LOGML Summer School
Academic mentor for advanced projects on symmetry and effective dimension in neural networks (2025) and posterior geometry and variational estimation of Bayesian complexity (2026).
Teaching & mentoring
Teaching students to see structure and work with it.
I teach mathematics, statistics, and machine learning as connected ways of reasoning. Whether in a large course or a small research team, I help students move between intuition, computation, and proof. I also help them develop the judgment to choose what a new problem needs.
Students arrive with different mathematical histories, but rigor does not require a single route in. I build explanations through geometric intuition, concrete examples, computation, and formal proof, then make the connections among them explicit.
I want students to treat questions, errors, and incomplete ideas as useful evidence. In a proof, a coding lab, or a conversation during office hours, learning to turn “something feels wrong” into a precise question is often the decisive step.
In mentoring, the aim is a gradual transfer of ownership: first make the question and technical path visible, then step back as the student learns to choose methods, evaluate evidence, and set the next question.
Research mentoring
From an open question to work the student owns.
IPAM RIPS
Academic mentor for a 2025 interdisciplinary team studying the geometry of representations in large language models, through UCLA's Research in Industrial Projects for Students program in partnership with Microsoft Research.
UCLA Statistics Club
Mentored undergraduate and master's students from 2022 to 2024, helping them turn broad interests into tractable projects, acquire technical tools, and communicate results.
Research habits
Form a question that can be tested. Run the smallest informative experiment. Say what evidence would change your mind. Explain the result clearly enough that someone else can build on it.
Selected instruction
Many audiences, one standard of care.
- Prep Camp, Master of Applied Statistics & Data Science
Lead or joint instructor for intensive mathematical and computational preparation at UCLA.
- Engineering & management statistics
Teaching assistant for model-based systems engineering, reliability, engineering management, and statistics for management decisions.
- Introductory statistical methods
Teaching assistant for life and health sciences, with an emphasis on statistical reasoning and reproducible coding.
In students' words
“Very patient and thorough with her explanations … she made the labs less daunting.”
Student evaluation
“Approachable and patient; I felt comfortable asking for additional explanation.”
Student evaluation
“She walked us through the process of fixing errors, which taught us how to debug our own code.”
Student evaluation