Mark Endo

CS PhD Student at Stanford University

Email: markendo [at] stanford [dot] edu

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Hi! I am a first-year PhD student in Computer Science at Stanford University. My research focuses on leveraging unlabeled and unstructured data to improve model understanding for healthcare applications. I'm particularly interested in building systems that learn a detailed understanding of people's behavior and interactions with the environment.

I recently finished my BS in Computer Science at Stanford, where I received the Special Recognition Award for Outstanding Psychiatric Research for my work on Parkinson's disease gait impairment severity estimation. During my undergrad, I was fortunate to work with Prof. Ehsan Adeli, Prof. Pranav Rajpurkar, and Prof. Jiajun Wu.


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]

Other projects

BioXtract: Learning Biomedical Knowledge From General and Random Data

CS 224n Final Project


Tutorial - Knowledge Graph Embeddings: Simplistic and Powerful Representations

CS 224w Final Project

[Blog Post] [Colab]