Reddit is a large online forum organized into communities called subreddits. I utilized topic modeling, a family of computational methods for identifying recurring themes in text, to categorize discussions in the social anxiety subreddit. Three exploratory approaches were compared: Latent Dirichlet Allocation (LDA), which uses patterns of word frequency, BERTopic, which groups text using transformer-based representations and TopicGPT, which uses a large language model to generate and assign topics. The full dataset comprised 27,197 posts and 238,589 comments spanning roughly one year before and one year during the COVID-19 pandemic.
LDAvis output showing the number of posts within each topic (measured by size), similarity between topics (measured by distance) and the most common words within each topic.BERTopic output showing the size and similarity of 90 topics in the embedding space.Diagram of TopicGPT output showing the physical symptoms topic and the descriptions of the subtopics it generated. For TopicGPT, I analyzed a subset of 600 posts, following the method authors’ recommendation. The model generated 20 high-level topics in total.
Network Analysis of Conscientiousness
Network psychometrics offers an alternative approach to latent variable methods by modeling psychological attributes as systems of interconnected components. In this project I reimplemented a network analysis of conscientiousness and nine trait-related goals using a public dataset of 432 participants. LASSO-regularized partial correlation networks were estimated at the trait, facet and item levels along with network structure and node centrality. Simulation and bootstrap methods were also used to evaluate network replicability and stability.
Methods & Tools
R · qgraph · bootnet · Network Psychometrics · EBICglasso
The three figures show the reproduced item level network, bootstrapped edge weight confidence intervals and the edge weight difference test matrix. The second and third figures correspond to the trait network. Colored nodes represent questionnaire items and goals; green and red edges represent positive and negative partial correlations. Bootstrap figures summarize uncertainty and significant differences among estimated edges.
Deep Learning for Binary Tumor Classification in Brain MRI
Medical imaging applications often face constraints in both the availability of labeled training data and access to computational resources. This project reimplemented a lightweight convolutional neural network for binary brain tumor detection in MRI images designed with these limitations in mind. The project covered image preprocessing, data augmentation, model implementation, training, validation and performance evaluation. I discuss its performance, limitations and potential areas for improvement.
Training and validation loss (left) and accuracy (right) across 35 epochs. The dashed red line marks epoch 14, when the lowest validation loss was recorded and the model checkpoint was saved.Test set ROC curve and confusion matrix for the saved checkpoint.
Single Cell RNA-Sequence Analysis of Neuro COVID Leukocytes
For the final project of my advanced bioinformatics lab course at UCSD, I used Scanpy to reanalyze single-cell RNA sequencing data from cerebrospinal fluid (CSF)-derived leukocytes of patients with neurological COVID-19 symptoms and controls. Analysis included data cleaning and normalization, dimensionality reduction, clustering, visualization, hyperparameter tuning and cell type identification.
