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Yanhao Jin
  • Home
  • Research
    • In-context Learning for Mixture of Linear Regression
    • Meta-Learning with Generalized Ridge Regression
    • Online Statistical Inference for Stochastic Gradient-Free Algorithm
  • Teaching
    • STA 243 Computational Statistics
    • STA 131A Introduction to Probability Theory
    • STA 160 Practice in Statistical Data Science
  • CV
Yanhao Jin
  • Home
  • Research
    • In-context Learning for Mixture of Linear Regression
    • Meta-Learning with Generalized Ridge Regression
    • Online Statistical Inference for Stochastic Gradient-Free Algorithm
  • Teaching
    • STA 243 Computational Statistics
    • STA 131A Introduction to Probability Theory
    • STA 160 Practice in Statistical Data Science
  • CV
  • More
    • Home
    • Research
      • In-context Learning for Mixture of Linear Regression
      • Meta-Learning with Generalized Ridge Regression
      • Online Statistical Inference for Stochastic Gradient-Free Algorithm
    • Teaching
      • STA 243 Computational Statistics
      • STA 131A Introduction to Probability Theory
      • STA 160 Practice in Statistical Data Science
    • CV

  • Email: yahjin@ucdavis.edu

  • Phone: +1 5307618206

  • Github: https://github.com/yanhaojin

  • LinkedIn: https://www.linkedin.com/in/yahjin/

  • Google Scholar: https://scholar.google.com/citations?hl=en&user=HyKpNT8AAAAJ

Education:

    • University of California, Davis:

        • Ph.D. candidate in Statistics

        • Sept.2021-June.2026 (Expected)

        • Topic courses include Survival Analysis, Generalized Linear Mixed Models, High Dimensional Statistical Inference, Advanced Probabilities and Topological Data Analysis

    • University of California, Davis:

        • Master of Science in Statistics, Data Science Track

        • Sept.2019-June.2021

        • Courses include Mathematical Statistics, Statistical Learning and Computational Statistics

    • University of Chinese Academy of Sciences (UCAS):

        • Bachelor of Science in Mathematics and Applied Mathematics

        • Sept.2015-Jun.2019

        • Courses include Real Analysis, Linear Algebra, Abstract Algebra, Numerical Analysis and Discrete Mathematics

Technical Skills and Interests:

  • Language:  C, Python, Bash, SQL, R, LaTeX, MATLAB 

  • Packages: Tensorflow, PyTorch, Natural Language Toolkit, Scikit-learn, Numpy, Pandas, Pymanopt, Scipy, Cvxopt, ggplot2, shiny, Tidyverse, lme4, glmnet, randomForest, torch 

  •  Skills: Statistical learning, Deep learning, Transfer learning, Multi-task learning, Meta learning, In-context learning,  Convex optimization, Multi-level optimization, Manifold optimization.  

  • Models: XGBoost, Random Forest, GLM, MLP, CNN, DNN, Deep Belief Networks (DBNs), Autoencoders, transformers(TFGPT2Model), state-space models(S4, S6, H3, Mamba) 

  • Research Directions: Deep learning theory, Transformer, In-context learning, High-dimensional statistical learning, Optimization and central limit theorem 

Achievements:

  • The Second Prize Scholarship for Academic Excellence, top 4/56, UCAS, Sept.2016

  • The Second Prize Scholarship for Academic Excellence, top 5/56, UCAS, Sept.2017

  • Outstanding Student Cadre for 2016-2017 academic year, UCAS, Sept.2017

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