Jun Yuan

Jun Yuan (袁珺)

My name is Jun Yuan. I'm currently working at Apple  Vision Products Group. I design and develop interactive visual analytics systems to help machine learning model developers understand their model's behaviors and performance.

I obtained my Ph.D. degree from New York University in Computer Science, advised by Prof. Enrico Bertini at Visualization Imaging and Data Analysis Center (VIDA) Lab. My research interest lies in the intersection of Explainable AI (xAI) and Data Visualization. Before joining NYU, I received my bachelor’s degree in Software Engineering from Fudan University, Shanghai, China.

CV / Google Scholar / LinkedIn

News

[Mar 2025] Our paper Towards a Better Evaluation of 3D CVML Algorithms: Immersive Debugging of a Localization Model got accepted by EuroVis'25.

[Jan 2025] Our paper VibE got accepted by IUI'25.

[June 2024] I'm giving a guest lecture at University of California, Berkeley, class DS100 Data, for “Visualization II”.

Research

	VibE: A Visual Analytics Workflow for Semantic Error Analysis of CVML Models at Subgroup Level Jun Yuan, K. Miao, H. Oh, I. Walker, Z. Xue, T. Katolikyan, M. Cavallo. ACM IUI, 2025 arXiv / paper By leveraging large foundation models (such as CLIP and GPT-4) alongside visual analytics, VibE enables developers to semantically interpret and analyze CVML model errors. This interactive workflow helps identify errors through subgroup discovery, supports hypothesis generation with auto-generated subgroup summaries and suggested issues, and allows hypothesis validation through semantic concept search and comparative analysis.
	Towards a Better Evaluation of 3D CVML Algorithms: Immersive Debugging of a Localization Model Tica Lin, Jun Yuan, Kevin Miao, Isaac Walker, Tigran Katolikyan, Marco Cavallo. Computer Graphics Forum, 2025 paper In this paper, we explore the application of Immersive Analytics (IA) methodologies to enhance the debugging process of 3D CVML models. Based on the insights we gained from in-depth interviews with eight CVML engineers, we propose a novel immersive analytics system for debugging an indoor localization algorithm and present it in Apple Vision Pro.
	Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini. IEEE Transactions on Visualization and Computer Graphics (TVCG) arXiv / video / github / evaluation In this paper, we contribute SURE, a visual analytics system that integrates an algorithmic and interactive solution to generate and visualize surrogate rules to help people understand model behaviors. Our validation studies with this system leads to many interesting findings, including a task taxonomy for rule analysis, that can be used for visualization design and future research.
	iSEA: An Interactive Pipeline of Semantic Error Analysis for NLP Models Jun Yuan, Jesse Vig, Nazneen Rajani. ACM IUI, 2022 arXiv / paper / demo / github iSEA is an interactive pipeline for Semantic Error Analysis in NLP Models, which automatically discovers semantically-grounded subpopulations with high error rates in the context of a human-in-the-loop interactive system.
	SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level Jun Yuan, Gromit Yeuk-Yin Chan, Kyle Overton, Brian Brian, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva. IEEE Computer Graphics & Applications (Special Issue on Human-Centered Visualization Approaches to AI Explainability), 2022 demo video / paper SUBPLEX is an interactive visualization widget embedding in Jupyter notebook that enables users to explore and validate local explanations of a model at the sub-population level.
	Context Sight: Model Understanding and Debugging via Interpretable Context Jun Yuan, Enrico Bertini. Workshop on Human-In-the-Loop Data Analytics (HILDA), ACM SIGMOD, 2022 paper / talk / slides In this work, we first identify two main components of context to assist model diagnosis through literature reviews. We then present Context Sight, a visual analytics system that integrates customized context generation and multiple-level context summarization.
	An Exploration and Validation of Visual Factors in Understanding Classification Rule Sets Jun Yuan, Oded Nov, Enrico Bertini. IEEE VIS, 2021 arXiv / paper In this paper, we explore the idea of designing alternative representations of rules, focusing on a number of visualfactors we believe have a positive impact on rule readability andunderstanding. We then presents a user study exploring their impact.
	AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation Oscar Gomez, Steffen Holter, Jun Yuan, Enrico Bertini. IEEE VIS, 2021 arXiv / paper AdViCE is a visual analytics tool that aims to guide users in black-box model debugging and validation through counterfactuals.
	Convolution Can Incur Foveation Effects Jun Yuan, Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani, Orion Reblitz-Richardson. Rethinking ML Papers - ICLR 2021 workshop, 2021 website / video Mind the Pad -- CNNs Can Develop Blind Spots Bilal Alsallakh, Narine Kokhlikyan, Vivek Miglani, Jun Yuan, Orion Reblitz-Richardson. ICLR, 2021 (Spotlight) arXiv / paper
	mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series Forecast Ke Xu, Jun Yuan, Yifang Wang, Claudio Silva, Enrico Bertini. SIGCHI, 2021 paper / video mTSeer is an interactive system for the exploration, explanation, and evaluation of multivariate time-series forecasting models.
	ViCE: Visual Counterfactual Explanations for Machine Learning Models Steffen Holter, Oscar Gomez, Jun Yuan, Enrico Bertini. ACM IUI, 2020 paper / code ViCE is an interactive visual analytics system integrating counterfactual explanation generation and exploration to contextualize and evaluate model decisions.
	ECGLens: Interactive ECG Classification and Exploration Jun Yuan, Siyao Fang, Xiang Huang, Nan Cao. IEEE VIS, poster, 2017 paper / video ECGLens is an interactive visual analytics system assisting users to inspect anomalous ECG signals (heartbeat, time-series).

Talks

June 2024, invited lecture at UC Berkeley, class DS100 (Summer'24), "Visualization II". [🎥 / 📝]

April 2022, invited lecture at The College of William & Mary, class CSCI780 Data Visualization, "Visualization for Machine Learning Explanations".

October 2020, presentation at Doctoral Colloquium of IEEE VIS 2020, "Interpreting Black-box Machine Learning Models By Visually Exploring High-Fidelity Surrogate Rules".

Service

Program Committee	VIS Short Papers (22, 23, 24), PacificVis Vis-Meets-AI Workshop (24, 25)
Reviewer	TVCG, CG&A, SIGCHI (19, 22, 23, 24), VIS (19, 20, 22, 23, 24), CSCW (20, 21), ICML (20), ChinaVis (22)
Student Volunteer	VIS (20, 21, 22)
Mentor (for Research)	NYU Undergradate Research Program (19,20,21)
Mentor (for K12 STEM Education)	ARISE (18, 20), 1000 Girls, 1000 Futures (18)

Teaching (NYU)

22'Fall, 19'Fall	Graduate Student Instructor	CS-GY 6313 Information Visualization
22'Spring	Graduate Student Instructor	CS-GY 9223 Visualization for Machine Learning
20'Spring, 19'Spring	Graduate Student Instructor	CS-GY 6323 Visual Analytics

Teaching (Fudan)

17'Spring	Teaching Assistant	Network Virtual Environment and Computer Application
16'Fall	Teaching Assistant	Programming

Website adapted from Jon Barron