Email: ykei@ucsc.edu
Google Scholar
I am a Visiting Assistant Professor in the Department of Statistics at University of California, Santa Cruz (UCSC). I obtained a Ph.D. in Statistics at University of California, Los Angeles (UCLA) in June 2024, under the supervision of Prof. Oscar Hernan Madrid Padilla.
I am interested in a better understanding of complex data to tackle real-world problems. Specifically, my research aims to develop realistic models and the associated inference and learning algorithms for complex data such as time series, graphs, and language to allow useful abstraction, generation, and detection. My research has also been advised by Prof. Mark Handcock and Prof. Ying Nian Wu at UCLA and many other amazing collaborators.
My research interests are
- Generative Models
- Representation Learning
- Graph Inference
- Anomaly Detection
- Empirical Bayes Methodology
- Computer Vision & Natural Language Processing
If you interested in working on these topics, please feel free to email me.
Previously, I received a B.S. in Statistics and a B.A. in Economics at UCLA. I also interned at Amazon and Cisco.
Publications
Published papers
A Partially Separable Model for Dynamic Valued Networks
Yik Lun Kei, Yanzhen Chen, Oscar Hernan Madrid Padilla
Computational Statistics & Data Analysis 2023
PDF
YouRefIt: Embodied Reference Understanding with Language and Gesture
Yixin Chen, Qing Li, Deqian Kong, Yik Lun Kei, Song-Chun Zhu, Tao Gao, Yixin Zhu, Siyuan Huang
The IEEE International Conference on Computer Vision (ICCV) 2021 (Oral)
PDF
Preprints
Generative Model for Change Point Detection in Dynamic Graphs
Yik Lun Kei, Jialiang Li, Hangjian Li, Yanzhen Chen, Oscar Hernan Madrid Padilla
Under Review
PDF
Change Point Detection on a Separable Model for Dynamic Networks
Yik Lun Kei *, Hangjian Li *, Yanzhen Chen, Oscar Hernan Madrid Padilla
Under Review
PDF
Funded by NSF DMS-2015489
Others
Online Multi-robot Deadlock Prediction with Conditional Variational Auto-Encoder for Sequences
Yik Lun Kei, Mo Zhang, Claire Stolz, Anoop Aroor, Yulin Zhang, Yuchen Gao
Amazon Robotics Science Summit (Poster Presentation)
* denotes equal contribution
Teaching
STAT 132: Classical and Bayesian Inference
Lectures
Work Experience
Cisco
Software Engineer Intern (2024)
Amazon Robotics
Data Scientist Intern (2023)
UCLA
Graduate Student Researcher (2019-2024)
UCLA Health
Senior Data Analyst (2017-2019)
Software
library(CPDstergm)
Github
- An R package to detect multiple change points in time series of graphs, using Separable Temporal Exponential-family Random Graph Model (STERGM). The optimization problem with Group Fused Lasso regularization on the model parameters is solved by Alternating Direction Method of Multipliers (ADMM).
Awards
UCLA Graduate Fellowship (2021-2023)
UCLA Summer Mentored Research Fellowship (2022)
Services
Journal Reviewer
Graudate Student Mentor