JSB UCLA
The JSB lab is led by the PI, Dr. Jingyi Jessica Li, with around ten highly motivated graduate and undergraduate students. Our research is at the junction of statistics and biology, as our lab name JSB represents. We focus on developing statistical and computational methods motivated by important questions in biomedical sciences and abundant information in big genomic and health related data. On the statistical methodology side, our example interests include association measures, high-dimensional variable selection, and classification metrics. On the biomedical application side, our example interests include next-generation RNA sequencing, comparative genomics, and information flow in the central dogma.
STATS 203 - Large Sample Theory (Spring 2025) Lec17: Wald, Score, Likelihood-ratio Test; M-estimator
STATS 203 - Large Sample Theory (Spring 2025) Lecture 16: Efficiency
STATS 203 - Large Sample Theory (Spring 2025) Lecture 15: MLE theory 2
STATS 203 - Large Sample Theory (Spring 2025) Lecture 14: MLE theory 1
STATS 203 - Large Sample Theory (Spring 2025) Lec 13: density estimation; max likelihood estimation
STATS 203- Large Sample Theory (Spring 2025) Lecture 12: statistical functionals; density estimation
STATS 203 - Large Sample Theory (Spring 2025) Lecture 11: Bootstrap
STATS 203 - Large Sample Theory (Spring 2025) Lecture 10: bootstrap
STATS 203 - Large Sample Theory (Spring 2025) Lecture 9: U statistic
STATS 203 - Large Sample Theory (Spring 2025) Lecture 8: sample quantile; statistical functional
STATS 203 - Large Sample Theory (Spring 2025) Lecture 7: extreme order statistic; sample quantile
STATS 203 - Large Sample Theory (Spring 2025) Lecture 6: Delta method; Extreme order statistic
STATS 203 - Large Sample Theory (Spring 2025) Lecture 5: CLT for stationary m-dependent sequence
STATS 203 - Large Sample Theory (Spring 2025) Lecture 4: Central limit theorem; Lindberg-Feller Thm
STATS 203 - Large Sample Theory (Spring 2025) Lecture 3: Law of large numbers; consistency
STATS 203 - Large Sample Theory (Spring 2025) Lecture 2: Modes of Convergence
STATS 203 - Large Sample Theory (Spring 2025) Lecture 1: Mathematical Foundations
STATS M254 - Statistical Methods in Computational Biology (Winter 2025) Lecture 18
STATS 205 - Hierarchical Linear Models (Winter 2025) Lecture 18
STATS M254 - Statistical Methods for Computational Biology (Winter 2025) - Lecture 11
STATS 205 - Hierarchical Linear Models (Winter 2025) - Lecture 11
STATS M254 - Statistical Methods for Computational Biology (Winter 2025) - Lecture 4
STATS 205 - Hierarchical Linear Models (Winter 2025) - Lecture 4
Guanao Yan: Categorization of 33 computational methods to detect spatially variable genes (SVGs)
STATS M254 - Statistical Methods for Computational Biology (Winter 2025) - Lecture 3
STATS 205 - Hierarchical Linear Models (Winter 2025) - Lecture 3
STATS 205 - Hierarchical Linear Models (Winter 2025) - Lecture 2
STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 16: review
STATS 205 - Hierarchical Linear Models (Spring 2024) - Lecture 15: linear mixed model
STATS M254 - Statistical Methods in Computational Biology (Spring 2024) - Lecture 16: review