
Deep Adversarial 3D Shape Net
10807: Topics in Deep Learning (Fall 2016)
3D shapes are a crucial but heavily underutilized resource in current computer vision research. Recently, deep learning models for 3D shapes have started to emerge because large amount of 3D shape data have become accessible. People are actively developing/finding proper 3D shape representations, such as uniform voxel representation and multiview images. With the help of geometric approaches, this research topic tries to answer  is there a better way to represent 3D shapes for training deep learning models? [pdf]


Combining Active Learning and Accuracy Estimation using Unlabelled Data
10701: Introduction to Machine Learning (Spring 2016)
How unlabelled data can be used to estimate the true accuracy of learned classifier is an important problem across different fields. It is counter intuitive because we usually need labelled data to compute the true accuracy. In this project, we explored Error Estimation model which use graphical model approach to capture the dependency of making error across classifiers in order to estimate true accuracy using only unlabelled data. Then we brought this information to active learning model to verify the usefulness of estimated accuracy. [pdf]


Shape Descriptor Design Using Spin Transformation
15869: Discrete Differential Geometry (Spring 2016)
A wellknown theory in differential geometry that mean curvature and metric should suffice to determine a surface uniquely. However, there has been no shape descriptor constructed using mean curvature and a metric. In this project, I applied Spin Transformation, mean curvature flow, and persistent barcode to construct a shape descriptor based on this theory. [pdf]


Recognition of Partially Obstructed 3D Objects from Point Cloud Data
24681: ComputerAided Design (Spring 2016)
In the 3D printing industry, a metal printing process called the depowdering process requires manually removing excess powder. In this project, we developed a framework to recognize 3D shapes from partially observed surface patches. With this framework in hand, we can then use robot arms to remove excess powder, which will significantly simplify the depowdering process. [pdf]


Fundamental Fluid Mechanics Laboratory
Undergraduate Research (2013)
In this project, the main task is to explore the phenomenon when waves propagate over a submerged bar. We conducted wave propagation experiments and used a numerical model to simulate the experimental results.


Ship Model Basin Laboratory
Undergraduate Research (2013)
Optimizing hull positions of a trimaran through conducting ship model resistance tests. Based on the results, we optimized the hull positions and predicted the ship's performance in real size.
