I am on the job market. My email is james.hong [at] cs.stanford.edu
I am a final year computer science Ph.D. student in the Graphics Lab at Stanford University advised by Prof. Kayvon Fatahalian. I have research experience in computer vision and machine learning, and systems (B.S / M.S. at Stanford).
My hobbies include photography, travel, computers, and collecting interesting facts and trivia.
I work on applied computer vision to build novel applications from real-world images and video. At Stanford, I developed algorithms and efficient systems to understand fine-grained actions in sports, analyze a decade of TV news video, and learn aesthetic priors from stock photos.
My projects often start from unstructured and unlabeled collections of in-domain data and involve generating large quantities of weak supervision for the task. [Full CV]
Future research: I am interested in applying weakly supervised learning and data generation to new domains, such as image and video editing; multimodal and 3D perception; and model safety.
James Hong, Lu Yuan, Michaël Gharbi, Matthew Fisher, and Kayvon Fatahalian
AAAI Conference on Artificial Intelligence (AAAI) 2024
Project website / Paper / Code
TL;DR: We turn a stock photo database into a weakly-labeled dataset for learning what makes an aesthetically pleasing composition. Despite being only weakly-supervsied, our system outperforms supervised methods trained on large, crowd-annotated datasets.
Lu Yuan, James Hong, Vishnu Sarukkai, and Kayvon Fatahalian
SyntheticData4ML Workshop @ NeurIPS 2023
TL;DR: We synthesize weakly-labeled scenes around objects using foundation models for image generation. By synthesizing a large number of scenes, we train a model that can better understand where objects can and cannot go.
James Hong, Haotian Zhang, Michaël Gharbi, Matthew Fisher, and Kayvon Fatahalian
European Conference on Computer Vision (ECCV) 2022
Project website / Paper / Code
TL;DR: We propose an efficient neural network for processing every frame of a video to detect very temporally fine-grained events at the granularity of a single frame.
James Hong, Matthew Fisher, Michaël Gharbi, and Kayvon Fatahalian
International Conference on Computer Vision (ICCV) 2021
Project website / Paper / Code
TL;DR: We generate large amounts of dense weak-supervision on unlabeled and untrimmed sports video in order to learn more robust features for recognizing fine-grained actions.
James Hong, Will Crichton, Haotian Zhang, Daniel Y. Fu, Jacob Ritchie, Jeremy Barenholtz, Ben Hannel, Xinwei Yao, Michaela Murray, Geraldine Moriba, Maneesh Agrawala, and Kayvon Fatahalian
Conference on Knowledge Discovery and Data Mining (KDD) 2021
Project website / Paper / Code
TL;DR: We conduct an analysis of the visual content in 300,000 hours of US cable TV news. I built infrastructure to process the video and the TV news analyzer, a public interface and query engine for interactive search and visualization of all of the data.
Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein
Symposium on Networked Systems Design and Implementation (NSDI) 2020
Project website / Paper / Code
Awards: USENIX NSDI Community Award, IRTF Applied Networking Research Prize
TL;DR: A public research platform for conducting video streaming experimentation. I designed the JS/HTML web client, which streams video and audio chunks over a websocket connection.
Dan Fu, Will Crichton, James Hong, Xinwei Yao, Haotian Zhang, Anh Truong, Avanika Narayan, Maneesh Agrawala, Christopher Ré, and Kayvon Fatahalian
AI Systems Workshop @ SOSP 2019
Prior to graduate school, I researched systems and networking for the Internet of Things, under the guidance of Prof. Philip Levis.
James Hong, Amit Levy, Laurynas Riliskis, and Philip Levis
Conference on Internet of Things Design and Implementation (IoTDI) 2018
Holly Chiang, James Hong, Kevin Kiningham, Laurynas Riliskis, Philip Levis, and Mark Horowitz
Conference on Internet of Things Design and Implementation (IoTDI) 2018
Amit Levy, James Hong, Laurynas Riliskis, Philip Levis, and Keith Winstein
Conference on Mobile Systems, Applications, and Services (MobiSys) 2016
Laurynas Riliskis, James Hong, and Philip Levis
IoT-App Workshop @ Sensys 2015
Research Assistant at Stanford University
2017 - present
Computer Science, Graphics Lab
Research Intern at Adobe
Summer 2020
Creative Intelligence Lab
Software Engineering Intern at Rubrik
Summer 2016, 2017
Security team
Software Engineering Intern at LinkedIn
Summer 2015
Data Analytics Infrastructure
Software Development Intern at PlayStation (SNEI)
Summer 2014
Experimentation Platform
TA for courses in computer systems, graphics, algorithms and NLP while at Stanford.
CS 149: Parallel Computing
Autumn 2022, 2023
CS 248: Interactive Computer Graphics
Winter 2022
CS 244: Advanced Topics in Computer Networking
Spring 2017
CS 144: Introduction to Computer Networking
Autumn 2015, 2016
CS 224N/D: Natural Language Processing with Deep Learning
Spring 2016, Winter 2017
CS 161: Design and Analysis of Algorithms
Winter 2016, Summer 2018
I like to travel and take pictures, especially in places with interesting history and landscapes. Here are some recent wanderings.