About Me
I recently graduated from Boston College with a Bachelor of Science in Computer Science and a minor in Finance. I currently work as a Data Scientist in the Hur Lab at Howard Hughes Medical Institute (Boston Children’s Hospital, Harvard Medical School), where I develop and apply computational and statistical methods to large-scale data. My work focuses on modeling complex patterns in high-dimensional datasets, extracting actionable insights from noisy signals, and building scalable tools for real-world data analysis. With a strong foundation in computer science and experience working with massive experimental datasets, I bring a data-first approach to solving the most difficult challenges.
Projects
FoxP3 Dimer Discovery
Customized ML pipelines revealed novel head-to-head FoxP3 binding motifs, reshaping our understanding of transcription factor interactions.
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SignalFrame Library
Engineered a lightning-fast Python package for genomic signal processing. Outperforms industry giants, now powering open-source pipelines.
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MarketMotif AI
Leveraged motif-mining algorithms on financial data to detect volatility regimes, enabling proactive risk management strategies.
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Genre & Sentiment Analysis of Song Lyrics
Used NLTK, scikit-learn, and DistilBERT to classify 150,000 songs by genre and analyze lyrical sentiment. Combined models to uncover patterns between genre and emotional tone.
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Tomato Leaf Disease Detection with CNNs
Developed clustering and CNN-based models to classify PlantVillage tomato leaf images as healthy or diseased. Focused on minimizing false negatives to aid early disease detection.
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fMRI Dementia Detection with Deep Learning
Developed a multi-model CNN pipeline (ResNet50, VGG16, Inception-V3, AlexNet) to classify dementia stages using fMRI scans. Achieved 93% accuracy with ResNet50 and visualized important brain regions using Grad-CAM.
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