Spring 2026, Wednesdays 2 to 3:20 pm, NCS 220 and Zoom link to be announced soon.
The seminar will be jointly taught by Prof. Dimitris Samaras (samaras@cs.stonybrook.edu).
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision.
To enroll in this course, you must either: (1) be in the Ph.D. program or (2) receive permission from the instructors.
Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend.
Please note: Exceptionally, the first meeting on 1/28 will be in NCS 120.
The seminar will be jointly taught by Prof. Dimitris Samaras (samaras@cs.stonybrook.edu).
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision.
To enroll in this course, you must either: (1) be in the Ph.D. program or (2) receive permission from the instructors.
Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend.
Please note: Exceptionally, the first meeting on 1/28 will be in NCS 120.
The biennial conference takes place Sun. Oct 19th through Thu the 23rd, 2025 at the Honolulu Convention Center.
October 19 - 20: Workshops
October 21 - 23: Main Conference
More information can be found here.
October 19 - 20: Workshops
October 21 - 23: Main Conference
More information can be found here.
Abstract:
Capturing the spatio-temporal (4D) dynamics of humans has been a long standing research problem in computer vision and graphics. Synthesizing photorealistic human avatars has broad applications, ranging from immersive telepresence in AR/VR and the movie industry, to enriching the education and healthcare systems. Earlier approaches relied on hand-engineered models that use a small amount of data from one or more subjects. With the advent of neural networks, training on large datasets enhanced the output visual quality. Currently, the combination of neural networks with graphics techniques has achieved natural-looking human animation. However, most approaches are identity-specific, trained only on a single identity, and use only one modality.
In this thesis, we address the problem of learning neural representations of humans in a holistic way. Given that the video data in the real world include multiple modalities (audio and video) and multiple identities, we develop multi-modal and multi-identity representations. First, we propose to reconstruct the 4D face geometry of humans by leveraging both audio and video information. In this way, the network produces accurate lip shapes and is robust to cases when either modality is insufficient. Next, we introduce a NeRF-based representation for audio-driven human face animation that achieves high-quality lip synchronization for cinematic content. Since humans communicate with their full body, combining body pose, hand gestures, and facial expressions, we extend our network to capture the full-body human motion for multiple identities simultaneously. In order to better disentangle identity and non-identity specific information, we subsequently study non-linear interactions between latent factors of variation, and propose a specific multiplicative module. In this way, we learn a multi-identity NeRF that robustly animates human faces under novel expressions and achieves a significant decrease in the total training time. Similarly, we propose a multi-identity gaussian splatting representation for human bodies, by constructing a high-order tensor. Assuming a low-rank structure, we learn a tensor decomposition that leads to a significant decrease in the total number of learnable parameters, as well as to a robust animation under novel poses. In the future, we propose to jointly synthesize audio and visual outputs from just text input. Given the recent rise of large language models, coupling text with natural-looking avatars can enhance the overall interaction between a human and an AI system.
Speaker: Aggelina Chatziagapi
Where: NCS, Room 220
Zoom link: https://stonybrook.zoom.
ID: 98775312249
Passcode: 505777
Capturing the spatio-temporal (4D) dynamics of humans has been a long standing research problem in computer vision and graphics. Synthesizing photorealistic human avatars has broad applications, ranging from immersive telepresence in AR/VR and the movie industry, to enriching the education and healthcare systems. Earlier approaches relied on hand-engineered models that use a small amount of data from one or more subjects. With the advent of neural networks, training on large datasets enhanced the output visual quality. Currently, the combination of neural networks with graphics techniques has achieved natural-looking human animation. However, most approaches are identity-specific, trained only on a single identity, and use only one modality.
In this thesis, we address the problem of learning neural representations of humans in a holistic way. Given that the video data in the real world include multiple modalities (audio and video) and multiple identities, we develop multi-modal and multi-identity representations. First, we propose to reconstruct the 4D face geometry of humans by leveraging both audio and video information. In this way, the network produces accurate lip shapes and is robust to cases when either modality is insufficient. Next, we introduce a NeRF-based representation for audio-driven human face animation that achieves high-quality lip synchronization for cinematic content. Since humans communicate with their full body, combining body pose, hand gestures, and facial expressions, we extend our network to capture the full-body human motion for multiple identities simultaneously. In order to better disentangle identity and non-identity specific information, we subsequently study non-linear interactions between latent factors of variation, and propose a specific multiplicative module. In this way, we learn a multi-identity NeRF that robustly animates human faces under novel expressions and achieves a significant decrease in the total training time. Similarly, we propose a multi-identity gaussian splatting representation for human bodies, by constructing a high-order tensor. Assuming a low-rank structure, we learn a tensor decomposition that leads to a significant decrease in the total number of learnable parameters, as well as to a robust animation under novel poses. In the future, we propose to jointly synthesize audio and visual outputs from just text input. Given the recent rise of large language models, coupling text with natural-looking avatars can enhance the overall interaction between a human and an AI system.
Speaker: Aggelina Chatziagapi
Where: NCS, Room 220
Zoom link: https://stonybrook.zoom.
ID: 98775312249
Passcode: 505777
What AI tools are available to help with the scholarly research process? Are they helpful? What do they do and is it worth the time and energy to try them out? Join librarian Christine Fena to explore and compare established and emerging AI research tools such as Elicit, Scite, Consensus, and Undermind. The online workshop will provide a starting point to understanding what these tools are, the basics of how they work, and how AI research assistants might bring changes to your search process in the future. All are welcome!
Register here via Zoom.
Register here via Zoom.
The infrastructure decisions being made around AI today will shape competitive advantage, cost, control, and institutional trust for years to come.
Organizations will invest in AI either way. The strategic question is whether they remain dependent on systems they cannot fully inspect or influence, or help shape shared infrastructure that they can adapt, govern, and build upon.
We would like to invite you to a live executive briefing from the AI Alliance. The discussion will focus on what leaders should understand now:
- The state of the movement: how 205 organizations across 29 countries are collaborating on more than 30 open projects.
- Project Tapestry: our global initiative to build frontier-capable, open-sourced AI through shared development, while allowing participating institutions to retain control over their data, resources, and strategic priorities.
- The business and policy landscape: how cost, sovereignty, trust, and access are reshaping AI strategy.
- Paths to participation: how organizations can contribute through the AI Alliance Open R&D Community and the Open Source AI Innovation Association.
Register here.
CSE 600 Seminar Series | Fall 2025
Abstract: Vision-language models that see and describe the world are now part of our daily lives, from internet search and accessibility tools to content generation and automatic moderation. However, as these models grow and become more widely used, their limitations have also become increasingly visible. In particular, it has been shown that these models are unable to reliably perform complex tasks that require abstraction and compositional reasoning. For example, they struggle to decompose an image or text into entities, attributes, and relations, and then reason over new combinations of these elements. As a result, we see generated content full of hallucinations, privacy leaks in images, and different types of biases in the model outputs.In this talk, I will outline a research agenda that aims to build trustworthy vision-language models in the age of generative AI. I will begin with compositional reasoning: how natural language inference can be used to decompose complex instructions and captions into atomic, verifiable statements, improving both evaluation and model behavior on tasks that require multi-step reasoning. I will then discuss how synthetic data and simulated environments can be used to train more reliable models, and how they can also stress-test models beyond standard benchmarks, revealing when models drop attributes, break object relations, or fail under distribution shifts. I will also share recent work on using hallucination correction as a signal to improve video-language alignment, and on privacy-preserving image understanding for blind and low-vision users. I will conclude with possible ways we can systematically probe, debug, and repair these models, turning synthetic perception into something we can trust in real-world deployments.
Speaker: Paola Cascante-Bonilla is a tenure-track Assistant Professor in the Department of Computer Science at Stony Brook University (SUNY). Before that, she was a Postdoctoral Associate at the University of Maryland Institute for Advanced Computer Studies (UMIACS), developing methods and metrics related to trustworthy machine learning. She received her Ph.D. in Computer Science at Rice University in 2024, working on Computer Vision, Natural Language Processing, and Machine Learning.Her research focuses on developing systems that enable compositional reasoning and common-sense inference through vision and language, while tackling issues such as cultural biases, data distribution, explainability, and trustworthy AI. Additionally, Cascante-Bonilla creates simulated environments for embodied agents to learn in a safe, controlled setting, aiming to facilitate effective collaboration and problem-solving for complex tasks by leveraging the implicit knowledge of large-scale pre-trained deep learning models.
Cascante-Bonilla is the recipient of the Ken Kennedy Institute SLB Graduate Fellowship (2022/23), she was selected as a Future Faculty Fellow by Rice's George R. Brown School of Engineering (2023) and as a Rising Star in EECS (2023).
Location: NCS 120
Abstract: Vision-language models that see and describe the world are now part of our daily lives, from internet search and accessibility tools to content generation and automatic moderation. However, as these models grow and become more widely used, their limitations have also become increasingly visible. In particular, it has been shown that these models are unable to reliably perform complex tasks that require abstraction and compositional reasoning. For example, they struggle to decompose an image or text into entities, attributes, and relations, and then reason over new combinations of these elements. As a result, we see generated content full of hallucinations, privacy leaks in images, and different types of biases in the model outputs.In this talk, I will outline a research agenda that aims to build trustworthy vision-language models in the age of generative AI. I will begin with compositional reasoning: how natural language inference can be used to decompose complex instructions and captions into atomic, verifiable statements, improving both evaluation and model behavior on tasks that require multi-step reasoning. I will then discuss how synthetic data and simulated environments can be used to train more reliable models, and how they can also stress-test models beyond standard benchmarks, revealing when models drop attributes, break object relations, or fail under distribution shifts. I will also share recent work on using hallucination correction as a signal to improve video-language alignment, and on privacy-preserving image understanding for blind and low-vision users. I will conclude with possible ways we can systematically probe, debug, and repair these models, turning synthetic perception into something we can trust in real-world deployments.
Speaker: Paola Cascante-Bonilla is a tenure-track Assistant Professor in the Department of Computer Science at Stony Brook University (SUNY). Before that, she was a Postdoctoral Associate at the University of Maryland Institute for Advanced Computer Studies (UMIACS), developing methods and metrics related to trustworthy machine learning. She received her Ph.D. in Computer Science at Rice University in 2024, working on Computer Vision, Natural Language Processing, and Machine Learning.Her research focuses on developing systems that enable compositional reasoning and common-sense inference through vision and language, while tackling issues such as cultural biases, data distribution, explainability, and trustworthy AI. Additionally, Cascante-Bonilla creates simulated environments for embodied agents to learn in a safe, controlled setting, aiming to facilitate effective collaboration and problem-solving for complex tasks by leveraging the implicit knowledge of large-scale pre-trained deep learning models.
Cascante-Bonilla is the recipient of the Ken Kennedy Institute SLB Graduate Fellowship (2022/23), she was selected as a Future Faculty Fellow by Rice's George R. Brown School of Engineering (2023) and as a Rising Star in EECS (2023).
Location: NCS 120
The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.
Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.
Place: https://stonybrook.
Time: 3 PM EST - Dec, 16th, 2020
Abstract:
Shadows provide useful cues to analyze visual scenes but also hamper many computer vision algorithms such as image segmentation, object detection, or tracking. For those reasons, shadow detection and shadow removal have been well-studied in computer vision.
Early work on shadow detection and removal focused on physical illumination models of shadows. These methods can express, identify, and remove shadows in a physically plausible manner. However, these models are often hard to optimize and are slow during inference due to their reliance on hand-designed image features. Recently, deep-learning approaches have achieved breakthroughs in performance for both shadow detection and removal. They learn to extract useful features through training while being extremely efficient during inference. However, these models are data-dependent, opaque, and ignore the physical aspects of shadows. Thus they often lack generalization and produce inconsistent results.
We propose incorporating physical illumination constraints of shadows into deep-learning models. These constraints force the networks to more closely follow the physics of shadows, enabling them to systematically and realistically modify shadows in images. For shadow detection, we present a novel Generative Adversarial Network (GAN) based model where the generator learns to generate images with realistic attenuated shadows that can be used to train a shadow detector. For shadow removal, we propose a method that uses deep-networks to estimate the unknown parameters of a shadow image formation model that removes shadows. The system outputs high-quality shadow-free images with little or no image artifacts and achieves state-of-the-art performance in shadow removal when trained on a fully-supervised setting. Moreover, the system is easy to train and constrain since the shadow removal mapping is strictly defined by the simplified illumination model with interpretable parameters. Thus, it can be trained even with a much weaker form of supervision signal. In particular, we show that we can use two sets of patches, shadow and shadow-free, to train our shadow decomposition framework via an adversarial system. These patches are cropped from the shadow images themselves.
Therefore, this is the first deep-learning method for shadow removal that can be trained without any shadow-free images, providing an alternative solution to the paired data dependency issue. The advantage of this training scheme is even more pronounced when tested on a novel domain such as video shadow removal where the method can be fine-tuned on a testing video with only the shadow masks generated by a pre-trained shadow detector and further improves shadow removal results.