hi, i'm fei!

about me

My name is Fangfei Lan. I am a PhD student at the Scientific Computing and Imaging Institute (SCI), University of Utah, working with Bei Wang on topological data analysis (TDA) and data visualization. I was initially drawn to topology because of fascinating objects such as the mobius strip and the Klein bottle. Now I study topology because I believe the "shape" of data matters. My research goal is to apply theories of algebraic and computational topology to analyzing high dimensional scientific data using visualization.

I am currently a visiting PhD student at the immersive visualization group at Linkôping University, Sweden. I work with Alexander Bock and Anders Ynnerman on the visualization of astrophysics.

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.

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

I'm currently looking for a Postdoc position starting at the beginning of 2025 on Machine Learning, TDA, and Scientific Visualization.

education

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

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

news

June 2024: Attending the MRC conference on Climate Science at the Interface Between Topological Data Analysis and Dynamical Systems Theory in Buffalo, NY, USA.

June 2024: Presenting my work on the Labeled Interleaving Distance for Reeb Graphs at the Young Researchers Forum at Computational Geometry week (CG Week) in Athens, Greece.

May 2024: Presenting my work on the Uncertainty Visualization of ARs at Eurographics Conference on Visualization (EuroVis) in Odense, Denmark.

April 2024: Presenting a poster at Data Science for the Sciences in Bern, Switzerland.

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

Poster: Topological Characterization and Uncertainty Visualization of Atmospheric Rivers.
Data Science for the Sciences, Bern, Switzerland, 2023.

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: Uncertianty 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

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

awards

Participant of AMS 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

Visiting Researcher, Linköping University (October 2023 - present).

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 Utah:

• CS 2100 Discrete Structures
• CS 6170 Computational Topology

American University:

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