Abstract:
People shift their visual attention to gather and prioritize information from their surroundings, helping them navigate complex environments. Understanding these attentional shifts involves decoding the features that guide where attention is directed (spatial areas of focus) and when attention shifts (timing). Decoding these processes can aid applications from interface design to medical diagnosis. However, prior models have not fully explored the underlying factors addressing these aspects. In this dissertation, we study the factors that guide visual attention across diverse image types, spanning natural images, graphic design documents, and whole slide images (WSIs) of cancer tissues, while also predicting visual attention based on these factors.
First, we propose a method to quantify object recognition uncertainty as a factor influencing spatio-temporal attention (where and when) in natural images. We found that it plays a larger role than bottom-up saliency in guiding visual attention. Second, we analyze graphic design documents such as webpages, comics, posters, mobile UIs, etc., which differ from natural images in that they are designed to convey specific messages or elicit desired viewer response. We propose a unified and interpretable deep learning model that predicts both static and dynamic visual attention behavior (addressing where and when) by integrating document layout and content saliency as factors, enhancing attention prediction performance. Finally, in the domain of digital pathology, we investigate pathologists' attention during their examination of giga-pixel WSIs of prostate cancer with an objective to aid in the development of computer-assisted pathology training and clinical decision support systems. Using a digital microscope interface, we collected the largest known dataset of pathologist attention, which allows us to study the factors that guide their spatial and temporal attention patterns (where and when) and develop predictive models. Our study explores key factors guiding their attention, including magnification, slide staining, the nature of the diagnostic task, and their expertise. Motivated by this analysis, we propose deep learning models to solve two tasks: 1) predicting pathologist attention via spatial (heatmaps) and spatio-temporal (scanpaths) models, and 2) inferring pathologist expertise level, both essential technical components towards developing an AI-assisted pathology training pipeline.

Speaker:
Souradeep Chakraborty

Location: New Computer Science Bldg., Room 220

Zoom Link: https://stonybrook.zoom.us/j/9755288447?pwd=TW95T2xqOUZjRnlqcnVFcUQvN0JMdz09
Meeting ID: 975 528 8447
Passcode: 338037

Communication-Efficient Heterogeneity-Aware Machine Learning System and Architecture by Xuehai Qian

ABSTRACT: The key success of deep learning is the increasing size of models that can achieve high accuracy. At the same time, it is difficult to train the complex models with large data sets. Therefore, it is crucial to accelerate training with distributed systems and architectures, where communication and heterogeneity are two key challenges. In this talk, I will present two heterogeneity-aware decentralized training protocols without communication bottleneck. Specifically, Hop supports arbitrary iteration gap between workers by novel queue-based synchronization which can tolerate heterogeneity with system techniques. Prague uses randomized communication to tolerate heterogeneity with a new training algorithm based on partial reduce -- an efficient communication primitive. If time permits, I will present the systematic tensor partitioning for training on heterogeneous accelerator arrays (e.g., GPU/TPU). We believe that our principled approaches are crucial for achieving high-performance and efficient distributed training.

BIO: Xuehai Qian is an assistant professor at University of Southern California. His research interests include domain-specific systems and architectures, performance tuning and resource management of cloud systems and parallel computer architectures. He received his PhD from the University of Illinois Urbana Champaign and was a postdoc at UC Berkeley. He is the recipient of W.J Poppelbaum Memorial Award at UIUC, NSF CRII and CAREER Award, and the inaugural ACSIC (American Chinese Scholar In Computing) Rising Star Award.

Abstract: The development of embodied AI has largely focused on scaling data and computational power, often at the cost of energy efficiency. In contrast, biological intelligence achieves remarkable adaptability with minimal resources, inspiring a shift toward neuromorphic AI, an approach that mimics the structure and dynamics of biological neural systems. In this talk, I will explore the promises and challenges of neuromorphic computer vision from three key perspectives: algorithms, robot actions, and data. First, I will discuss algorithmic advances, including continuous visual hull reconstruction, continuous-time human motion field estimation, and unsupervised independent motion segmentation. Next, I will illustrate how neuromorphic vision enables agile robotic actions by leveraging event-based perception for real-time decision-making. Finally, I will address challenges in training data-driven models with event data, highlighting strategies to enhance data availability and efficiency. By integrating these elements, neuromorphic AI paves the way for energy-efficient, high-performance embodied intelligence in dynamic real-world environments.

Speaker Bio: Ziyun (Claude) Wang is a fifth-year Ph.D. student in the General Robotics, Automation, Sensing & Perception (GRASP) Lab at the University of Pennsylvania, advised by Professor Kostas Daniilidis. His research focuses on developing algorithms for neuromorphic computer vision and integrating them with real hardware to enable agile perception in embodied AI systems. Prior to his Ph.D., he worked at the Samsung AI Center New York, where he developed 3D reconstruction techniques for robotic applications and earned three patents. He also contributed to the Apple Vision Pro team, enhancing user comfort for AR glasses. His research work has been recognized at major computer vision, robotics, and machine learning venues including the AAAI Conference on Artificial Intelligence (AAAI), European Conference on Computer Vision (ECCV), International Conference on Learning Representations (ICLR), Conference on Computer Vision and Pattern Recognition (CVPR) workshops, and IEEE Robotics and Automation Letters (R-AL), with an oral presentation at ECCV placing in the top 2.7%. His research aims to drive the development of next-generation bio-inspired AI systems, enabling more efficient, adaptive, and intelligent embodied perception.


Date of Event

Joel H. Saltz, MD, PhD
SUNY Distinguished Professor Cherith Professor and Founding Chair
Department of Biomedical Informatics
Stony Brook University

Apostolos K. Tassiopoulos, MD, FACS
Professor of surgery and vice chair for quality and outcomes Chief of the Division of Vascular and Endovascular Surgery
Director of the Stony Brook Vascular Center Stony Brook Medicine

Title: Clinical applications of artificial intelligence to improve diagnosis and risk stratification for patients with aortic aneurysms

Time: Wednesday, Feb 17, 2021 3 pm - 4 pm

Join Zoom Meeting
https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISj...
Meeting ID: 956 1719 7636 Passcode: 924293

Description:

As artificial intelligence and data science reshape the global information landscape, libraries are emerging as key players in both technological innovation and ethical stewardship. This international Zoom discussion brings together library professionals and educators from the U.S., Philippines, and Hong Kong to explore how institutions are integrating AI and data into their pedagogy and services.

Panelists will share concrete examples from their own libraries--ranging from data literacy initiatives to increasing discoverability. The conversation will also examine regional trends in librarianship, spotlighting how institutions in Asia are navigating the evolving role of data and AI.

Join us for a global conversation that highlights the transformative potential of libraries as hubs for innovation and critical inquiry in the age of AI.

Register for this free Zoom panel.

Panelists:

Ahmad Pratama is a Faculty Member and Associate Librarian at Stony Brook University Libraries, where he is working to build a comprehensive, campus-wide data literacy program within the Libraries. As the Data Literacies Lead, his work focuses on empowering students, faculty, and staff to critically and ethically engage with data and AI, including the development of a credit-bearing course in Critical Data & AI Literacies supported by an EDGE Fund Award from the Provost's Office. Previously, Dr. Pratama served as an Associate Professor of Information Technology, and his research and teaching explore the intersections of technology, policy, and society with a focus on data, AI, and innovation in higher education.

Dan Anthony Dorado is a full-time faculty member at the U.P. School of Library and Information Studies, where he teaches information technology, management and marketing, research methodology, and quantitative research. He was also the director of the Diliman Learning Resource Center under the Office of the Vice Chancellor for Student Affairs. Before that, he was an Information Specialist at the College of Engineering Library, in charge of the System and Network Administration and The Learning Commons. He completed his master's degree at the Technology Management Center in U.P. Diliman and is currently pursuing his PhD in Data Science. As a member of Sync.Bio.Optics laboratory and the Publics, Archives, and Data (PANDA) Lab, his research specialization covers Computational Methods, Open Education, Critical Data Studies, and Radical Statistics.

Ryun LEE is Associate University Librarian at The Chinese University of Hong Kong Library, leading Digital Initiatives and Library IT and Systems. He drives digital innovation through emerging technologies, particularly artificial intelligence to enhance services, streamline operations, and support CUHK's mission in research, education, and knowledge advancement. With a background in cataloging and digital repository development, Ryun leads projects in digitization, OCR, data visualization, text and network analysis, GIS, and digital scholarship. He actively promotes knowledge graph applications in Hong Kong studies and oversees efforts to digitize and preserve resources related to Hong Kong and Southern China. His recent work focuses on creating seamless digital experiences and developing data-driven infrastructure. He is currently exploring AI-driven approaches to digitization workflows and entity extraction, aiming to improve access, discovery, and long-term preservation of library materials.

Visual Analytics and Machine Learning for Biomedical Imaging Diagnosis

 

Arie Kaufman

 

We present an integrated approach using visual analytics and machine learning (ML) to diagnose abnormalities in 3D radiological imaging and biological microscopes. The primary example will involve 3D virtual pancreatography (VP), a novel visualization-ML procedure and application for non-invasive diagnosis and classification of pancreatic lesions, the precursors of pancreatic cancer. Currently, non-invasive screening of patients is performed through visual inspection of 2D axis-aligned CT images, though the relevant features are often not clearly visible nor automatically detected. VP is an end-to-end visual diagnosis system that includes an ML-based automatic segmentation of the pancreatic gland and the lesions, a semi-automatic approach to extract the primary pancreatic duct, an ML-based automatic classification of lesions into four prominent types, and specialized 3D and 2D exploratory visualizations of the pancreas, lesions and surrounding anatomy. We combine volume rendering with pancreas- and lesion-centric visualizations and measurements for effective diagnosis. We designed VP through close collaboration and feedback from expert radiologists, and evaluated it on multiple real-world CT datasets with various pancreatic lesions and case studies examined by the expert radiologists. Other applications include virtual colonoscopy, COVID-19, pathology, brain neurites, etc.


Biography: Arie Kaufman is Distinguished Professor and formerChair of the Department of Computer Science at Stony Brook University, where he is also Director of the Center for Visual Computing (CVC), and Chief Scientist at the Center of Excellence in Wireless and Information Technology (CEWIT). 

He received his PhD in Computer Science at Ben-Gurion University of the Negev in 1977.   He is known for his work in visualization, graphics, virtual reality, user interfaces, multimedia, and their applications, especially in bio-medicine. He is especially well known for his work on the 3-dimensional virtual colonoscopy, a revolutionary low-risk technique for colon cancer screening, and for pioneering the use of Graphics Processing Units (GPUs) and GPU-clusters. In 2012, he presided over the development and opening of the Reality Deck, the largest virtual reality display in the world, at Stony Brook University.

Kaufman was the founding Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG), co-founded the IEEE Visualization Conference and Volume Graphics series, and is currently the director of IEEE Computer Society Technical Committee on Visualization and Graphics. He is an IEEE Fellow, ACM Fellow, winner of many awards, including the IEEE Visualization Career Award, and member of the European Academy of Sciences.



Steven Skiena is inviting you to a scheduled Zoom meeting.

Topic: AI Seminar: Arie Kaufman
Time: Apr 21, 2021 10:00 AM Eastern Time (US and Canada)

Join Zoom Meeting
https://stonybrook.zoom.us/j/96017498640?pwd=SE0rdHB6ZVlCM2ZpY2RnRUxyVnR3Zz09

The Future of Learning: Rethinking Practice in a Changing World

Thursday, March 26, 2026 (Workshops)
Friday, March 27, 2026 (Symposium)

Open to Stony Brook University Faculty, Staff, and Graduate Students. Hosted by the Center for Excellence in Learning and Teaching, Office of the Provost.

Thursday, March 26, 2026
Workshop: AI Tools and Techniques
  • Open to all faculty & staff
  • Hands-on, exploratory
  • Registration only limited to the size of the room
  • Location: In-person, TBD
  • Time: 10 AM - 12 PM
  • Registration required

Friday, March 27, 2026
Keynote: Teaching and Thinking with AI
  • Faculty, TAs, postdocs, and academic staff
  • In-person on-campus conference venue
  • Location: SAC Balroom
  • Time: 9 AM - 3 PM
  • Registration required

Keynote Speaker: José Antonio Bowen

José Antonio Bowen has been leading innovation and change for over 40 years at Stanford, Georgetown and the University of Southampton (UK), as a dean at Miami University and SMU and as President of Goucher College. Bowen has worked as a musician with Stan Getz, Dave Brubeck, and many others and his symphony was nominated for the Pulitzer Prize in Music (1985).
Bowen holds four degrees from Stanford and has written over 100 scholarly articles and books, including the Cambridge Companion to Conducting (2003), Teaching Naked (2012 and the winner of the Ness Award for Best Book on Higher Education), Teaching Naked Techniques with C. Edward Watson (2017) and Teaching Change: How to Develop Independent Thinkers using Relationships, Resilience and Reflection (Johns Hopkins University Press, 2021).
Bowen has appeared in The New York Times, Forbes, The Wall Street Journal, and has three TED talks. Stanford honored him as a Distinguished Alumni Scholar (2010) and he has presented keynotes and workshops at more than 300 campuses and conferences 46 states and 17 countries around the world. In 2018, he was awarded the Ernest L. Boyer Award (for significant contributions to American higher education). He is a senior fellow for the American Association of Colleges and Universities.

Register here.