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PhD Student in Mathematics @ Humboldt University Berlin
PhD Student in Mathematics @ Humboldt University Berlin
Hi, my name is Duc, I am a first year PhD student in mathematics at Humboldt University of Berlin and a Phase II Scholar at the Berlin Mathematical School, where I am currently advised by Prof. Sven Wang. My project is on Operator Learning, which leverages methods from ML and statistics for computation and inference in models based on differential equations. The project is jointly advised by Prof. Jakob Zech and Dr. Evelyn Herberg at Heidelberg University.
I did my Bachelor’s degree in Mathematics and Physics at Ulm University and obtained my Master’s degree in a joint program of Humboldt University, Free University and Technical University of Berlin under the supervision of Prof. Nicolas Perkowski, working on energy solutions of fractional stochastic partial differential equations.
I am also on the board of BLISS e.V., a student-initiative focused on connecting students, researchers and early-career professionals in AI and ML, where I am responsible for partnerships/sponsoring. We offer the largest public research seminar series related to ML in Germany, inviting leading researchers from academia and industry to give talks for a broad audience. Additionally, we offer reading groups and regular hackathons. Let me know if you are interested in co-organizing events!
Reseach Interests
I work in the intersection of PDE theory and probability theory/statistics, with a broad interest in dynamical systems under the influence of randomness, including (stochastic and deterministic) ordinary and partial differential equations arising in the natural sciences. Previously, I have worked on stochastic partial differential equations that arise as scaling limits of interacting particle systems with long-range jumps.
In my PhD, I will be investigating problems at the intersection of (nonparametric) statistics and partial differential equations, with the goal of providing mathematical guarantees for modern methods in Scientific Machine Learning. I am particularly interested in the mathematical and statistical and theoretical foundations for Operator Learning, Data Assimilation and Statistical Optimal Transport.
News
June 2025: was invited and will be attending a SLMath summer school on Statistical Optimal Transport at UC Berkeley
May 2025: gave a talk on Fluctuations in Long-Range Particle Systems and Energy Solutions of Fractional SPDEs based on the results of my Master's thesis at the research seminar for stochastics at FU Berlin
April 2025: will attend a workshop on Uncertainty Quantification at Oberwolfach
March 2025: Officially started my PhD at Humboldt University of Berlin, where I will be working on a project on Operator Learning, as part of the DFG Special Priority program on Foundations of Deep Learning
March 2025: attended the GPSD 2025 in Dresden
February 2025: gave a talk at the BMS student conference based on my Master's thesis on Energy Solution for Fractional SPDEs
January 2025: Just submitted my Master's thesis on Fluctuations of Long-Range Particle Systems and Energy Solutions of Fractional SPDEs
November 2024: attended the annual meeting on Foundations of Deep Learning at LMU Munich
September 2024: attended a conference on Nonlinear Statistical Inverse Problems at the University of Cambridge
July 2024: attended the 9th GAMM Juniors Summer School on Uncertainity Quantification and SPDEs at ETH Zurich
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