Advisor: Linqing Feng

DCAN for Cell Nuclei Image Segmentation

I was supported by NSF IPAN to work with Dr. Linqing Feng at KIST. We were interested in automating cell segmentation to study micro-scale brain connectivity. This is crucial to efficiently analyze large volumes of electron microscopy (EM) images of regions such as the hippocampus. To test the efficacy of such a method, I implemented TensorFlow code for the DCAN model on human U2OS cell images.

DCAN by Chen et al. builds upon the fully convolutional network (FCN), in which semantic insight of deeper layers is combined with locality details of shallower layers. This end-to-end network can make per-pixel predictions for tasks like semantic segmentation.

ECOviz: Comparative Visualization of Time-Evolving Network Summaries

Time-evolving graphs can be observed in a variety of domains, such as neuroscience and sociology. In connectomics, we can infer temporal networks from fMRI data; the nodes are voxels (volume units of neurons) and the edges are their thresholded associations. How can we track the evolution of domain-specific communities in such graphs?

ECOviz is a system that summarizes and visualizes temporal network changes in a semi-supervised manner. Due to the small-worldness of brain networks, we used a subset of labeled nodes to inform TimeCrunch, a dynamic graph summarization algorithm. In addition to showing the topology of summary structures, ECOviz allows users to compare the effects of preprocessing parameters (i.e., threshold and time interval granularity).

Advisor: Andrew DeOrio

Sponsors: Dana Schlegel, Thiran Jayasundera

Collaborators: Ajaay Chandrasekaran, Edmond Cunningham, Levin Kim, Wenlu Yan, Xinghai Zhang, Yaman Abdulhak

Cloud-Based Ocular Disease Diagnosis

Sponsored by the Kellogg Eye Center, this was a two-semester project in the Multidisciplinary Design Program (MDP). Retinal dystrophies are inheritable disorders that require expensive genetic tests and medical expertise to diagnose. Given patient inheritance and history data, we built a web app with a data-driven model to predict retinal dystrophy diagnosis.

I developed the web app prototype (Python Flask, PostgreSQL) to input patient data and visualize aggregate statistics of the model output. The model first predicted inheritance pattern, then paired this with patient history to output the final diagnosis via an RBF kernel SVM.

Advisor: Jun Zhang

Collaborator: Yinbin Lei

Report Code
Visualization of Formal Concepts

Under Prof. Jun Zhang and visiting faculty Yinbin Lei, this was an independent study project in the department of psychology. Though algorithms to extract concept hierarchies exist, I contributed code to visualize and query the computed lattice through set operations. This work was motivated by analysis of concept formation data.

Formal concept analysis (FCA) is a method for deriving a concept hierarchy from a table of object-attribute relations. A formal concept is composed of a set of objects and their attributes. Concepts can be partially ordered into a lattice, or hierarchy, such that edges represents set closures.