I'm a Master's student at Stony Brook University specializing in computer vision, computational pathology, and biomedical image analysis. I focus on building practical and research-driven solutions for real-world medical and biological applications.
I am actively seeking internships and full-time opportunities in AI, machine learning, and data analysis.
I am a Master's student in Biomedical Informatics at Stony Brook University, with a strong background in Applied Mathematics and Software Engineering. My research interests focus on computer vision, computational pathology, and biomedical image analysis, where I aim to develop data-driven solutions for real-world medical and biological problems.
I have experience in building deep learning models for image segmentation and classification, including nuclei segmentation on the PanNuke dataset and pathology image analysis on NYBB data. Recently, I have been working on FishLEN, a deep learning model for fish length estimation from images. I am particularly interested in applying machine learning to practical systems and continuously improving model performance through experimentation and analysis.
Here are a few projects that reflect my interests in research, engineering, and applied machine learning.
A deep learning project for multi-class nuclei segmentation on the PanNuke dataset using U-Net with a ResNet encoder. Conducted systematic evaluation using metrics such as Dice and IoU, and explored model improvements through architectural design and post-processing techniques.
A brief summary of my academic background.
A brief summary of my research-related experience.
I’m happy to connect about research, internships, full-time opportunities, and collaborative projects.