Biography
I am enthusiastic about applying mathematical ideas to solve real-world problems, by developing interpretable statistical methods, accompanied by robust software implementations. From a statistical perspective, this interest encompasses high-dimensional data, selective inference, and supervised learning.
Download my resumé .
Interests
- Statistics
- Applied Mathematics
- Bioinformatics
Education
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Msc in Mathematics, 2023
University of Heidelberg
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BSc in Mathematics, 2020
University of Heidelberg
Experience
Research Assistant
Researching statistical methods for mutational patterns estimation with tree structures in the lab of Niko Beerenwinkel with focus on data from the Tumor Profiler.
Research Assistant
- Analyzed the role of air pollution in the race-ethnicity to premature mortality causal chain, under Pascal Geldsetzer’s guidance, leading to key insights that contribute to policy-shaping discussions.
- Spearheaded the project with minimal supervision.
- Devised and implemented a comprehensive statistical analysis in R, synthesized findings from 150 pertinent publications, wrote the initial manuscript and technical supplement, and drove the manuscript from conceptualization to successful publication.
- Harmonized geospatial and tabular data on air pollution, mortality, population numbers, and orchestrated analyses of 10 different steps.
- Executed major revisions of the manuscript and conducted new analyses, including 15 new figures, within a strict 2-month deadline as part of the ‘Revise and Resubmit’ response.
- Developed an interactive Shiny web application to visualize 17-dimensional data, enhancing collaboration and data interpretation among the research team.
- Collaborated with seven Stanford co-authors to systematically gather and integrate critical feedback throughout various project stages, driving a significant enhancement in research quality.
Exchange student
Chosen as one of two master’s students to represent the University of Heidelberg in a year-long study abroad program at Yale University. Hosted by the Applied Mathematics Program. Advised by Smita Krishnaswamy.
Research Assistant
- Developed and implemented a novel statistical method in R under the guidance of Wolfgang Huber and Nikos Ignatiadis to identify outliers in large-scale data sets, enhancing detection capabilities in the presence of high-dimensional side-information.
- Tripled statistical detection power in a high-dimensional setting by integrating Selective Inference, Machine Learning, and Empirical Bayes approaches.
- Successfully applied the developed method to genome-wide association study, identifying key genetic markers linked to diseases.
- Presented research findings at seven scientific events, including a seminar talks at Yale University and University of North Carolina at Chapel Hill and a competitively selected oral contribution at DAGStat 2022, attended by 100 scholars.
- Conducted the peer review for manuscript at Bioinformatics Advances, contributed the peer review for manuscript at Cell Biology.
Exchange student
Graduate-level courses in Functional Analysis, Algebraic Combinatorics, and Quantitative Models at Einstein Institute of Mathematics.