I am a Tenure Track Assistant Professor in the department of Statistics at Columbia University. I obtained my PhD at Maastricht University, the Netherlands, in December 2016. Before joining the Department of Statistics at Columbia university, I held a postdoctoral position in mathematical statistics at the Ruhr University in Bochum, Germany.
My research interests lie at the intersection of mathematical statistics, probability theory, functional analysis, and metric geometry, with a central focus on establishing the theoretical foundations of metric-space-valued and nonstationary stochastic processes.
Broadly, my research addresses sequential measurements of complex mathematical structures—such as curves, surfaces, manifolds, random sets, or operators—that exhibit changes in their probabilistic structure over time. Rather than forcing such data into vector spaces through artificial embeddings that potentially introduce geometric distortion, my approach is to model these time-dependent random objects intrinsically, capturing the delicate interplay among geometry, topology, stochastic dependence, and nonstationarity.
My early work introduced locally stationary functional time series, time-varying spectral representations, dimension reduction, and approximating martingale limit theory under mild dependence conditions for random operators on Hilbert spaces. Recognizing that Hilbertian embeddings distort the intrinsic geometry of nuclear operators and fail to preserve the continuity of essential linear functionals, I subsequently built a comprehensive theory directly for random elements taking values in the non-commutative Banach space of trace-class operators—developing functional-analytic tools, weak invariance principles, and statistical testing frameworks natively.
Moving beyond vector space embeddings entirely, my recent research focuses on intrinsic analysis of processes with values in Polish spaces. I establish native geometric and measure-theoretic foundations to obtain tractable, structure-preserving separating classes, as well as corresponding limit theory for the associated nonstationary random fields—providing the mathematical and statistical framework required to model complex topological and geometric data arising in contemporary scientific applications, from analyzing cell differentiation trajectories and neurological pathways to tracking evolving atmospheric structures.
Since 2023, I am Associate member of the Herbert and Florence Irving Institute for Cancer Dynamics
I was the recipient of the Itô prize in 2021.
Between 2020-2025, I served as Associate Editor for Statistical Inference for Stochastic Processes
Since 2023, I am Associate Editor for Journal of the American Statistical Association Reviews