Join Zoom Meeting
https://stonybrook.zoom.us/j/511954566?pwd=d3hGdHlGZVJISnV3d2pRcnBRbFBJZz09 (ID: 511954566, password: fxnmj4)
Join by phone
(US) +1 646-876-9923
Join using SIP
511954566@zoomcrc.com (passcode: 542040)
Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DDBgsLRhCTQCGwD9DAxf7mw%253D%253D%26signature%3D02e2f3393481f8bc18cbb5520e174a6b8ceee7e9e58c9dca9d0aa4db9a63279a&sa=D&usg=AOvVaw1e3Mi7F_8TdXl5XstjsrIx
Password: fxnmj4
Are you tired of drowning in a sea of resumes and losing top talent in the hiring whirlwind? Transform your hiring process through a different lens and learn about AI in the Workplace and the Applicant Tracking System (ATS). Whether you're a recent graduate seeking your first job or an undergraduate student looking to delve into more career-oriented opportunities, this workshop by SBU Career Center is designed to equip you with the knowledge and strategies needed to succeed.
Register here: https://stonybrook.joinhandshake.com/stu/events/1568133?
Abstract: Artificial intelligence (AI) is rapidly transforming scientific discovery, enabling breakthroughs in areas ranging from drug discovery to modeling complex physical systems. In the life sciences, AI has traditionally been applied to prediction tasks such as classifying molecules as toxic or non-toxic, estimating drug properties, or solving partial differential equations. These discriminative models have proven powerful, but they are inherently limited to mapping existing inputs to deterministic outputs. A new wave of methods is shifting the paradigm from discrimination to generation: creating new possibilities, such as generating novel molecules or designing new drugs. By reframing AI as both a predictive and generative engine, this shift offers new pathways for accelerating discovery and innovation in life sciences at an unprecedented scale. This talk will cover several aspects of AI for Science (AI4Sci), beginning with advances in discriminative models for molecular systems and solving PDEs, and then turning to generative approaches, including diffusion models for 3D molecular generation and large language models for drug editing. Together, these developments illustrate how moving from prediction to creation is redefining what AI can contribute to science.
Bio: Wenhan Gao is a fourth-year Ph.D. student in Applied Mathematics under the supervision of Professor Yi Liu. He was also a Staff Research Scientist Intern at VISA Research, where he worked on large language models (LLMs) and multi-agent systems for commerce. Wenhan's research focuses on AI for Science (AI4Sci), with a particular emphasis on generative AI. His work looks deep into the fundamental mechanisms of AI models when applied to scientific tasks, and he strives to incorporate established scientific priors, such as symmetry, into model design. He has published papers as a first or corresponding author in leading AI and computational venues, including ICLR, ICML, NeurIPS, TMLR, ACL, and the Journal of Computational Physics. In addition to his research, Wenhan has served as a reviewer and oral session chair for top AI conferences and as a lecturer for both undergraduate and graduate courses at Stony Brook University.
Location: IACS Seminar Room or Zoom
This seminar will take place in person and online*
Join Zoom Meeting: https://stonybrook.zoom.us/j/
Meeting ID: 916 7009 3552
Passcode: 434045
Bio: Wenhan Gao is a fourth-year Ph.D. student in Applied Mathematics under the supervision of Professor Yi Liu. He was also a Staff Research Scientist Intern at VISA Research, where he worked on large language models (LLMs) and multi-agent systems for commerce. Wenhan's research focuses on AI for Science (AI4Sci), with a particular emphasis on generative AI. His work looks deep into the fundamental mechanisms of AI models when applied to scientific tasks, and he strives to incorporate established scientific priors, such as symmetry, into model design. He has published papers as a first or corresponding author in leading AI and computational venues, including ICLR, ICML, NeurIPS, TMLR, ACL, and the Journal of Computational Physics. In addition to his research, Wenhan has served as a reviewer and oral session chair for top AI conferences and as a lecturer for both undergraduate and graduate courses at Stony Brook University.
Location: IACS Seminar Room or Zoom
This seminar will take place in person and online*
Join Zoom Meeting: https://stonybrook.zoom.us/j/
Meeting ID: 916 7009 3552
Passcode: 434045
You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.
Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.
Speakers
Kriti Chopra, Computing & Data Sciences (CDS)
Thomas Flynn, Computing & Data Sciences (CDS)
Wenjie Liao, Chemistry Division
Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1
Meeting ID: 161 528 9117
Passcode: 991382
The Hudson River Estuary (HRE) and New York Bight (NYB) are closely connected, with HRE acting as crucial areas where many NYB marine species spawn and grow. Understanding how these biotic and abiotic environments interact, especially with rapid climate change, is key to better managing fisheries and conserving ecosystems. To better understand the HRE-NYB ecosystem, we develop a comprehensive ecosystem model that links physical and biological processes. Using data from long-term monitoring programs, we analyze ecological patterns and identify key factors regulating the ecosystem. We use this information to develop a model that mimics the food web from tiny plankton to large predators in the ecosystem. This model can help us better understand how changes in the environment, like rising temperatures, and human activities such as fishing affect marine lives and ecosystem over time. The insights from this model can support smarter fisheries management and efforts to conserve marine ecosystems in the HRE-NYB region.
IACS Student Seminar Speaker: Xiangyan Yang, Dept. of Applied Math & Statistics
Location: IACS Seminar Room or Zoom
Join Zoom Meeting: https://stonybrook.zoom.us/j/91650247483?pwd=fvAGEwadplJh7jFC5RWcdvZ5NWPJth.1
Meeting ID: 916 5024 7483
Passcode: 631055
IACS Student Seminar Speaker: Xiangyan Yang, Dept. of Applied Math & Statistics
Location: IACS Seminar Room or Zoom
Join Zoom Meeting: https://stonybrook.zoom.us/j/91650247483?pwd=fvAGEwadplJh7jFC5RWcdvZ5NWPJth.1
Meeting ID: 916 5024 7483
Passcode: 631055
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 dissertation, 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 (e.g., 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 the network to capture 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. Last but not least, 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.
Location: NCS 220 or Zoom
In this dissertation, 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 (e.g., 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 the network to capture 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. Last but not least, 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.
Location: NCS 220 or Zoom
Zoom: https://stonybrook.zoom.us/j/
Meeting ID: 97512368946
passcode: 045476
passcode: 045476
Abstract: Implicit functions have long been a fundamental representation for both 2D and 3D objects in computer graphics, playing a significant role in the field's early development. With the rise of 3D deep learning and the rapid advancement of neural rendering techniques, implicit representations of 3D shapes have regained significant attention in recent years. In this talk, I will present several recent research projects focusing on implicit function-based 3D reconstruction and neural rendering. Furthermore, I will discuss potential future developments in this dynamic and rapidly evolving field.
Biography: Ying He is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University, where he also serves as the Director of the Centre for Augmented and Virtual Reality. His research interests lie in geometric computation and analysis, with applications spanning computer graphics, 3D vision, computer-aided design, multimedia, and wireless sensor networks. Dr. He is an active member of the technical program committees for major conferences on geometric modeling and has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and Computational Visual Media. He has also taken on key leadership roles as General/Program Co-Chair for several conferences, including Shape Modeling International (SMI) 2022, Solid and Physical Modeling (SPM) 2022 & 2023, Geometric Modeling and Processing (GMP) 2014 & 2021, and Computational Visual Media (CVM) 2020. For more information, please visit https://personal.ntu.
Location: NCS 115
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