Mark Endo

CS PhD Student at Stanford University

Email: markendo [at] stanford [dot] edu

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Hi! I am a PhD student in Computer Science at Stanford University, advised by Prof. Serena Yeung-Levy and Prof. Fei-Fei Li. My research focuses on foundational understanding of multimodal machine learning and its applications in healthcare. My research is graciously funded by the NSF Graduate Research Fellowship Program.

Before starting my PhD, I received my BS in Computer Science at Stanford. During my undergrad, I was fortunate to work with Prof. Ehsan Adeli, Prof. Pranav Rajpurkar, and Prof. Jiajun Wu.

Research

Downscaling Intelligence: Exploring Perception and Reasoning Bottlenecks in Small Multimodal Models

Under Review

Mark Endo, Serena Yeung-Levy

[Project Page] [Paper] [Code]

Systematic Evaluation of Large Vision-Language Models for Surgical Artificial Intelligence

Under Review

Anita Rau, Mark Endo, Josiah Aklilu, Jaewoo Heo, Khaled Saab, Alberto Paderno, Jeffrey Jopling, F. Christopher Holsinger, Serena Yeung-Levy

[Paper] [Code]

Feather the Throttle: Revisiting Visual Token Pruning for Vision-Language Model Acceleration

International Conference on Computer Vision (ICCV) 2025

Mark Endo, Xiaohan Wang, Serena Yeung-Levy

[Project Page] [Paper] [Code] [Poster] [Presentation]

Data-Driven Discovery of Movement-Linked Heterogeneity in Neurodegenerative Diseases

Nature Machine Intelligence 2024

Mark Endo, Favour Nerrise, Qingyu Zhao, Edith V. Sullivan, Li Fei-Fei, Victor W. Henderson, Kilian M. Pohl, Kathleen L. Poston, Ehsan Adeli

[Paper] [Code]

Motion Question Answering via Modular Motion Programs

International Conference on Machine Learning (ICML) 2023

Mark Endo*, Joy Hsu*, Jiaman Li, Jiajun Wu

[Project Page] [Paper] [Code] [Poster]

Evaluating Progress in Automatic Chest X-Ray Radiology Report Generation

Patterns - Cell Press [September 2023 Issue Cover]

Feiyang Yu*, Mark Endo*, Rayan Krishnan*, Ian Pan, Andy Tsai, Eduardo Pontes Reis, Eduardo Kaiser Ururahy Nunes Fonseca, Henrique Min Ho Lee, Zahra Shakeri Hossein Abad, Andrew Y. Ng, Curtis P. Langlotz, Vasantha Kumar Venugopal, Pranav Rajpurkar

[Paper] [Data] [HMS Blog]

GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation

Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 [Special Recognition Award for Outstanding Psychiatric Research]

Mark Endo, Kathleen Poston, Edith Sullivan, Li Fei-Fei, Kilian Pohl, Ehsan Adeli

[Paper] [Code] [Poster]

Retrieval-Based Chest X-Ray Report Generation Using a Pre-trained Contrastive Language-Image Model

Machine Learning for Health (ML4H) 2021

Mark Endo*, Rayan Krishnan*, Viswesh Krishna, Andrew Y. Ng, Pranav Rajpurkar

[Paper] [Code] [ML4H Content]

CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation

Medical Imaging with Deep Learning (MIDL) 2021

Soham Gadgil*, Mark Endo*, Emily Wen*, Andrew Y. Ng, Pranav Rajpurkar

[Paper] [Code] [OpenCV Webinar] [Spotlight Presentation] [MIDL Content]