Gang Yang

I am a M.S. student in Statistics at University of Michigan Ann Arbor, advised by Professor Jonathan Terhorst. I obtained B.S. in Applied Math at the School of Physical Sciences and B.S. in Computer Science at the Jacobs School of Engineering at the University of California, San Diego (UCSD).

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Schedule

Research

I am broadly interested in Applied Probability and in particular fascinated by Markov chains, Markov processes, stochastic algorithms, and their applications in a wide range of areas such as stochastic optimizations.

Teaching

At UM (Graduate Student Instructor)

Introduction to Statistical Computing STATS 306 Spring 2024 An introductory statistical computing course based on the R programming language and the tidyverse package. Topics covered include data wrangling, data visualization, basics of programming in R, and basics of statistical modeling.

Introduction to Statistics and Data Analysis STATS 250 Fall 2023, Winter 2024 An introductory course in applied statistical methodology from an analysis-of-data viewpoint. Frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis.

At UCSD (Undergraduate Teaching Assistant)

Discrete Mathematics CSE 20 Spring 2021, Summer 2021, Summer 2022, Fall 2022 Introduction to the ways logic is used in computer science: for reasoning, as a language for specifications, and as operations in computation. Concepts include sets, relations, functions, equivalence relations, partial orders, number systems, and proof methods (especially induction and recursion). Propositional and predicate logic will be introduced and applied to various computer science domains such as circuit design, databases, cryptography, and program correctness.

Introduction to Programming in Python CSE 8A Fall 2020 taught by Professor Adalbert Gerald Soosai Raj and Professor Sorin Lerner Introductory course for students interested in computer science and programming. Basics of programming including variables, conditionals, loops, functions/methods. Structured data storage such as arrays/lists and dictionaries, including data mutation. Hands-on experience with designing, writing, hand-tracing, compiling or interpreting, executing, testing, and debugging programs. Students solve relevant computational problems using a high-level programming language.

Projects (In Construction)

These include courseworks, projects and other contest work not for publishing. contents to be updated (2024.09.09). To remind myself what to put: CSE 150B, CSE 250A, CSE 158, DataHacks.

Classification of Aviation Incidence Reports for Safety Management

The problem we will be working on is the aviation incident cause prediction. We will get more than 200,000 reports and narratives after each aviation incident from 1980s and then analyze the primary problem behind each aviation incident based on the reports from officials and narratives from relevant individuals. We will apply some classical models to aim at a better access to the data (classification and information retrieval) and help aviation safety experts in their analysis (text mining). Later, we will introduce more complex models for this classification task.