Time: 04/28 Wed 3pm-4pm
Remote Access
Join Zoom Meeting https://stonybrook.zoom.us/j/
Meeting ID: 956 1719 7636 Passcode: 924293
Title: Brain imaging genetics for Alzheimer's disease: integrated analysis and machine learning
Li Shen, Ph.D.
Professor of Informatics
Department of Biostatistics, Epidemiology and Informatics
Perelman School of Medicine
University of Pennsylvania
Bio: Li Shen, Ph.D., is a Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University of Pennsylvania. He is an elected fellow of the American Institute for Medical and Biological Engineering (AIMBE). He obtained his Ph.D. degree in Computer Science from Dartmouth College. The central theme of his lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput omics data, cognitive and other biomarker data, electronic health record (EHR) data, and rich biological knowledge such as pathways and networks, with applications to complex disorders. His research interests include medical image computing, biomedical informatics, machine learning, network science, imaging genomics, Alzheimer's disease, and big data science in biomedicine. He has authored over 280 peer-reviewed articles (h-index 57) in these fields. Dr. Shen's work has been continuously supported by the NIH and NSF, and he is presently the PI of multiple NIH and NSF grants on developing computational methods for various biomedical applications including brain imaging genomics, genetics of Alzheimer's disease, genetics of human connectome, mining drug effects from the EHR data, and big data mining in brain science. He is co-leading the NIA Alzheimer's Disease Sequencing Project AI4AD Consortium and oversees the imaging genomics aspect of this landmark project. Dr. Shen served as the Executive Director of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Board of Directors during 2016-2019. He has chaired and co-chaired various professional meetings in medical image computing and bioinformatics. He is an Associate Editor of BioData Mining and Frontiers in Radiology (Section of AI in Radiology), and serves on the Editorial Board of Medical Image Analysis and Brain Imaging and Behavior.
Abstract: Brain imaging genetics is an emerging data science field, where integrated analysis of brain imaging and genetics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the genetic, molecular and phenotypic characteristics of the brain as well as their impact on normal and disordered brain function and behavior. Many methodological advances in brain imaging genetics are attributed to large-scale landmark biobank projects such as the Alzheimer's Disease Sequencing Project, the Alzheimer's Disease Neuroimaging Initiative, and the UK Biobank. Using the study of Alzheimer's disease as an example, we will discuss fundamental concepts, state-of-the-art statistical and machine learning methods, and innovative applications in this rapidly evolving field. We show that the wide availability of brain imaging genetics data from various large-scale biobanks, coupled with advances in biomedical statistics, informatics and computing, provides enormous opportunities to contribute significantly to biomedical discoveries in brain science and to impact the development of new diagnostic, therapeutic and preventative approaches for complex brain disorders such as Alzheimer's disease.
More details:
https://bmi.
Join a collaborative workshop series designed to help you redesign writing-intensive assignments in the age of AI. The program includes four self-paced modules, weekly interactive workshops (September 18, 25, and October 2, 9), student resources, and a SUNY-wide virtual symposium. Applications are accepted through August 31.
https://you.stonybrook.edu/campusannouncements/2026/06/22/june-22-2026/
https://you.stonybrook.edu/campusannouncements/2026/06/22/june-22-2026/
Face Editing with Machine Learning presented by Zhixin Shu
ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.
ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.
AI3, SBU Libraries and IACS present
at International Love Data Week
sponsored by The Office of the Provost and
Educational and Institutional Effectiveness (EIE)
Special Talk and Panel Discussion
How I Learned to Stop Worrying and Love AI (For Now)
with Paul Fain from The Job and Work Shift
A reporter's take on what we know--and what we don't know--about AI's emerging impacts on the labor market. The discussion will include the latest research from economists and the AI labs themselves about how workers are using AI, and current thinking among experts on how the tech's rapid deployment will play out across job roles, industries, and regions.
Limited Seats!
Registration is required.
at International Love Data Week
sponsored by The Office of the Provost and
Educational and Institutional Effectiveness (EIE)
Special Talk and Panel Discussion
How I Learned to Stop Worrying and Love AI (For Now)
with Paul Fain from The Job and Work Shift
A reporter's take on what we know--and what we don't know--about AI's emerging impacts on the labor market. The discussion will include the latest research from economists and the AI labs themselves about how workers are using AI, and current thinking among experts on how the tech's rapid deployment will play out across job roles, industries, and regions.
Panel discussion to follow with:
- Lav Varshney, Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute
- Nicholas Johnson, Director of AI, SBU Libraries
- Marianna Savoca, Associate Vice President for Career Readiness and Experiential Education
Limited Seats!
Registration is required.
Abstract: Self-supervised representation learning (SRL) has emerged as a pivotal advancement in machine learning, offering high-quality data representations without the need for labeled datasets. While SRL has demonstrated enhanced adversarial robustness compared to supervised learning, its resilience against other attack types, particularly backdoor attacks, remains an open question. Recent studies have revealed potential vulnerabilities in SRL, underscoring the necessity for a comprehensive security analysis. However, existing research often extrapolates attacks from supervised learning paradigms, neglecting the unique challenges and opportunities inherent to self-supervised mechanisms.
This thesis proposal aims to address three critical objectives in the realm of self-supervised learning: (1) exploring novel attack vectors, (2) implementing and evaluating practical attacks, and (3) developing robust countermeasures. We focus on two key SRL paradigms: Contrastive Learning and Diffusion Models. For Contrastive Learning, we synthesize existing security vulnerabilities and introduce innovative attack vectors, such as CTRL, to uncover distinctive risks. We conduct a comparative analysis of contrastive and supervised learning approaches in their defense against these threats, exploring potential safeguards and highlighting the limitations of current protective measures in self-supervised contexts. Regarding Diffusion Models, we demonstrate inherent vulnerabilities in their application to adversarial purification.
Our research aims to illuminate the unique challenges posed by emerging attack vectors in self-supervised learning, fostering technical advancements to address underlying security risks in real-world applications. By contributing to the development of more resilient and secure self-supervised representation learning systems, we seek to enhance their reliability and trustworthiness in practical scenarios. This comprehensive examination of SRL's security landscape will provide valuable insights for the broader machine-learning community and pave the way for more robust AI systems.
Join here.
This thesis proposal aims to address three critical objectives in the realm of self-supervised learning: (1) exploring novel attack vectors, (2) implementing and evaluating practical attacks, and (3) developing robust countermeasures. We focus on two key SRL paradigms: Contrastive Learning and Diffusion Models. For Contrastive Learning, we synthesize existing security vulnerabilities and introduce innovative attack vectors, such as CTRL, to uncover distinctive risks. We conduct a comparative analysis of contrastive and supervised learning approaches in their defense against these threats, exploring potential safeguards and highlighting the limitations of current protective measures in self-supervised contexts. Regarding Diffusion Models, we demonstrate inherent vulnerabilities in their application to adversarial purification.
Our research aims to illuminate the unique challenges posed by emerging attack vectors in self-supervised learning, fostering technical advancements to address underlying security risks in real-world applications. By contributing to the development of more resilient and secure self-supervised representation learning systems, we seek to enhance their reliability and trustworthiness in practical scenarios. This comprehensive examination of SRL's security landscape will provide valuable insights for the broader machine-learning community and pave the way for more robust AI systems.
Join here.
CSE 600 Seminar Series | Fall 2025
Abstract:
We often talk about AI as if it begins with a dataset and ends with an application. But behind every model lie two sets of actors who are rarely acknowledged in technical documentation: the workers who train AI systems and the researchers who try to make sense of them. This talk brings both groups into view.
Dr. Ben Zhang will offer an on-the-ground examination of the prevailing values and invisible labor that underpin commercial AI production and data production. Drawing on ethnographic research inside AI data annotation centers in China, he introduces the concept of precision labor to unpack the labor dimension of constructing, managing, and performing technical accuracy. This concept highlights the hidden and excessive labor required to reconcile the ambiguity and uncertainty involved in AI training. A precision labor lens challenges the legitimacy and sustainability of the relentless pursuit of technical accuracy, raising new questions about its consequences and implications.
On the other end of the pipeline, as LLMs become embedded in society, social scientists like Dr. Jieshu Wang is scrutinizing their potential biases while employing them as research tools. She will present her recent work auditing LLM responses across different contexts, revealing that LLMs exhibit varying levels of environmental awareness and disproportionately reward institutional prestige in peer-review simulations. She also demonstrates how LLMs can serve as useful tools in social-science pipelines, e.g., extracting location information, inferring demographics, parsing citations, mapping social networks, and analyzing occupational data.
By placing these two worlds side by side - the labor of training AI and the scholarly efforts to study it - we show why responsible AI should go beyond the deployment phase - emphasizing fairness audits, and model explainability. It requires reimaging the values, labor regimes, and social science practices that shape AI systems from annotation to analysis.
Bios:
Dr. Jieshu Wang is an interdisciplinary researcher studying the human and social dimensions of artificial intelligence (AI) and how people can thrive in an AI-integrated future. She combines computational methods with qualitative insights to trace technology trends and understand their broader societal impact. She earned her Ph.D. in Human and Social Dimensions of Science and Technology from Arizona State University, after earlier degrees in Civil Engineering, Economics, and Science and Technology Studies. She has also worked as a patent examiner, an editor at a popular science magazine, and co-founded Synced (机器之心), an AI-focused media company in China. Her research looks both backward and forward. Backward-looking, she examines how AI are created, who creates them, and who is missing from the process. Forward-looking, she studies how AI is transforming the way we live, connect, invent, work, and adapt, as well as how AI might help address challenges such as climate change and workforce transitions.
Dr. Ben Zhang is an Assistant Professor in the Department of Technology. His research explores the production and sociotechnical impacts of AI systems in critical areas such as work, health, and sustainability. Drawing from his background in Human-Computer Interaction (HCI), Human-Centered AI, and Science and Technology Studies (STS), he employs a life-cycle-centered approach to holistically examine the promises and harms of these systems and to inform the design of responsible AI infrastructures across their development, deployment, and governance. Ben received his Ph.D. in Information Science from the University of Michigan. Ben's work has been supported by competitive awards and fellowships, including the University of Michigan Rackham Predoctoral Fellowship and the Weizenbaum Fellowship. His research has appeared in premier computing venues, including ACM CHI, ACM CSCW, and AAAI ICWSM.
Location: NCS 120
Abstract:
We often talk about AI as if it begins with a dataset and ends with an application. But behind every model lie two sets of actors who are rarely acknowledged in technical documentation: the workers who train AI systems and the researchers who try to make sense of them. This talk brings both groups into view.
Dr. Ben Zhang will offer an on-the-ground examination of the prevailing values and invisible labor that underpin commercial AI production and data production. Drawing on ethnographic research inside AI data annotation centers in China, he introduces the concept of precision labor to unpack the labor dimension of constructing, managing, and performing technical accuracy. This concept highlights the hidden and excessive labor required to reconcile the ambiguity and uncertainty involved in AI training. A precision labor lens challenges the legitimacy and sustainability of the relentless pursuit of technical accuracy, raising new questions about its consequences and implications.
On the other end of the pipeline, as LLMs become embedded in society, social scientists like Dr. Jieshu Wang is scrutinizing their potential biases while employing them as research tools. She will present her recent work auditing LLM responses across different contexts, revealing that LLMs exhibit varying levels of environmental awareness and disproportionately reward institutional prestige in peer-review simulations. She also demonstrates how LLMs can serve as useful tools in social-science pipelines, e.g., extracting location information, inferring demographics, parsing citations, mapping social networks, and analyzing occupational data.
By placing these two worlds side by side - the labor of training AI and the scholarly efforts to study it - we show why responsible AI should go beyond the deployment phase - emphasizing fairness audits, and model explainability. It requires reimaging the values, labor regimes, and social science practices that shape AI systems from annotation to analysis.
Bios:
Dr. Jieshu Wang is an interdisciplinary researcher studying the human and social dimensions of artificial intelligence (AI) and how people can thrive in an AI-integrated future. She combines computational methods with qualitative insights to trace technology trends and understand their broader societal impact. She earned her Ph.D. in Human and Social Dimensions of Science and Technology from Arizona State University, after earlier degrees in Civil Engineering, Economics, and Science and Technology Studies. She has also worked as a patent examiner, an editor at a popular science magazine, and co-founded Synced (机器之心), an AI-focused media company in China. Her research looks both backward and forward. Backward-looking, she examines how AI are created, who creates them, and who is missing from the process. Forward-looking, she studies how AI is transforming the way we live, connect, invent, work, and adapt, as well as how AI might help address challenges such as climate change and workforce transitions.
Dr. Ben Zhang is an Assistant Professor in the Department of Technology. His research explores the production and sociotechnical impacts of AI systems in critical areas such as work, health, and sustainability. Drawing from his background in Human-Computer Interaction (HCI), Human-Centered AI, and Science and Technology Studies (STS), he employs a life-cycle-centered approach to holistically examine the promises and harms of these systems and to inform the design of responsible AI infrastructures across their development, deployment, and governance. Ben received his Ph.D. in Information Science from the University of Michigan. Ben's work has been supported by competitive awards and fellowships, including the University of Michigan Rackham Predoctoral Fellowship and the Weizenbaum Fellowship. His research has appeared in premier computing venues, including ACM CHI, ACM CSCW, and AAAI ICWSM.
Location: NCS 120
This workshop is intended for researchers, practitioners, students, and industry professionals in AI, robotics, machine learning, human-robot interaction, and related fields.
Workshop Overview:
Instead of learning from data alone, an embodied AI system learns through its movements, sensors, and interactions with the environment. This form of active, experience-based learning, informed by ongoing self-evaluation of its own abilities, enables embodied AI systems to adapt on the fly, understand context rather than just commands, and collaborate with humans in more natural and trustworthy ways.
Workshop Goals:
- Foster interdisciplinary dialogue across AI, robotics, and cognitive science.
- Identify key challenges and future research directions in embodied intelligence.
- Examine the role of embodiment in advancing toward AGI.
This workshop is Invitation-only. Please email Dr. IV Ramakrishnan (ram@cs.stonybrook.edu) to attend.
Read the announcement: https://mcusercontent.com/237207911c0fd4c1f78dd8524/files/070dec2e-a2f5-143e-0fe2-c4ebecdb5193/Embodied_AI_Workshop_Invitation_.pdf
The Fourth Arabic Natural Language Processing Conference (ArabicNLP 2026) is organized by the ACL Special Interest Group on Arabic NLP (SIGARAB).
The research focus of ArabicNLP is, naturally, Arabic, a collection of language varieties, from Classical to Modern Standard Arabic (MSA), and including many living and historical Arabic dialects. Arabic poses many challenges for the field of computational linguistics, including rich morphology, orthographic ambiguity as well as the wide variety of understudied dialects.
Location: Budapest, Hungary
Register here.
The research focus of ArabicNLP is, naturally, Arabic, a collection of language varieties, from Classical to Modern Standard Arabic (MSA), and including many living and historical Arabic dialects. Arabic poses many challenges for the field of computational linguistics, including rich morphology, orthographic ambiguity as well as the wide variety of understudied dialects.
Location: Budapest, Hungary
Register here.