interested in large language models and agentic AI, with a developing focus on building reliable, research-oriented AI systems for quantitative finance and healthcare. Particularly interested in applications at the intersection of LLM/agent workflows and medical AI, including medical imaging, brain and neuroscience, cardiac imaging, and aging/gerontology. Motivated to contribute to interdisciplinary research that combines AI systems, data-driven modeling, and clinically meaningful applications.
Programming Languages: Python, SQL, R, Stata
Machine Learning & Deep Learning: PyTorch, machine learning workflows, deep learning fundamentals, model training and evaluation
LLM / Agent Systems: LLM application development, LangChain and LangGraph application, retrieval-augmented generation pipelines, API calling, prompt engineering, agent workflow prototyping, model context protocol , LLM evaluation, ablation experiments
Tools & Libraries: Pandas, NumPy, scikit-learn, Matplotlib, Jupyter Notebook, Git
Research Skills: literature review, empirical analysis, technical writing, reproducible research workflows
Medical Imaging & Multimodal AI: medical imaging analysis, multimodal clinical data modeling, image-text integration, computer vision for biomedical applications, imaging-aware clinical AI workflows
Clinical Data & Research: clinical data preprocessing, electronic health record data handling, clinical text processing, benchmark analysis, experimental design, empirical evaluation, result interpretation
Academic Interest: Calculus, Linear algebra, Statistical Probability
Daily Interest: Game mechanism, Chess
Social Interest: Socializing and collaborating with AI people