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
The SUNY Office of Research, Innovation & Economic Development (ORIED) is hosting a webinar, Pathways to Innovation: Exclusive STEM Opportunities for Students at Premier Labs, with the Air Force Research Laboratory (AFRL), the Griffiss Institute and Brookhaven National Laboratory (BNL).

Please join us on October 30 from 12:30 - 2:00 pm to learn more about the labs and the wide variety of research, education, and workforce development programs they offer.

Register here: https://rfsuny.zoom.us/webinar/register/WN_fjWNU9l8Sr6WO_M3AoZ-Rw?mc_cid=50c2045945&mc_eid=357e15f9df#/registration
AI for Conservation: AI and Humans Combating Extinction Together by Daniel I. Rubenstein of Princeton University

ABSTRACT: The state of our planet is not good. We have lost more than 60% of the world's wildlife. Stopping the decline remains a challenge, especially since acquiring appropriate knowledge is expensive, time consuming and risky. Visual observations following the fates of a few individuals was the currency of the realm. But GPS technology and now machine learning provide a non-invasive scalable alternative. Photographs, taken by field scientists, tourists, automated cameras and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation coordinated by a non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high-resolution information databases, enabling scientific inquiry, conservation and citizen science.

BIO: Dan Rubenstein is the Class of 1877 Professor of Zoology. He is currently Director of Princeton's Environmental Studies Program and is former Chair of Princeton University's Department of Ecology and Evolutionary Biology and Director of Princeton's Program in African Studies. He is a behavioral ecologist who studies how environmental variation and individual differences shape social behavior, social structure, sex
roles and the dynamics of populations. He has special interests in all species of wild horses, zebras and asses, and has done field work on them throughout the world identifying rules governing decision-making, the emergence of complex behavioral patterns and how these understandings influence their management
and conservation. In Kenya he also works with pastoral communities to develop and assess impacts of various grazing strategies on rangeland quality, wildlife use and livelihoods. He has also developed a scout program for gathering data on Grevy's zebras and created curricular modules for local schools to raise awareness about the plight of this endangered species. He engages people as 'Citizen Scientists' and has recently extended his work to measuring the effects of environmental change, including issues pertaining to the global commons
and changes wrought by management and by global warming, on behavior.
Abstract: Graphs are a universal language of science. Molecules, materials, quantum systems, and knowledge bases can all be naturally represented as graphs. This talk explores how graph-based artificial intelligence is emerging as a powerful engine for scientific discovery. Using molecular design as a guiding example, we examine how modern graph AI enables machines not only to analyze complex scientific structures but also to generate new ones. We will discuss graph neural networks for learning predictive models of molecular properties, graph generative models for constructing novel chemical structures, and emerging multimodal graph-language models that support inverse design and synthesis planning. Together, these advances make graph AI more scalable, interpretable, and data-efficient--key capabilities for real-world scientific discovery. As artificial intelligence enters the era of foundation models, the next frontier lies in multimodal reasoning. Scientific knowledge is not purely textual; it is expressed through structures, code, and experimental data. By integrating graph representations with large language models, we move toward AI systems that can reason across multiple modalities and engage with scientific knowledge in its native forms. Looking ahead, we envision AI systems that behave less like tools and more like collaborators in the scientific process--generating hypotheses, designing candidate structures, planning experiments, interpreting results, and iteratively refining ideas through cycles of success and failure. In this vision, multimodal and agentic AI will enable scientists to explore vast and previously inaccessible design spaces, accelerating breakthroughs across domains ranging from drug discovery and materials innovation to software systems and quantum technologies.

Bio: Jie Chen is an interdisciplinary researcher working at the intersection of computing and mathematics, with a current focus on foundation models and AI agents for scientific discovery. His research integrates machine learning, statistics, scientific computing, and numerical linear algebra, with contributions spanning graph neural networks, multimodal graph LLMs, graph structure learning, scalable Gaussian processes, graph coarsening, and matrix functions. He is widely recognized for transformative contributions to graph-based deep learning and large-scale statistical modeling, and for bridging theory with real-world scientific and engineering applications. Dr. Chen has led externally funded, multi-institutional research programs supported by Shell, Evonik, and the U.S. Department of Energy, with applications in materials discovery, financial forensics, and power system resilience. He previously served as a Senior Research Scientist and Manager at IBM Research and the MIT-IBM Watson AI Lab, and as a Postdoctoral Fellow at Argonne National Laboratory. He has published extensively in top-tier AI, statistics, and applied mathematics venues, and his work has been recognized by multiple IBM Outstanding Technical Achievement Awards and the SIAM Student Paper Prize. He earned his Ph.D. in Computer Science from the University of Minnesota and his B.S. in Mathematics with honors from Zhejiang University.

Location: NCS 120
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
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.
Join the Department of Computer Science as we welcome Lyle Ungar, University of Pennsylvania, who will be delivering a lecture on 'Measuring Cultural Variation using Natural Language Processing.' When: 11/08/24 @ 2:30 PM Where: New Computer Science Building, Room 120. Reception to follow. Abstract: Cultures vary widely in how they view the world, for example being more individualist or collectivist. Such cultural differences are, of course, reflected in the words that people use. We first show a variety of ways in which multilingual language models are not multicultural; they speak Hindi or Mandarin, but still think like Americans. In contrast, we then present a scalable method that uses embedding-derived lexica to successfully measure regional variation in culture. Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he also holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group uses natural language processing and explainable AI for psychological research, including analyzing social media and cell phone sensor data to better understand the drivers of physical and mental well-being. They are currently building socio-emotionally sensitive GPT-based tutors and coaches.
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.
AI Institute Seminar Title: A Geometric Understanding of Deep Learning Abstract: This work introduces an optimal transportation (OT) view of generative adversarial networks (GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold distribution principle: the distribution of a class of data is close to a low-dimensional manifold. GANs mainly accomplish two tasks: manifold learning and probability distribution transformation. The latter can be carried out using the classical OT method. From the OT perspective, the generator computes the OT map, while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution; both can be reduced to a convex geometric optimization process. Furthermore, OT theory discovers the intrinsic collaborative--instead of competitive--relation between the generator and the discriminator, and the fundamental reason for mode collapse. We also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT map for probability distribution transformation. This AE-OT model improves the theoretical rigor and transparency, as well as the computational stability and efficiency; in particular, it eliminates the mode collapse. The experimental results validate our hypothesis, and demonstrate the advantages of our proposed model.