Yunong Liu

MSCS Student at Stanford University

I am a Master's student in Computer Science at Stanford University, advised by Jiajun Wu.

I am working closely with Juan Carlos Niebles, Manling Li, Weiyu Liu and Cristobal Eyzaguirre on projects including grounding 4D assembly instructions in internet videos and tracking emergence in video generation models.

I received my BEng in Electronics and Computer Science from the University of Edinburgh (joint degree with Honors). During my time there, I was fortunate to work with Bonnie Webber and Xixian Liao on natural language processing research. Professor Webber is a role model for me, inspiring me with her unwavering passion and dedication to the field.

I also spent a wonderful semester and summer at the University of Texas at Austin for my exchange program and summer research internship, where I worked with Dhiraj Murthy on misinformation detection.

🎭 Fun fact: I have a twin sister who works in a completely different field - museology! It's fascinating how we've taken such divergent paths in our careers.

I am actively looking for a PhD position starting from Fall 2025. :> Feel free to check my resume or reach out to me at yunongl@stanford.edu!

Research Interests

Generative Models: Emergence and control in video generation models, temporal consistency in video generation
Visual Prompting: Investigating emergent behaviors and controllability in generative models through visual prompting
4D Grounding: 4D grounding and spatial-temporal alignment between 3D models and real-world videos
Self-Supervised Learning: Self-supervised representation learning for video understanding

Publications

Tracking Emergence in Video Generation Models

Cristobal Eyzaguirre*, Yunong Liu*, Stefan Stojanov, Adrien Gaidon, Juan Carlos Niebles, Jiajun Wu (*Alphabetically)

Ongoing ICCV Submission / Project Page

Discovered video diffusion models' emergent ability to track objects without explicit training. Developed novel intervention-based framework revealing models' implicit understanding of physical dynamics.

IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videos

Yunong Liu, Cristobal Eyzaguirre, Manling Li, Shubh Khanna, Juan Carlos Niebles, Vineeth Ravi, Saumitra Mishra, Weiyu Liu*, Jiajun Wu*.

NeurIPS Datasets and Benchmarks Track 2024 / Paper / Project Page / X (Twitter) Thread /

Created first dataset aligning real-world assembly videos with 3D models and instruction manuals. Developed methods for dense spatio-temporal correspondences in unconstrained videos.

COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic

Yunong Liu*, Nikhil Kolluri*, Dhiraj Murthy.

(*equal contribution)

JMIR Infodemiology Vol 2, No 2 (2022) / Paper

Developed hybrid framework combining machine learning with crowdsourced annotations to combat COVID-19 misinformation. Developed systematic comparison framework across classical models (SVM, LR, BNB) and pre-trained models (BERT, RoBERTa, XLNet) on 7 dataset combinations.

Selected Fun Course Projects

EMo-Mask: Emotional Controllable Motion Generation

Course Project on CS348I Computer Graphics in the Era of AI (2024 Winter)

Project Page

Just Dance Everywhere

Best Course Project Award

EE379K Introduction to Computer Vision Course (2022 Spring) at UT Austin

Project Page

Gates Building 3rd Floor Render

A+ Grade & Course Showcase Feature

CS 148: Introduction to Computer Graphics and Imaging (Autumn 2024)

Project Page | Course Showcase

Reinforcement Learning in Marrakech Game

Course Project on AA 228/CS 238: Decision Making under Uncertainty (2024 Autumn)

Awards

UT Austin Cockrell School of Engineering Fellowship (Declined), Apr 2023
Turing Scheme Funding, January 2022
Leadership in Student Opportunities Edinburgh Award, July 2021
1st Year Class Medal, awarded to the top overall student in 1st year Electronics and Electrical Engineering Discipline at University of Edinburgh, July 2020

Industry Experience

Student Tech Intern, Semiconductor Manufacturing Anomaly Detection

NXP Semiconductors, Tianjin, China | May 2021 - Aug 2021

Created validation methodology integrating domain knowledge with statistical confidence measures.
Designed adaptive thresholding mechanism to handle process variations across production lines.
Implemented ensemble detection system combining unsupervised learning methods (Isolation Forest, One-Class SVM)

Deep Learning Intern, Wind Farm Performance AI Optimization

Zealen AI (Startup), Beijing, China | Aug 2021 - Sep 2021

Helped on developing a framework combining LIDAR measurements with multi-spectral satellite imagery for wind pattern analysis.
Designed and validated forecasting system integrating physical models with learned spatial-temporal features

Other Experience

Research Assistant - NLP Project

University of Edinburgh | July 2022 - Jan 2023

Worked with Prof Bonnie Webber on discourse relation analysis:

Investigated relationships between non-canonical syntax (preposing) and discourse relations using large language models
Developed mask-filling tasks with BERT to analyze implicit discourse relations
Achieved quantitative evidence for syntactic effects on relation prediction

Helped with a publication in EACL 2024.

Academic Leadership

University of Edinburgh | 2022 - 2023

Teaching Demonstrator, InfBase (Sep 2022 - Jan 2023)
- Provided tutoring for year-1 and year-2 students in various Computer Science courses
Programme Representative (Sep 2022 - June 2023)
- Collected and coordinated weekly feedback from 3rd year E&EE students
- Served as liaison between students and faculty for curriculum improvements
Global Buddies Program Mentor (Sep 2021 - June 2022)
- Provided guidance and support for incoming international students

Volunteer Work

Sri Lanka Wildlife Conservation Society (SLWCS) | Jul 2019 - Aug 2019
- Developed strategies to mitigate human-elephant conflicts
- Conducted community outreach and education programs