I am an empirical machine learning researcher with an interdisciplinary background spanning deep learning, generative models, LLM evaluation, and quantitative social science.
I have worked on computer vision and language-model systems, with hands-on experience in PyTorch pipelines, synthetic data generation, and model evaluation for medical imaging and robustness studies.
My research interests center on AI safety, model evaluation, adversarial robustness, and the societal effects of advanced AI systems. I enjoy turning open-ended research questions into reproducible experiments and clear, evidence-based conclusions.
I have co-authored peer-reviewed work on diffusion-based failure diagnosis and contributed to experimental studies that examine how linguistic variation affects AI-text detector reliability.
Alongside research, I have practical backend development experience using FastAPI, SQLAlchemy, and Node.js, and I am comfortable working across data analysis, experimentation, and software implementation.
I also have experience in teaching, mentoring, and organizing technical communities, which has strengthened my communication, leadership, and cross-functional collaboration skills.
I am open to opportunities where I can contribute to applied machine learning, evaluation, and research-driven product development in a rigorous and collaborative environment.
Developed and evaluated computer vision and generative model pipelines for medical imaging and structural failure diagnosis. Built a PyTorch workflow for preprocessing, augmentation, transfer learning, and evaluation, and contributed to peer-reviewed research on diffusion-based failure diagnosis.
Analyzed survey and cross-national datasets using R for data inspection, transformation, exploratory analysis, and regression modeling. Evaluated experimental validity, interpreted statistical relationships, and documented reproducible analysis workflows.
Delivered backend functionality for a government learning-management system using FastAPI, SQLAlchemy, and Node.js. Translated user requirements into maintainable backend components and API endpoints.
Founded and organized a university-wide technical club, coordinated cross-functional teams, and secured sponsorships from technology partners and public organizations. Led events and community-building initiatives for technical education.
Provided individualized tutoring and admissions guidance for students pursuing technical degree programs. Improved programming and quantitative problem-solving skills through tailored instruction and practice.
Bachelor of Arts, Social Sciences
Expected graduation in Jun 2026. Relevant coursework includes Programming for Scientists and Engineers, Performance and Data Structures, Computer Systems and Organization, Deep Learning (audited), Calculus I, and Linear Algebra with Applications. AI/Data Science coursework GPA: 3.78/4.0.
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