Sven Wang

I am an Assistant Professor (Juniorprofessor) at the Department of Mathematics of Humboldt University Berlin. I work in mathematical statistics and its intersections with analysis, data science, computation and PDE.

Research interests:

Statistical inference in complex systems (PDE/SDE/inverse problems)
Mathematics of data science
High-dimensional Bayesian inference
Social choice theory (models for democratic processes)

Email: sven.wang [at] hu-berlin [dot] de

Before joining HU Berlin, I was a postdoc at MIT (2021-2023) and obtained my PhD at Cambridge University (2021) under the supervision of Richard Nickl. From 2024-2029, I am elected member of the Junge Akademie which fosters interdisciplinary and societal dialogue.

Google Scholar CV (PDF)

Teaching: In the summer semester 2025, I am teaching Mathematical Statistics (Moodle page).

Seminars: I co-organize the WIAS Berlin Mathematical Statistics Seminar and the HU Mathematical Statistics Group seminar.

Research Group: Oleg Butkovsky (Postdoc, 2025-), Dennis Nieman (Postdoc, 2024-), Duc Ly Hoang (PhD student, 2025-)

Projects as (co-)PI:

DFG Special Priority Programme Foundations of Deep Learning (project on Operator Learning) Link
DFG Collaborative Research Center 388 Rough paths, stochastic dynamics and related fields (project on Statistics for SDEs) Link
Excellence Cluster MATH+ (project EF6-2 on Social Choice Theory) Link

News:

Jan 2025: Upcoming Oberwolfach workshops Data Assimilation: From Mathematical and Statistical Foundations to Applications (2025/02) and Frontiers of Statistics and Machine Learning (2025/03)
Jan 2025: Paper Statistical algorithms for low-frequency diffusion data: A PDE approach accepted to Annals of Statistics
Jan 2025: Paper Wasserstein Distributionally Robust Gaussian Process Regression and Linear Inverse Problems accepted to Annals of Applied Probability

Papers and Preprints

[15] Statistical Learning Theory for Neural Operators (with N. Reinhardt and J. Zech)

Preprint (2024)

[14] Statistical algorithms for low-frequency diffusion data: A PDE approach (with M. Giordano)

Annals of Statistics, to appear

[13] Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes (with X. Zhang, J. Blanchet, Y. Marzouk and V.A. Nguyen)

Preprint (2023)

[12] Distribution learning via neural differential equations: a nonparametric statistical perspective (with Y. Marzouk, R. Ren and J. Zech)

Journal of Machine Learning Research (2024)

[11] Manipulation-Robust selection of Citizens' Assemblies (with B. Flanigan and A. Procaccia)

Proceedings of AAAI (2024)

[10] Infinite-dimensional diffusion models for function spaces (with J. Pidstrigach, Y. Marzouk, S. Reich)

Journal of Machine Learning Research, to appear

[9] Voters with Stakes can Ward Off Bad Candidates (with B. Flanigan and A. Procaccia)

Foundations of Responsible Computing (2023)

[8] On free energy barriers in Gaussian priors and failure of MCMC for high-dimensional unimodal distributions (with A.S. Bandeira, A. Maillard, and R. Nickl)

Philosophical Transactions of the Royal Society A 381 (2023)

[7] Distortion under Public-Spirited Voting (with B. Flanigan and A. Procaccia)

Economics and Computation (2023)

[6] On minimax density estimation via measure transport (with Y. Marzouk)

Preprint (2022)

[5] Wasserstein Distributionally Robust Gaussian Process Regression and Linear Inverse Problems (with X. Zhang, J. Blanchet, Y. Marzouk, V.A. Nguyen)

Annals of Applied Probability, to appear

[4] Laplace priors and spatial inhomogeneity in Bayesian inverse problems (with S. Agapiou)

Bernoulli (2024)

[3] On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms (with R. Nickl)

Journal of the European Mathematical Society (2024)

[2] Convergence rates for Penalised Least Squares estimators in PDE-constrained regression problems (with R. Nickl and S. van de Geer)

SIAM/ASA Journal of Uncertainty Quantification (2020)

[1] The nonparametric LAN expansion for discretely observed diffusions

Electronic Journal of Statistics (2019)

[A] Statistical inference and computation in PDE models

PhD thesis, University of Cambridge (2021)

Miscellanea

I love playing piano, singing and composing music. Some groups I've been lucky to be part of:

The wonderful Pembroke lieder scheme where I studied with Joseph Middleton
Michael Schopper, my mentor and singing teacher
I had a great time being staff collaborative pianist for the MIT music deparment
Here is me playing some late Schubert in 2019, recorded in Trinity College Chapel
Munich Bach Choir

In former years, I was also very active outdoors.

In 2015, I biked through 22 European countries for 6 months (with my very good friend Niklas from Berlin). We paired our trip with a fundraising project towards medical care for refugees from the war in Syria.
In 2012, my friend Nick came up with the glorious idea of hiking across the alps with a donkey (named Thomas) from our German hometown Aschaffenburg to the Mediterranean Sea in Italy. It was truly life-changing for 16-year-old me. Thomas had a solar panel on his back, and we funded our trip by playing the accordion in the streets. We were (unintentionally) discovered by the Italian media amidst an apparent summer drought of stories to report about.

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