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 (TTIC).

I completed my Ph.D. and M.S. in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC) advised by Prof. Alexander Schwing and Prof. Minh Do. I received my B.S. degree in Electrical Engineering from UIUC working with Prof. Mark Hasegawa-Johnson.

I am interested in research relating to machine learning and computer vision. My research focuses on developing algorithms to learn and design effective models across several domains including audio, vision, language, and multi-agent systems.

We are looking for highly motivated students to join our lab.

Thanks for your interest in joining! Due to the number of emails, I am unable to respond to all. Please start your email with "I have read the notes to prospective students." and follow the instructions below.

  • To prospective Ph.D. students:
    1. To get a sense of what we work on, read at least three papers for which I am the first or last author.
    2. 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.
      • Currently not at Purdue: Please apply to Purdue Computer Science Graduate Program and list my name in the application.
      • Current Ph.D. student at Purdue: Please make sure to have communicated with your current/initial adivsor that you intended to work me and include the advisor's name in the email.
  • To master/undergraduate students at Purdue: To get a sense of what we work on, please read at least one paper for which I am the first or last author. Email me your CV, transcript, time commitment, e.g., 15 hours per week for six months, and how you plan to be involved.
Final Note: Please do not showup unaccounced 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

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

Amber Yijia Zheng

Chiao-An Yang

Hairong Yin

Haomeng Zhang

Jiraphon Yenphraphai

Md Ashiqur Rahman

Tinghan Yang



Publications

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

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

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


Awards and Honors