Short Bio

I am an Assistant Professor in the Department of Computer Science at Purdue University. Prior to joining Purdue, I was a Research Assistant Professor at Toyota Technological Institute at Chicago.

I completed my Ph.D. and M.S. in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC) advised by Alexander Schwing and Minh Do. I received my B.S. degree in Electrical Engineering from UIUC working with Mark Hasegawa-Johnson. I interned several times at Google, working with Aseem Agarwala, Jonathan Huang, and Kevin Murphy.

I am interested in research at the intersection of machine learning and computer vision. My work focuses on developing algorithms for learning and designing effective generative and perceptual models across diverse domains. Recently, my research has expanded beyond task performance to emphasize safety, reliability, and robustness guarantees.

We are looking for highly motivated and talented students! Openings for Ph.D. (Fall 2027) and Undergrad at Purdue with the intention to pursue graduate school.

Thank you for your interest in joining! Due to the high volume of emails, I am unable to respond to everyone.

To get a sense of what we work on, read at least three papers for which I am the first or last author then follow the instructions below.

  • To prospective Ph.D. students NOT at Purdue:
    1. Apply to Purdue Computer Science Graduate Program and list my name in the application.
    2. There is no need to email me unless you have specific topics/interests that fits my group. Keep it brief. Finally, list which of the three papers you have read.
  • To prospective Ph.D. students at Purdue:
    1. Email me your CV, transcript, research experience, and a topic of interest. Explain why your background is suitable and how it fits in the group. Keep it brief. Finally, list which of the three papers you have read.
    2. Make sure to have communicated with the current/initial adivsor that you intended to work with me and include the advisor's name in the email.
  • To master/undergraduate students at Purdue:
    1. Email me your CV, transcript, time commitment, e.g., 15 hours per week for six months, and how you plan to be involved. Finally, list which of the three papers you have read.
Final Note: Please do not showup unannounced at my office to discuss this matter.
Raymond A. Yeh
Email: rayyeh at purdue dot edu

     

Current and Past Affiliations


Fall 2022-
2021-2022
2014-2021
Summer '19, '18
Summer '17, '16, '15
Summer '14, '13

News

Jun, 2026 We are grateful for the support from NSF with a CAREER Award 2026.
Feb, 2026 We had our first ICRA publication! Three papers (two highlights) accepted to CVPR 2026.
Nov, 2025 Two papers accepted to AAAI 2026, one paper accepted to NeurIPS 2025 and Area Chair for ICLR, CVPR, ICML 2026
Jun, 2025 One paper accepted to ICML 2025 as oral presentation and four papers accepted to ICCV 2025.
Jun, 2025 We are grateful for the support from Google with a Google Research Scholar 2025.
Feb, 2025 One paper accepted to ICLR, CVPR 2025 and Area Chair for NeurIPS 2025.
Dec, 2024 One paper accepted to AAAI 2025 and Area Chair for CVPR, ICML 2025.
Sep, 2024 We are grateful for the support from National Science Foundation (NSF IIS RI) of our research.
Sep, 2024 Two papers accepted to NeurIPS 2024 and Area Chair for ICLR 2025.
Jul, 2024 Four papers accepted to ECCV 2024 and Area Chair for NeurIPS, ACML 2024.
Feb, 2024 Two papers accepted to CVPR 2024.
Sep, 2023 Paper accepted to NeurIPS 2023 and Area Chair for ICLR 2024.
Aug, 2023 Area Chair for CVPR 2024 and Associate Editor for IET Computer Vision.
Apr, 2023 Paper accepted to ICML 2023.
Mar, 2023 Two papers accepted to CVPR 2023.
Mar, 2023 Area Chair for NeurIPS 2023.
Jan, 2023 SPC for IJCAI 2023.
Oct, 2022 Area Chair for CVPR 2023.
Sep, 2022 Papers accepted at NeurIPS 2022, BMVC 2022, and ACCV 2022.
Aug, 2022 Joined Purdue University in the CS department!



People

Chiao-An Yang

Hairong Yin

Haomeng Zhang

Jiraphon Yenphraphai

Md Ashiqur Rahman

Michael N. Cheng

Timothy Chen


Former team members and collaborators:

Amber Yijia Zheng (PhD 2026), Yu-Shan Tai (visiting PhD 2026)




Publications

* Papers highlighted in blue are personal favorites.

Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu, Unnat Jain, Raymond A. Yeh, Alexander G. Schwing
International Conference on Machine Learning (ICML), 2021
Long Talk
PDF Project Code

SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data
Yuan-Ting Hu, Jiahong Wang, Raymond A. Yeh, Alexander G. Schwing
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Oral Presentation
PDF Project

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2020
PDF Project Code

High-Throughput Synchronous Deep Reinforcement Learning
Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2020
PDF Project Code

Chirality Nets for Human Pose Regression
Raymond A. Yeh*, Yuan-Ting Hu*, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2019
Also presented at Sets & Paritions Workshop

Contributed Talk
PDF Project Code

Learning Motion in Feature Space: Locally-Consistent Deformable Convolution Networks for Fine-Grained Action Detection
Khoi-Nguyen C. Mac, Dhiraj Joshi, Raymond A. Yeh, Jinjun Xiong, Rogerio S. Feris, Minh N. Do
International Conference on Computer Vision (ICCV), 2019
Oral Presentation
PDF Project Code

Diverse Generation for Multi-agent Sports Games
Raymond A. Yeh, Alexander G. Schwing, Jonathan Huang, Kevin Murphy
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Oral Presentation
PDF Project

Unsupervised Textual Grounding: Linking Words to Image Concepts
Raymond A. Yeh, Minh N. Do, Alexander G. Schwing
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
Spotlight Presentation
PDF Project

Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Raymond A. Yeh, Jinjun Xiong, Wen-mei W. Hwu, Minh N. Do, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2017
Oral Presentation
PDF Project

Video Frame Synthesis using Deep Voxel Flow
Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala
International Conference on Computer Vision (ICCV), 2017
Oral Presentation
PDF Project Code

Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh*, Chen Chen*, Teck Yian Lim, Alexander G. Schwing, Mark Hasegawa-Johnson, Minh N. Do
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
PDF Project Code

Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation
Amber Yijia Zheng, Lu Liu, Raymond A. Yeh, Xi Yin
arXiv preprint, 2026
PDF

Zero-shot 2D Grounding with Novel Affordance Types
Haomeng Zhang, Raymond A. Yeh
arXiv preprint, 2026
PDF

DREAM-Chunk: Reactive Action Chunking with Latent World Model
Wenxi Chen, Kaidi Zhang, Chi Lin, Zhiyuan Zhang, Yu She, Yuejiang Liu, Raymond A. Yeh, Shaoshuai Mou, Yan Gu
arXiv preprint, 2026
PDF

Helix4D: Complex 4D Mesh Generation
Jiraphon Yenphraphai, Jianqi Chen, Jian Wang, Gordon Qian, Sergey Tulyakov, Rameen Abdal, Raymond A. Yeh, Peter Wonka, Chaoyang Wang
arXiv preprint, 2026
PDF Project

Designing to Forget: Deep Semi-parametric Models for Unlearning
Amber Yijia Zheng*, Yu-Shan Tai*, Raymond A. Yeh
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026
Highlight
PDF Code

Tunable Soft Equivariance with Guarantees
Md Ashiqur Rahman, Lim Jun Hao, Jeremiah Jiang, Teck-Yian Lim, Raymond A. Yeh
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026
PDF Code

4D-RGPT: Toward Region-level 4D Understanding via Perceptual Distillation
Chiao-An Yang, Ryo Hachiuma, Sifei Liu, Subhashree Radhakrishnan, Raymond A. Yeh, Yu-Chiang Frank Wang, Min-Hung Chen
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2026
Highlight
PDF Project Code

Semantic Consistent Language Gaussian Splatting for Point-Level Open-vocabulary Querying
Hairong Yin, Huangying Zhan, Yi Xu, Raymond A. Yeh
IEEE International Conference on Robotics and Automation (ICRA), 2026
PDF Project

ShapeGen4D: Towards High Quality 4D Shape Generation from Videos
Jiraphon Yenphraphai, Ashkan Mirzaei, Jianqi Chen, Jiaxu Zou, Sergey Tulyakov, Raymond A. Yeh, Peter Wonka, Chaoyang Wang
International Conference on Learning Representations (ICLR), 2026
PDF Project

WebAccessVL: Making an Accessible Web via Violation-Conditioned VLM
Amber Yijia Zheng*, Jae Joong Lee*, Bedrich Benes, Raymond A. Yeh
arXiv preprint, 2025
PDF

Auto-Vocabulary 3D Object Detection
Haomeng Zhang, Kuan-Chuan Peng, Suhas Lohit, Raymond A. Yeh
arXiv preprint, 2025
PDF

Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models
Jae Joong Lee, Bedrich Benes, Raymond A. Yeh
The AAAI Conference on Artificial Intelligence (AAAI), 2026
PDF

Building Instance Segmentation for Dense Urban Settlements
Adnan Firoze, Raymond A. Yeh, Daniel Aliaga
The AAAI Conference on Artificial Intelligence (AAAI), 2026

Knowledge Distillation Detection for Open-weights Models
Qin Shi*, Amber Yijia Zheng*, Qifan Song, Raymond A. Yeh
Neural Information Processing Systems (NeurIPS), 2025
PDF Code

Heatmap Regression without Soft-Argmax for Facial Landmark Detection
Chiao-An Yang, Raymond A. Yeh
International Conference on Computer Vision (ICCV), 2025
PDF Project Code

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts
Chiao-An Yang, Kuan-Chuan Peng, Raymond A. Yeh
International Conference on Computer Vision (ICCV), 2025
PDF

Local Scale Equivariance with Deep Equilibrium Canonicalizer in the Latent Space
Md Ashiqur Rahman, Chiao-An Yang, Michael N. Cheng, Lim Jun Hao, Jeremiah Jiang, Teck-Yian Lim, Raymond A. Yeh
International Conference on Computer Vision (ICCV), 2025
PDF Project Code

CLIPSym: Delving into Symmetry Detection with CLIP
Tinghan Yang, Md Ashiqur Rahman, Raymond A. Yeh
International Conference on Computer Vision (ICCV), 2025
PDF Code

Model Immunization from a Condition Number Perspective
Amber Yijia Zheng*, Cedar Site Bai*, Brian Bullins, Raymond A. Yeh
International Conference on Machine Learning (ICML), 2025
Oral Presentation
PDF Project Code

DarkDiff: Advancing Low-Light Raw Enhancement by Retasking Diffusion Models for Camera ISP
Amber Yijia Zheng, Yu Zhang, Jun Hu, Raymond A. Yeh, Chen Chen
arXiv preprint, 2025
PDF

CFG-Zero*: Improved Classifier-Free Guidance for Flow Matching Models
Weichen Fan, Amber Yijia Zheng, Raymond A. Yeh, Ziwei Liu
arXiv preprint, 2025
PDF Project Code

Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection
Wenxi Chen, Raymond A. Yeh*, Shaoshuai Mou, Yan Gu*
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025
PDF Code

Group Downsampling with Equivariant Anti-aliasing
Md Ashiqur Rahman, Raymond A. Yeh
International Conference on Learning Representations (ICLR), 2025
PDF Project Code

Multi-concept Model Immunization through Differentiable Model Merging
Amber Yijia Zheng, Raymond A. Yeh
The AAAI Conference on Artificial Intelligence (AAAI), 2025
PDF Project Code

Multi-Object 3D Grounding with Dynamic Modules and Language Informed Spatial Attention
Haomeng Zhang, Chiao-An Yang, Raymond A. Yeh
Neural Information Processing Systems (NeurIPS), 2024
PDF Project Code

Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
Md Ashiqur Rahman, Robert J. George, Mogab Elleithy, Daniel Leibovici, Zongyi Li, Boris Bonev, Colin White, Julius Berner, Raymond A. Yeh, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar
Neural Information Processing Systems (NeurIPS), 2024
PDF Code

IMMA: Immunizing text-to-image Models against Malicious Adaptation
Amber Yijia Zheng, Raymond A. Yeh
European Conference on Computer Vision (ECCV), 2024
Best Paper Runner-up at AI4CC in CVPR Workshop 2024
PDF Project Code

Learning to Obstruct Few-Shot Image Classification over Restricted Classes
Amber Yijia Zheng*, Chiao-An Yang*, Raymond A. Yeh
European Conference on Computer Vision (ECCV), 2024
PDF Project Code

Deep Nets with Subsampling Layers Unwittingly Discard Useful Activations at Test-Time
Chiao-An Yang, Ziwei Liu, Raymond A. Yeh
European Conference on Computer Vision (ECCV), 2024
PDF Code

Tree-D Fusion: Simulation-Ready Tree Dataset from Single Images with Diffusion Priors
Jae Joong Lee, Bosheng Li, Sara Beery, Jonathan Huang, Songlin Fei, Raymond A. Yeh , Bedrich Benes
European Conference on Computer Vision (ECCV), 2024
PDF Project Code

Making Vision Transformers Truly Shift-Equivariant
Renan A. Rojas-Gomez, Teck-Yian Lim, Minh N. Do, Raymond A. Yeh
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024
PDF Project

Alpha Invariance: On Inverse Scaling Between Distance and Volume Density in a Neural Radiance Field
Joshua Ahn*, Haochen Wang*, Raymond A. Yeh, Greg Shakhnarovich
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024
PDF Project

AmbiGen: Generating Ambigrams from Pre-trained Diffusion Model
Boheng Zhao, Rana Hanocka, Raymond A. Yeh
CVPR Workshop on Graphic Design Understanding and Generation, 2024
PDF Project

Truly Scale-Equivariant Deep Nets with Fourier Layers
Md Ashiqur Rahman, Raymond A. Yeh
Neural Information Processing Systems (NeurIPS), 2023
PDF Code

Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction
Yuan-Ting Hu, Alexander G. Schwing, Raymond A. Yeh
International Conference on Machine Learning (ICML), 2023
PDF Project Code

Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation
Haochen Wang*, Xiaodan Du*, Jiahao Li*, Raymond A. Yeh, Greg Shakhnarovich
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
PDF Project Code

Tree Instance Segmentation using Temporal Structured Images
Adnan Firoze, Cameron Wingren, Raymond A. Yeh, Bedrich Benes, Daniel Aliaga
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023
PDF Project Code

Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks
Renan A. Rojas-Gomez, Teck-Yian Lim, Alexander G. Schwing, Minh N. Do, Raymond A. Yeh
Neural Information Processing Systems (NeurIPS), 2022
PDF Project Code

TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
William M Gao, April Wang, Gal Metzer, Raymond A. Yeh, Rana Hanocka
British Machine Vision Conference (BMVC), 2022
Oral Presentation
PDF Project Code

Inverting Adversarially Robust Networks for Image Synthesis
Renan A. Rojas-Gomez, Raymond A. Yeh, Minh N Do, Anh Nguyen
Asian Conference on Computer Vision (ACCV), 2022
PDF Code

Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
Xiaodan Du, Raymond A. Yeh, Nicholas Kolkin, Eli Shechtman, Greg Shakhnarovich
arXiv preprint, 2022
PDF Code

Adapting CLIP For Phrase Localization Without Further Training
Jiahao Li, Greg Shakhnarovich, Raymond A. Yeh
arXiv preprint, 2022
PDF Code

Total Variation Optimization Layers for Computer Vision
Raymond A. Yeh, Yuan-Ting Hu, Zhongzheng Ren, Alexander G. Schwing
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
PDF Project Code

Equivariance Discovery by Learned Parameter-Sharing
Raymond A. Yeh, Yuan-Ting Hu, Mark Hasegawa-Johnson, Alexander G. Schwing
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
PDF Project Code

Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning
Iou-Jen Liu*, Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing
International Conference on Intelligent Robots and Systems (IROS), 2021
Also presented at Reinforcement Learning for Real Life Workshop at ICML, 2021

PDF Project

Cooperative Exploration for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu, Unnat Jain, Raymond A. Yeh, Alexander G. Schwing
International Conference on Machine Learning (ICML), 2021
Long Talk
PDF Project Code

SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data
Yuan-Ting Hu, Jiahong Wang, Raymond A. Yeh, Alexander G. Schwing
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Oral Presentation
PDF Project

MULTI-DECODER DPRNN: Source Separation for Variable Number of Speakers
Junzhe Zhu, Raymond A. Yeh, Mark Hasegawa-Johnson
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
PDF

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2020
PDF Project Code

High-Throughput Synchronous Deep Reinforcement Learning
Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2020
PDF Project Code

Chirality Nets for Human Pose Regression
Raymond A. Yeh*, Yuan-Ting Hu*, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2019
Also presented at Sets & Paritions Workshop

Contributed Talk
PDF Project Code

PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu*, Raymond A. Yeh*, Alexander G. Schwing
Conference on Robot Learning (CoRL), 2019
PDF Project Code

Learning Motion in Feature Space: Locally-Consistent Deformable Convolution Networks for Fine-Grained Action Detection
Khoi-Nguyen C. Mac, Dhiraj Joshi, Raymond A. Yeh, Jinjun Xiong, Rogerio S. Feris, Minh N. Do
International Conference on Computer Vision (ICCV), 2019
Oral Presentation
PDF Project Code

Diverse Generation for Multi-agent Sports Games
Raymond A. Yeh, Alexander G. Schwing, Jonathan Huang, Kevin Murphy
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Oral Presentation
PDF Project

Unsupervised Textual Grounding: Linking Words to Image Concepts
Raymond A. Yeh, Minh N. Do, Alexander G. Schwing
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
Spotlight Presentation
PDF Project

Time-Frequency Networks for Audio Super-Resolution
Teck Yian Lim*, Raymond A. Yeh*, Yijia Xu, Minh N. Do, Mark Hasegawa-Johnson
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
PDF Project Code

Image Restoration with Deep Generative Models
Raymond A. Yeh*, Teck Yian Lim*, Chen Chen, Alexander G. Schwing, Mark Hasegawa-Johnson, Minh N. Do
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
PDF Project Code

Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts
Raymond A. Yeh, Jinjun Xiong, Wen-mei W. Hwu, Minh N. Do, Alexander G. Schwing
Neural Information Processing Systems (NeurIPS), 2017
Oral Presentation
PDF Project

Video Frame Synthesis using Deep Voxel Flow
Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala
International Conference on Computer Vision (ICCV), 2017
Oral Presentation
PDF Project Code

Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh*, Chen Chen*, Teck Yian Lim, Alexander G. Schwing, Mark Hasegawa-Johnson, Minh N. Do
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
PDF Project Code

Semantic Facial Expression Editing using Autoencoded Flow
Raymond A. Yeh, Ziwei Liu, Dan B Goldman, Aseem Agarwala
arXiv preprint, 2016
PDF Project

Stable and Symmetric Filter Convolutional Neural Network
Raymond Yeh, Mark Hasegawa-Johnson, Minh N. Do
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016
PDF Project




Teaching

Purdue University

University of Illinois at Urbana-Champaign (Teaching Assistant)

  • Fall 2019: Pattern Recognition
  • Spring 2018: Machine Learning
  • Fall 2017: Pattern Recognition
  • Fall 2016: Pattern Recognition
  • Fall 2015: Embedded DSP Laboratory
  • Spring 2015: Embedded DSP Laboratory
  • Fall 2014: Embedded DSP Laboratory



Services

Area Chair: NeurIPS, CVPR, ICLR, ICML, IJCAI, ACML
Associate Editor: IET Computer Vision
Conference Reviewer: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, AISTATS
Journal Reviewer: TPAMI, IJCV, SIGGRAPH, TMLR, Pattern Recognit.