Selected Courses
STAT Ph.D. Qualifying Exam (Aug, 2020)
Subject | Theory | Computation | Probability | Methods |
---|---|---|---|---|
Score | 2.5/3 | 2.5/3 | 2.0/3 | N.A. |
Graduate Study
Course Code | Course Title | Score |
---|---|---|
STAT69500 | Data Science For Deep Analysis | A+ |
STAT69500 | Deep Learning & Statistics | A |
STAT69500 | Machine Learning for Dynamic Systems | A |
STAT52800 | Mathematical Statistics | A |
STAT55300 | Linear Models | A |
STAT54500 | Computational Statistics I | A |
STAT54600 | Computational Statistics II | A |
STAT52500 | Intermediate Statistical Method | A |
CS59200/STAT59800 | Hands-On Learning Theory | A+ |
CS 57700 | Natural Language Processing | A |
CS 54100 | Database Systems | A+ |
CS 69000 | Statistical Machine Learning II | A |
CS 58000 | Algorithms Design Anly and Impl | A |
Home Study
Source/Year | Course Title | Certificate |
---|---|---|
UMD CMSC 858G/2022 | Bandits Experts and Games | N.A. |
Coursera/2021 | Operating Systems | cert |
edX/2018 | Robotics: Vision Intelligence and Machine Learning | N.A. |
Undergraduate Study (Advanced Courses)
Course Code | Course Title | Score |
---|---|---|
MATH4400 | Teichmuller Maps (Undergrad Research) | A |
MATH425 (UPenn) | Partial Differential Equations | A |
MATH465 (UPenn) | Differential Geometry | A |
MATH4050 | Real Analysis | A- |
MATH3360 | Mathematical Imaging | A |
MATH3230 | Numerical Analysis | A |
MATH3230 | Computational and Applied Mathematics | A |
STAT430 (UPenn) | Probability | A+ |
CSCI3160 | Design and Analysis of Algorithms | A- |