Abstract: The rapid growth of observational data presents unprecedented opportunities to enhance both the predictability and mechanistic understanding of Earth systems. However, fully harnessing big Earth data needs computational frameworks that bridge the gap between physics-based models and machine learning. In this talk, I will first demonstrate how AI methods can significantly improve the prediction of environmental systems. Despite their predictive accuracy, machine learning models often lack physical interpretability, limiting their ability for scientific inquiry. To address this, I will introduce the developed hybrid, differentiable modeling framework that unifies physical models with machine learning in an end-to-end trainable system. This framework autonomously learns from large observations while maintaining physical clarity. The machine learning components can be seamlessly embedded into physical backbones to assimilate multi-source data, support automatic parameterization, and represent uncertain processes. I will showcase applications of this framework in simulating and understanding the terrestrial water cycle and its interactions with ecosystems at continental and global scales. This talk will highlight how differentiable modeling not only improves the modeling ability in both data-rich and data-scarce scenarios, but also provides a systematic pathway to enhancing model structures, deciphering uncertain physical relations, and facilitating knowledge discovery in Earth system sciences.


IACS Seminar Speaker: Dapeng Feng, Stanford Univeristy

Location: IACS Seminar Room

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.

Get hands-on with data cleaning techniques using Python and AI tools. Join SBU Libraries' Data Literacies Lead, Ahmad Pratama, to learn how to identify and rectify errors, handle missing data, and prepare your dataset for analysis. This workshop introduces you to powerful yet easy-to-use tools and techniques that make data cleaning efficient and effective, turning chaotic data into valuable insights.

Please register for the Data Cleaning with Python and AI here.
Are you concerned about AI issues with your asynchronous online courses? Is your fully online course vulnerable to AI plagiarism? Do you want to engage your online students using AI? Discover the future of education with our AI-powered solutions designed specifically for online asynchronous courses. This innovative approach uses artificial intelligence to transform the way courses are delivered, making learning more personalized, engaging, and effective.

Register here: https://stonybrook.zoom.us/meeting/register/RD94cHiHRwCj6xNkCZqNEg
Presented by Stony Brook University Department of Biomedical Informatics and Long Island Network for Clinical and Translational Science (LINCATS).

The seminar aims to empower participants with the knowledge and skills necessary to harness AI effectively in clinical practice and research. It will equip attendees with practical insights, case studies, and interactive discussions led by experts in both AI and medicine, fostering a collaborative environment where attendee can explore how to overcome barriers and maximize the potential of AI in transforming modern healthcare delivery.

All Stony Brook Audiences Welcome.
Please note: This exciting event is open to all Stony Brook Faculty/Staff/Students. While the overarching theme for this event is the application of AI in medicine, the event is designed to bridge the professional practice gap that exists between cutting-edge AI research and its practical implementation in clinical settings, While AI holds immense promise for transforming healthcare delivery, many physicians and researchers lack the foundational knowledge and practical skills needed to effectively integrate AI into their daily practices.

THIS CONFERENCE IS FOR STONY BROOK UNIVERSITY & HOSPITAL FACULTY/STAFF & STUDENTS ONLY.


Registration link: https://cme.stonybrookmedicine.edu/continuing-medical-education/conferences/235/bench-to-bedside-understanding-the-practical-application-of-ai-in-medicine-2024/10/17/2024

FOR QUESTIONS
joseph.cesaria@stonybrookmedicine.edu
mary.saltz@stonybookmedicine.edu

Abstract:
Deep neural network (DNN) is a powerful tool for solving image processing and computer vision tasks such as image and video reconstructions, object recognition, and scene understanding, etc. However, DNN have been used for only the digital domain in the imaging pipeline, such as the feature extractor and classifier models after an image is captured and digitized. In this research, we propose a new framework called deep sensing. The proposed framework also models the analog layer to the neural network model and jointly optimizes the parameters in optics and sensor designs of a camera, as well as reconstruction and classification models by the same training strategy.

Bio:
Hajime Nagahara is a professor at D3 Center, Osaka University, since 2024. He received Ph.D. degree in system engineering from Osaka University in 2001. He was a research associate of the Japan Society for the Promotion of Science from 2001 to 2003. He was an assistant professor at the Graduate School of Engineering Science, Osaka University, Japan from 2003 to 2010. He was an associate professor in Faculty of Information Science and Electrical Engineering at Kyushu University from 2010 to 2017. He was a professor at Institute for Datability Science, Osaka University, from 2017 to 2024. He was a visiting associate professor at CREA University of Picardie Jules Verns, France, in 2005. He was a visiting researcher at Columbia University in 2007-2008 and 2016-2017. Computational photography and computer vision are his research areas. He received IPSJ Nagao Special Researcher Award in 2012, ICCP2016 Best Paper Runners-up, and SSII Takagi Award in 2016. He is a program chair for ICCP2019, General Chair for upcoming ACCV2026 Osaka, Associate Editor for IEEE Transactions on Computational Imaging in 2019-2022, Associate Editor for IEEE Transactions of Pattern Recognisiton and Machine Intelligence, and Director of Information Processing Society of Japan in 2022-2024.

Zoom: https://stonybrook.zoom.us/j/4742917886?pwd=YiW3AL5lvPlEaWrd221YfLzYhlgsRK.1

AI on Campus: Your Thoughts, Your Future

Join the Conversation: Share Your Thoughts about Learning, Academics, and AI

The world of college is changing fast, and Artificial Intelligence (AI) is at the center of it. We are part of the Institute on AI, Pedagogy, and the Curriculum with AAC&U, and we need to hear from the people AI affects most: you!

This is an open discussion for all students to share their honest experiences, their top concerns, and their best ideas about AI in our academic environment. We'll be diving into these key questions:

  • How can AI actually make learning better or easier? What opportunities do you see for using AI tools to enhance your assignments, research, or skills?

  • What are your biggest worries about AI? Is it about cheating, being graded fairly, or preparing for the job market? How is AI impacting your workload or stress levels?

  • What specific tools, workshops, or policies would help you use AI responsibly and successfully? (Think training, software, or clear rules.)

Dates/Times:

  • Wednesday, 2/4 at 2pm

  • Thursday, 2/5 at 12pm

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

Don't worry if you can't attend! You can still share your thoughts via video in our AI Zoom Room or via email: rose.tirotta-esposito@stonybrook.edu.

Videos will not be shared publicly and comments will only be shared in aggregate.

Your voice matters. Come tell us how AI is affecting your studies, your stress, and your success!

  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)

  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)

  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)

  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)

  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)

What comes after today's large language models and deep neural networks? Join the Computing Community Consortium (CCC) for a virtual 30-min community chat led by David Jensen, CCC Council Member and lead author of the new CCC whitepaper, Envisioning Possible Futures for AI Research. Jensen will explore paradigm-shifting AI Research Futures like Neuro-Symbolic, Embodied, Multi-Agent, and Quantum AI, and then open the floor to the audience for an engaging Q&A discussion.

Register here.