Email: allen29@ucla.edu
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Github
Lectures
I am a Ph.D. candidate in the Department of Statistics & Data Science at University of California, Los Angeles (UCLA). My advisor is 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.
Previously, I interned at Amazon. I also received a B.S. in Statistics and a B.A. in Economics at UCLA.
My research interests are
- Generative Models
- Representation Learning
- Graph Inference
- Anomaly Detection
- Empirical Bayes Methodology
- Computer Vision & Natural Language Processing
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
Change Point Detection in Dynamic Graphs with Generative Model
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
Work Experience
Amazon Robotics
Data Scientist Intern (2023)
UCLA
Graduate Student Researcher (2019-present)
UCLA Health
Senior Data Analyst (2017-2019)
Teaching
Guest Lecturer
Research Design, Sampling, and Analysis (201A)
Linear Models (100C) [2 times]
Mathematical Statistics (100B) [2 times]
Teaching Fellow
Mathematical Statistics (403)
Statistical Modeling and Learning (201B)
Research Design, Sampling, and Analysis (201A)
Computation and Optimization for Statistics (102B)
Introduction to Data Analysis and Regression (101A)
Linear Models (100C)
Mathematical Statistics (100B) [8 quarters]
Introduction to Probability Theory (100A)
Introduction to Statistical Methods for Life and Health Sciences (13)
Introduction to Statistical Reasoning (10)
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) and solved by Alternating Direction Method of Multipliers (ADMM) with Group Fused Lasso regularization.
Awards
UCLA Graduate Fellowship (2021-2023)
UCLA Summer Mentored Research Fellowship (2022)
Services
Journal Reviewer
Graudate Student Mentor