Inkee Jung

About Me

Welcome! a PhD candidate at Boston University working in geometry and topology, with growing expertise in machine learning and AI applications.

I'm a member of the Geometry and Physics research group in BU.

My research advisor is Professor Siu-Cheong Lau

My undergraduate studies were in mathematics department at the Yonsei University in South Korea.

You can download my resume here.

Research

I enjoy bridging abstract mathematical ideas—like geometry and topology—with practical AI applications, especially in model interpretability and explainability.

Publication & Preprints

Inkee Jung, Wonwoo Kang, and Heehyun Park. (2025). Persistent Laplacian Diagrams, arxiv
Jung, I., & Lau, S.-C. (2025). A Logifold Structure for Measure Space. Axioms, 14(8), 599. https://doi.org/10.3390/axioms14080599
I. Jung and S. -C. Lau, "Logifold: A Geometrical Foundation of Ensemble Machine Learning," 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Male, Maldives, 2024, pp. 1-6, doi: 10.1109/ICECCME62383.2024.10796884.

Talks

Teaching

2020 Fall - Present: Boston University
Teaching Assistant for Calculus I & II, Linear Algebra, Complex Variables, and Elementary Statistics.
Led problem-solving sessions and discussions for over 400 students across 8 semesters.
Summer 2023: Boston University, CAS MA 581 – Probability
Instructor for a summer course covering probability theory, Bayes' rule, random variables, distributions, moment-generating functions, the law of large numbers, and the central limit theorem.
Designed assignments and provided feedback throughout the course.
[Syllabus]
2020 Spring, Summer: Yonsei University
Teaching Assistant for Calculus I & II.
Grading and leading discussion sessions.

Selected Projects

Predicting Aviation Accident Severity - The Erdős Institute - Summer 2025
GitHub | Video | Slides
Led data extraction and cleaning by converting .mdb files to .csv and integrating event, aircraft, and sub-aircraft tables into a unified dataset. Engineered and imputed features for classification and regression tasks. Built ensemble models (XGBoost, Random Forest, Extra Trees, Histogram Gradient Boosting) with grid search and cross-validation. Achieved an F1 score of 0.459 (Extra Trees) in aircraft damage classification, significantly above the 0.316 baseline.
Predicting untrustworthy predictions and the Law of Learning Dynamics - Spring 2025 - Present
On-going project developing a geometric framework for untrustworthy samples including adversarial attacks using Logifold structures.
Using Logifold structures to detect perturbations, enhance robustness, and improve ensemble performance.
Persistent Laplacian Diagrams - Spring 2025 - Present
Developing a featurization method for the Persistent Laplacian analogous to the persistence image for persistent homology, and defining signatures of the Persistent Laplacian together with a stability analysis for the resulting Persistent Laplacian diagrams.
Constructed an explicit pair of non-isomorphic graphs that are indistinguishable by persistent homology or classical graph Laplacian spectra, but distinguishable by our Persistent Laplacian based signatures, demonstrating their additional discriminative power.
Currently extending this framework to biomolecular, point cloud, and graph datasets through a series of benchmark experiments.