hi, i'm fei!

about me

I am a postdoctoral researcher at the University of Lausanne, working with Dr. Tom Beucler on AI for climate science as part of the Horizon Europe project AI4PEX. My research focuses on improving atmospheric parameterizations in Earth system models using machine learning, evaluating the performance of these hybrid models, and exploring novel approaches, such as topological data analysis (TDA), to enhance climate modeling.

I earned my Ph.D. in 2024 from the Scientific Computing and Imaging (SCI) Institute at the University of Utah, where I worked with Dr. Bei Wang on topological data analysis and scientific visualization. Broadly, my goal is to apply the theories of algebraic and computational topology to the analysis and visualization of high-dimensional scientific data.

During my Ph.D., I was also a visiting researcher with the immersive visualization group at Linköping University, Sweden, where I collaborated with Alexander Bock and Anders Ynnerman on the visualization of astrophysics. Applications of AI and TDA to astrophysics remain an ongoing interest in my research.

I earned a Bachelor of Science in Mathematics with a second major in Computational Science from American University (AU) in 2017. Topology was introduced to me by the brilliant applied mathematian Dr. Michael Robinson at AU, who encouraged and supported me at the start of my research journey in TDA.

Outside of research, I'm a dedicated salsera, an avid traveler and outdoor explorer, a bookworm, and a food lover.

education

Ph.D. in Computing, University of Utah (2019 - 2024)

B.S. in Mathematics and Computational Science, American University (2014 - 2017)

news

June 2024: Attending the AI4PEX General Assembly in Lund, Sweden.

publications

	NEOviz: Uncertainty-Driven Visual Analysis of Asteroid Trajectories. Fangfei Lan, Malin Ejdbo, Joachim Moeyens, Bei Wang, Anders Ynnerman, Alexander Bock. Under review at IEEE Visualization, 2024.
	Topological Characterization and Uncertainty Visualization of Atmospheric Rivers. Fangfei Lan, Brandi Gamelin, Lin Yan, Jiali Wang, Bei Wang, Hanqi Guo. Accepted at Eurographics Conference on Visualization (EuroVis), 2024.
	Labeled Interleaving Distance for Reeb Graphs. Fangfei Lan, Salman Parsa, Bei Wang. Under review at Journal of Applied and Computational Topology (APCT), 2024.
	Uncertainty Visualization for Graph Coarsening. Fangfei Lan, Sourabh Palande, Michael Young, Bei Wang. IEEE International Conference on Big Data (IEEE Big Data), 2022.
	Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth. Fangfei Lan, Michael Young, Lauren Anderson, Anders Ynnerman, Alexander Bock, Michelle A. Borkin, Angus G. Forbes, Juna A. Kollmeier, Bei Wang. Eurographics Conference on Visualization (EuroVis), 2021. Computer Graphics Forum, 2021.

recent talks

Tutorial: Neural Operators: Machine Learning on Function Spaces
AI4PEX General Assembly Workshop: Neural Operators for Earth System Modeling, Lund, Sweden, 2025.

Poster: HybridESM-Bench: Towards an End-to-End Benchmark for Hybrid Earth System Models.
AI4PEX General Assembly, Lund, Sweden, 2025.

Talk: HybridESMBench.
AI4PEX Webinar, 2025.

Thesis defense: Ensemble Analysis of Graphical Descriptors.
University of Utah, Salt Lake City, Utah, 2024.

Talk: Labeled Interleaving Distance for Reeb Graphs.
AMS Sectional Meeting: Special Session on Applied and Computational Topology, Albany, NY, 2024.

Talk: Labeled Interleaving Distance for Reeb Graphs.
Computational Geometry week (CG Week) Young Researchers Forum, Athens, Greece, 2024.

Poster: Topological Characterization and Uncertainty Visualization of Atmospheric Rivers.
Data Science for the Sciences, Bern, Switzerland, 2024.

Thesis proposal: Ensemble Analysis of Graphical Descriptors.
University of Utah, 2023.

Presentation: Topological Characterization and Uncertainty Visualization of Atmopheric Rivers.
Summer School on "Topological Data Analysis in Visualization", Linköping University, 2023.

Presentation: Uncertainty Visualization of Graph Coarsening.
Visualization Seminar, University of Utah, 2023.

Invited Talk: Topological Characterization of Atmopheric Rivers.
AMS Special Session on Applied Topology: Theory and Implementation, Joint Mathematics Meetings,
Boston, 2023.

organized workshops

Neural Operators for Earth System Modeling. AI4PEX General Assembly, Lund, Sweden, 2025.

AMS Special Session on Models and Methods for Sparse (Hyper) Network Science (a Mathematics Research Communities Session). Joint Mathematics Meetings, Boston, US, 2023.

awards

Participant of AMS Mathematics Research Communities (MRC) 2024 on Climate Science at the Interface Between Topological Data Analysis and Dynamical Systems Theory.

Participant of AMS Mathematics Research Communities (MRC) 2022 on Models and Methods for Sparse (Hyper)Network Science.

Participant of 2021 GRA-WP Grad Cohort for Women Workshop.

Member of Upsilon Pi Epsilon (international honor society for the computing and information disciplines).

professional experience

Postdoctoral Researcher, University of Lausanne (January 2025 - Present).

Visiting Researcher, Linköping University (October 2023 - May 2024).

Ph.D. Research Intern, Argonne National Laboratory (Summer 2022).

Bioinformatics Analyst, Medstar Health Research Institute (October 2018 - July 2019).

teaching

I have been the teaching assistant for the following undergraduate and graduate courses.

University of Lausanne:

• SA25 Introduction to Scientific Programming with Python
• SA25 Machine Learning for Earth and Environmental Sciences

University of Utah:

• CS 2100 Discrete Structures
• CS 6170 Computational Topology

American University:

• STAT 203 Basic Statistics with Calculus
• MATH 313 Calculus III