Courses I Have Taken |
Fall 2005 |
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HST.508/Biophysics 170. Quantitative Genomics
- Instructors: Prof. Alvin T Kho, Prof. Leonid A Mirny, Prof. Robert C Berwick, Prof. Shamil Sunyaev, Prof. Isaac Kohane
- Key Words: Evolutionary and Population Genetics; Comparative Genomics; Structural Genomics and Proteomics; and Functional Genomics and Regulation.
- Course Project:
Positive Selection in Human Genes: Why mutations in the genes under positive selection are easier to be fixed? Is there any explanation in the codon and amino acid level? (Collaborated with: Wei Zhang & Yuan Yuan, Supervised by: Prof. Shamil Sunyaev)
- Grade: A
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Spring 2005 |
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STAT315. Computational Biology and Bioinformatics
- Instructor: Prof. Jun S. Liu
- Key Words: research seminar; DP; HMM; MCMC computing.
- Presentation:
1. A module map showing conditional activity of expression modules in cancer.
2. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. (Collaborated with Jiajun Gu)
- Grade: SAT
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STAT230. Multivariate Statistical Analysis
- Instructor: Prof. Carl N. Morris
- Key Words: Matrix Algebra and Random Vectors; Inference for multivariate normal mean& variance; Analysis of covariance structure; Multivariate Regression; Classification and clustering.
- Presentation:
Solutions for multivariate problems from the qualify exams of our department.
- Grade: A
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Fall 2004 |
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STAT225. Spatial Statistics
- Instructor: Prof. Rima Izem
- Key Words: computational methods for description, modeling and analysis for three types of spatial data: point pattern, geospatial and lattice;
- Presentation:
Model-Based Geostatistics. (Collaborated with Tingting Zhang, Wei Zhang and Taeyoung Park)
- Grade: A
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STAT321. Stochastic Modeling and Bayesian Inference
- Instructor: Prof. S. C. Samuel Kou
- Key Words: research seminar; stochastic processes; Bayesian inference and MCMC computing; stochastic differential equations; diffusion processes
- Presentation:
Decoding Human Regulatory Circuits.
- Grade: SAT
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Spring 2004 |
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STAT211. Statistical Inference
- Instructor: Prof. S. C. Samuel Kou
- Key Words: Frequency, Bayesian and decision-theoretic approaches for statistical inference; Likelihood, sufficiency, consistency, unbias; MLE, LRT, BF, CLT, delta method; computation and approximation.
- Grade: A
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STAT215. Fundamentals of Computational Biology
- Instructor: Prof. Wing H. Wong
- Key Words: statistical estimation, BLAST, multiple sequence alignment, gene regulatory motif discoveries, HMM, Gibbs sampler, phylogenetic inference, protein structures, comparative genomics, microarray data analysis, clustering and classification, database
- Course Project:
Bayesian Detection of Periodically Expressed Genes.
- Grade: A-
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STAT139. Statistical Sleuthing
- Instructor: Prof. Xiao-Li Meng
- Key Words: pros and cons of t-tools and their alternatives, multiple-group comparisons, linear regressions, model checking and refinement; statistical designs for the estimation of the effects of treatments in randomized experiments
- Course Project:
Are Stock Prices More Associated with Past Results or Expectation of Future Results? (Collaborated with Wei Zhang and Ye Li)
- Grade: A
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Fall 2003 |
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STAT220. Bayesian Data Analysis
- Instructor: Prof. Jun S. Liu
- Key Words: hierarchical and mixture models; Dirichlet process; missing data framework; data augmentation methodology; model checking, sensitivity analysis; importance sampling, rejection sampling, sequential imputation; EM algorithm, Newton-Raphson method, Metropolis-Hastings algorithms, Gibbs sampling; variable selection; non-parametric hierarchical Bayes.
- Course Project:
A Tentative Study on Periodically Expressed Genes Detection: Bayesian Approach.
- Grade: A
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STAT335. Statistical Computing Software
- Instructor: Dr. Mark Irwin
- Key Words: Unix/Linux, R/SPlus, LaTeX, Matlab, SAS
- Grade: SAT
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STAT210. Probability Theory and Mathematical Statistics
- Instructor: Prof. Carl N. Morris
- Key Words: measure theory; probability, independence and conditional probability, random variables, expectation and conditional expectation, generating functions, multivariate distributions, families of distributions, sampling distributions, convergence, limit theorems, inequalities, approximations, and simulation
- Grade: A
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STAT160. Design and Analysis of Sample Surveys
- Instructor: Prof. Alan M. Zaslavsky
- Key Words: questionnaire design and validation; SRS, stratification, clustering and multistage sampling, and unequal-probability unit sampling; unbiased estimation, ratio and regression estimation, calibration estimators, sampling weights and variance estimation methods, Taylor linearization, jackknife and bootstrap; missing data and nonresponse in surveys, weighting and imputation;
- Course Project:
1. Epidemiologic Catchment Area (ECA) Study.
2. Survey on Usage of University Health Services (Collaborated with Aaron Zimmerman, Ning Kang and Raphael Schoenle).
3. On Jackknife and Bootstrap for Variance Estimation.
- Grade: A
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Main Courses taken in Tsinghua University, Beijing, China (Average Grade: 91/100) |
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Mathematics Courses
- Functional Analysis
- Applied Stochastic Processes
- Geometry and Algebra
- Calculus
- Sets and Logic
- Stochastic Mathematical Methods
- Introduction to Complex Analysis
- Introduction to Methods of Mathematics and Physics
- Mathematical Modeling
- Numerical Analysis
- Operations Research
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Computer Science Courses
- C Language and Programming
- Fundamentals of Computer Software Technique
- Principles and Applications of Computer Drafting
- Computer Principles and Applications
- Computer Networks and Applications
- Single Chip Microcontroller Technology
- Computer Image Processing & Multi-media Technology
- Computer Simulation
- Introduction to Artificial Intelligence
- Statistical Pattern Recognition
- Virtual Reality Technology and Its Applications
- Pattern Recognition
- Introduction to Statistical Learning Theory
- Data Structures
- Analysis and Design of Applied Computer Software System
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Electronics, Control and System Theory Courses
- Principle of Circuits
- Fundamentals of Digital Electronics
- Fundamentals of Process Engineering
- Electrical Machinery and Electrical Drive
- Signals and Systems Analysis
- Automatic Control Theory
- Fundamentals of Analog Electronics
- Fundamentals of Power Electronics
- Process Measurement and Instrumentation
- Process Control
- Introduction to Systems Engineering
- Computer Control Systems
- Fundamentals of System Identification
- Topics on Control
- Design Studio in Speciality Field
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Others
- General Chemistry
- Fundamentals of Engineering Drawing
- Physics
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