General information
From August 2009 to June 2010, I was studying on the Graduate School of Electrical Engineering & Computer Science (EECS) at Oregon State University. My major advisor was Dr. Thomas G. Dietterich.
Research Interests: I am interested in Medical Image Analysis, Biomedical Engineering, Machine Learning, Statistics and Computer Vision.
Former Projects:
At OSU I was working on the BugID project in the field of statistical machine learning.
Graduate Coursework at Oregon State University:
CS515: Datastructures and Algorithms
Introduction to computational complexity. Survey of data structures: linear lists, strings, trees, graphs. Representation and algorithms; analysis of searching and sorting algorithms; storage management.
CS531: Artificial Intelligence 1
Representation, reasoning, and learning with propositional representations. Propositional logic. Reasoning with propositional logic: backward chaining, Davis/Putnam, WalkSAT. Constraint satisfaction methods; Arc-consistency. Belief networks. Inference using the factoring algorithm. Propositional learning algorithms such as rules, decision trees, naive Bayes, perceptrons, neural networks. Bias-variance trade-off in parameter estimation. EM algorithm for belief networks with hidden variables.
CS534: Machine Learning
This course provides a broad introduction to machine learning and data mining. Topics include: supervised learning (discriminative/generative learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction); learning theory (bias/variance tradeoffs; VC theory; large margins); ensemble learning (bagging, boosting). If time allows, we will also cover sequential learning problems and algorithms. Lectures will discuss general issues in these topics and well-established algorithms, both from a computational aspect (how to compute the answer) and a statistical aspect (how to ensure that future predictions are accurate).
CS539: Introduction to Bayesian Networks
CS556: Computer Vision
Algorithm development for automatic interpretation of the three-dimensional world that is captured in a set of images; cameras and image formation; color; keypoint and edge detection; perceptual grouping; segmentation; shape representation; texture; object recognition; optical flow; motion estimation and tracking; and 3-D scene reconstruction from motion and stereo.