Coursework

Metabolomics for Lung Cancer Detection

As an intern at the UCSD Supercomputer Center, I am analyzing metabolomics data to identify metabolite panels for recognition of lung cancer at early stages. I use tools like PaDEL, WEKA, MetaboAnalyst, and more.

DNA Methylation Sequencing for Lung Cancer Detection

I conducted a research project that used bisulfite padlock probing to sequence methylation in lung cancer and control plasma. The goal was to identify differentially methylated regions of the genome that could be used to diagnose cancer via a non-invasive liquid biopsy test. I discovered many significant differences in methylation between lung adenocarcinoma and healthy patients. For my work, I won Grand Prize Runner Up at the Greater San Diego Science and Engineering Fair and became an ISEF finalist.

Bovine Leukemia Virus and Breast Cancer Meta-analysis

Over 2 years, I researched bovine leukemia virus and breast cancer at the UCSD Supercomputer Center. I performed a meta-analysis on the potential similarities between BLV->Breast Cancer and HTLV-1-> Adult T-cell Leukemia/Lymphoma. I published my research as first author in Microbial Pathogenesis (check Publications section).

Pesticide Degradation via Synthetic Biology

As co-captain of my school’s iGEM synthetic biology team, I’m helping with designing bacteria that can safely break down toxic pesticides.

Lung Cancer Nodule Classification with Machine Learning

I programmed a convolutional neural network in Python to classify CT scan lung nodules as cancerous or benign with 93% accuracy.

Computational Biology Data Network Construction

As a paid intern at the Su Wu Lab at the Scripps Institute, I programmed a tool to visualize interactions between biological entities. For example, if a user inputs a disease and a drug, my software automatically displays intermediate nodes and pathways in an intuitive graphical format. For example, if you enter chronic myelogenous leukemia and imatinib, the tool displays numerous pathways of genes that imatinib targets and how the genes affect chronic myelogenous leukemia.

Parkinson’s Biomarker Identification

I wrote a paper on identifying blood-based biomarkers for early detection of Parkinson’s via microarray. I used tools like StringDB, PantherDB, BART, DAVID, and more. Finally, my selected biomarker panel had an AUC ROC of .80.