STC-QCB / Biological Physics Seminar: Rong Ma (Harvard University) "Modern Nonlinear Embedding Methods Unpacked: Empowering Biological Discoveries with Statistical Insights"
- Event Type
- Seminar/Symposium
- Sponsor
- Quantitative Cell Biology STC / Biophysics
- Location
- 3269 Beckman Institute (3rd floor tower room).
- Virtual
- Join online
- Date
- Oct 10, 2025 2:00 pm
- Speaker
- Rong Ma
- Contact
- Brandy Koebbe
- bkoebbe@illinois.edu
- Views
- 13
- Originating Calendar
- Physics - Biological Physics / iPoLS / STC-QCB Seminar
Abstract: Learning and representing low-dimensional structures from noisy, high-dimensional
data is a cornerstone of modern biomedical data science. Stochastic neighbor embedding
algorithms, a family of nonlinear dimensionality reduction and data visualization methods, with
t-SNE and UMAP as two leading examples, have become especially influential in recent years,
particularly in single-cell analysis. Yet despite their popularity, these methods remain subject to
points of debate, including limited theoretical understanding, ambiguous interpretations, and
sensitivity to tuning parameters. In this talk, I will present our recent efforts to decipher,
demystify, and improve these nonlinear embedding approaches. Our key results include a
rigorous theoretical framework that uncovers the intrinsic mechanisms, large-sample limits, and
fundamental principles underlying these algorithms; a set of theory-informed practical
guidelines for their principled use in trustworthy biological discovery; and a collection of new
algorithms that address current limitations and improve performance in areas such as bias
reduction and stability. Throughout the talk, I will highlight how these advances not only
deepen our statistical understanding but also open new avenues for biological insight.