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Chinmayee Athalye

About

I'm a 4th year Bioengineering PhD student at the University of Pennsylvania, working at the intersection of machine learning and biomedical research, advised by Paul Yushkevich. My work focuses on developing computational methods that bridge the gap between complex biological data and actionable insights for translational research.

Currently, I'm building deep learning approaches for multimodal postmortem brain imaging analysis in Alzheimer's disease research, working to automate the registration (high-fidelity alignment) of MRI and histology data that traditionally takes weeks to process manually. This work builds on my interdisciplinary background combining bioengineering, computer science, and hands-on experience with large-scale clinical datasets.

Previously, as a Data Scientist in Arnaout Lab at UCSF, I worked on machine learning models for rare fetal heart disease detection in ultrasound imaging, where I engineered domain-adaptive data augmentation methods to improve model performance on underrepresented clinical cases. This experience with real-world healthcare data was pivotal in shaping my understanding of how AI can be practically applied to address clinical challenges.

I completed my Master's in Computer Science at the University of California Irvine where I worked with Charless Fowlkes and Shu Kong. I received my Bachelor's in Electronics & Telecommunication Engineering from the College of Engineering Pune (COEP) in India.

I'm passionate about developing computational tools that can meaningfully impact translational research and ultimately improve patient outcomes. I'm currently seeking internship opportunities where I can contribute to cutting-edge AI applications in healthcare and biological discovery.

Publications