Coding
I work on AI/ML research and applied software projects, with a focus on building, testing, and evaluating intelligent systems.
My current work explores how machine learning models learn useful representations, align different types of data, and perform in retrieval-based tasks. I am especially interested in:
- π§ Multimodal learning
- π Retrieval systems
- π Model evaluation
- 𧬠Representation learning
- π₯ Medical AI
- π οΈ Applied AI systems
I like building projects that do not stop at:
"The model trained successfully."
I care about what the model actually learned, how it behaves, where it fails, and whether the evaluation proves meaningful progress.
A diagnostic project for studying embedding-space behavior using neighborhood preservation, clustering behavior, graph connectivity, and spectral structure.
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A controlled benchmark comparing MLP and FT-Transformer-style tabular encoders for EHR-style retrieval tasks.
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An experiment studying how false negatives affect contrastive retrieval and how loss design changes model behavior.
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A project exploring pseudo-pairing, pair quality, and matching strategies in multimodal retrieval experiments.
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A controlled medical AI-style project studying CXR-text retrieval and multimodal alignment.
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A natural-language movie discovery web application built with Next.js, TypeScript, Tailwind CSS, and movie APIs.
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AI/ML: Python, PyTorch, NumPy, Pandas, Scikit-learn, Matplotlib
Software Development: TypeScript, Next.js, Tailwind CSS, Git, GitHub
Research Workflow: Experiment design, metric analysis, reproducible documentation, result interpretation
Sample size, pairing, loss design, and split behavior Evaluation Metrics
Recall@K, lift-over-random, positive-pair similarity Embedding Analysis
Geometry, clustering, trustworthiness, spectral diagnostics Working Systems
Python ML pipelines, GitHub docs, Next.js web apps
I am currently building a portfolio of AI/ML and applied software projects focused on model behavior, evaluation, retrieval, representation learning, and practical AI systems.