Mathematical Statistics in the Information Age (talk)
FOCM - section Foundations of Data Science and Machine Learning (talk)
IMS Institute for Mathematical Statistics, Annual Meeting (talk)
TUM Machine Learning seminar (talk)
One world probability seminar (talk)
NYU Finance and Risk Engineering seminar (talk)
LMU applied probability seminar (talk)
Uni Vienna, Data Science seminar (talk)
AI Theory seminar (talk)
2025
QMF conference Sydney (talk)
SUTD research seminar (talk)
WU Vienna Statistics Research Seminar (talk)
BANFF workshop Bridging Theory and Practice in Finance (talk)
Eighteenth Annual Risk Management Conference (talk)
NUS QF conference (talk)
TU Graz, Colloquium in Data Science (talk)
UC Irvine, Combinatorics and Probability seminar (talk)
UC Irvine (research stay)
Stanford (research stay)
UC Berkeley (research stay)
Oxford, Stochastic analysis and mathematical finance seminar (talk)
Co-authors: Beatrice Acciaio, Julio Backhoff, Mathias Beiglboeck, Patrick Cheridito, Samuel Drapeau, Stephan Eckstein, Manu Eder, Annemarie Grass, Songyan Hou, Michael Kupper, Thibaut Lux, Gabor Lugosi, Shahar Mendelson, Ariel Neufeld, Jan Obloj, Roberto Imbuzeiro Oliveira, Gudmund Pammer, Antonis Papapantoleon, Kyunghyun Park, David Proemel, Zoraida Rico, Stefan Schrott, Ludovic Tangpi, Johannes Wiesel, Xin Zhang
Contact: bartld at nus dot edu dot sg; Office 07.09, S17 Science Drive