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
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.

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
Paul Fain is co-founder of Work Shift, editor of the must-read newsletter, The Job, and host of The Cusp podcast. A veteran higher education reporter, Paul is perhaps the nation's top journalist focused on connections between education and work. He started Work Shift after a decade as a senior reporter and then news editor at Inside Higher Ed, where he led the outlet's coverage of low-income and first-generation students, college completion, community colleges, federal policy, and emerging models of higher education. He also was the founding host of the successful podcast, The Key with Inside Higher Ed, and has contributed chapters for books on innovation in higher education, published by the Harvard University Press and the Stanford University Press. Earlier in his career, Paul was a senior reporter at The Chronicle of Higher Education.

Limited Seats!

Registration is required.
Recently, large-scale language data combined with modern machine learning techniques have shown strong value as means for studying human psychology and behavior. For example, language alone has been shown predictive in mental health, personality, and health behaviors. However, many applications for such language-based assessments have readily available and important data beyond language (i.e. extra-linguistics), such as predicting the subjective well-being of a community using tweets, where one can take into account their age, education, and demographic attributes. Language may capture some characteristics while extra-linguistic variables captures others. We believe that effectively integrating linguistic and extra-linguistic data can yield benefits beyond either independently. In this thesis, we develop methods which effectively integrate extra-linguistic data with language data focused primarily on social scientific applications. The central challenge is dealing with the size and heterogeneity of, often sparse and noisy, language data versus the, often low-dimensional and non-sparse, extra-linguistic variables. First, we consider structured extra-linguistics, like socioeconomic (income and education rates) and demographics (age, gender, etc.), and propose two integration methods, named residualized controls (RC) and residualized factor adaptation (RFA), to be used in county-wise prediction tasks. Demonstrating techniques that integrate information at both the model-level and data-level, we found consistently strong improvement over naively combining features, for example, increasing county level well-being predictions by over 12%. Next, we consider unstructured extra-linguistic data. In the first part, we incorporate social network connections and language over time to propose a novel metric for quantifying the stickiness of words - their ability to spread across friendship connections in a social network over time (or in other words, stick in ones vocabulary after seeing friends use it). We obtain which language features are more probable to disseminate through friendship and show such a metric is useful for predicting who will be friends and what content will spread. In addition, we analyze language content over time by proposing a novel dynamic content-specific topic modeling technique that can help to identify different sub-domains of a thematic scope and can be used to track societal shifts in concerns or views over time.
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.
Abstract: Generative image models trained on massive datasets encode the statistics of our visual world. An off-the-shelf diffusion or flow-matching model can therefore serve as a general-purpose image prior, providing information about images across a wide range of tasks. Harnessing these capabilities, however, requires combining the model at inference time with additional signals or constraints unknown during training. This thesis will develop training-free methods for inference under a generative denoising prior. I first show how sampling from a trained denoiser can be formulated as an optimization problem, which combined with a constraint, can solve tasks ranging from conditional generation and weakly supervised segmentation to combinatorial optimization. I then demonstrate how the inherent properties of denoisers can accelerate inference under such constraints. Replacing gradient descent with an inexact Newton update, based on the symmetry of the denoiser's Jacobian, substantially reduces inference costs without tradeoffs. I also explore a middle ground between model adaptation and fully training-free inference by using the denoiser's robust internal representations to learn constraints from limited labeled data. These methods are applied to gigapixel image domains such as digital histopathology and remote sensing, where generative models can only be trained at a patch scale. To synthesize arbitrarily large images at resolutions unseen during training, I introduce inference-time algorithms that enforce consistency across spatially overlapping patches and image scales. Finally, I propose repurposing inference-time algorithms from sampling tools, to mechanisms for understanding the prior learned by a denoising model. Extending the previously developed techniques, I analyze the Jacobians of generative denoisers, where preliminary results indicate that Jacobian spectra correlates with generative quality. This motivates a Jacobian-spectrum regularization as a way to improve model performance using insights derived from inference-time algorithms.

Speaker: Alexandros Graikos

Location: NCS 220

Talk Title: Knowledge-enhanced LLMs and Human-AI Collaboration Frameworks for Creativity Support


Abstract:

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing and Artificial Intelligence. To build AI systems that are human-centered, I propose we need knowledge-aware models and human-AI collaboration frameworks to help them solve tasks ultimately aligning these models better with human values. In this talk, I will discuss my research agenda for human-centered AI with a case study on creativity that focuses on how to augment LMs with external knowledge, build effective human-AI collaboration frameworks as well as theoretically grounded robust evaluation protocols for measuring capabilities of NLG systems. I will begin by describing knowledge-enhanced methods for creative text generation such as metaphors. Next, I will describe how content creators can collaborate and benefit from the creative capabilities of text-to-image-based AI models. Finally, I will focus on the design and development of theoretically grounded evaluation protocols to benchmark the creative capabilities of Large Language Models in both producing as well as assessing creative text. I will end this talk by highlighting the current limitations of existing models and future directions toward building better models that will enable efficient and trustworthy human-AI collaboration systems.


Bio:

Tuhin Chakrabarty is a final-year Ph.D. candidate in the Natural Language Processing group within the Computer Science department at Columbia University. His research is supported by the Columbia Center of Artificial Intelligence & Technology (CAIT) & an Amazon Science Ph.D. Fellowship. He was also a Computational Journalism fellow at NYTimes R&D and an intern at the Allen Institute of Artificial Intelligence, Salesforce Research, and Deepmind. His research interests are broadly in Natural Language Processing, Computer Vision, and Human-Computer Interaction with a special focus on Human-Centered Methods for Understanding, Generation, and Evaluation of Creativity. His work has been recognized at top natural language processing and human-computer interaction conferences and journals such as ACL, NAACL, EMNLP, TACL, and CHI. He has been involved in organizing several workshops and tutorials at NLP conferences such Figurative Language Processing workshop at EMNLP 2022, NAACL 2024, and the tutorial on Creative Text Generation at EMNLP 2023. His work on AI and creativity has been mentioned in mainstream news media such as The Hollywood Reporter and more recently The Washington Post.

Join Zoom Meeting https://stonybrook.zoom.us/j/97103601583?pwd=TnpGMXdpeEd1N0hZcXppS1BLNFJhZz09 (ID: 97103601583, passcode: 004031) Join by phone (US) +1 646-931-3860 (passcode: 004031) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DILacj94mRvSXgTYt0Cqs1w%253D%253D%26signature%3D9f2f1e7e603bbcb9034724d084eea8846c19a38b7436180170dfc3f1d718b425%26v%3D1&sa=D&source=calendar&usg=AOvVaw3MsNgLSPMRl8L5i6BosYrB Meeting host: H.Andrew.Schwartz@stonybrook.edu

Join Zoom Meeting:
https://stonybrook.zoom.us/j/97103601583?pwd=TnpGMXdpeEd1N0hZcXppS1BLNFJhZz09

Join Stony CELT for a focused, one-hour overview on how to redesign and future-proof assessments in the age of AI. This session will cover three key areas: leveraging AI as a co-pilot for developing effective exam questions, designing authentic assessments, and exploring how AI can strategically support active learning structures like Team-Based Learning (TBL), Project-Based Learning (PBL), and Scenario-Based Learning (SBL).

Register here to attend.

Virtual Talk: Metadata Matters: Robust Document Classification via Adaptation Methods for Text-driven Public Health by Xiaolei Huang

Zoom link to follow.

Abstract: Document classifiers have been widely applied in solving health-related issues, such as suicide prevention, flu vaccination surveillance and disease diagnosis. However, document metadata including time, gender, age and location has an enormous impact on robustness of 
document classifiers. Language varies across the metadata bringing both challenges and opportunities to build reliable document classifiers. For example, online written language changes over time, and males and females express opinions differently. This talk describes how to use domain adaptation to integrate temporal and user demographic factors into document classifiers. By adapting knowledge of how language varies across the metadata, models can learn generalized representations of language through the metadata-invariant embeddings. 
This approach will lead to metadata-adapted document classifiers and can also extend to personalize classification models by user embedding. 

Bio: Xiaolei Huang is a 4th-year PhD candidate in Information Science at the University of Colorado, Boulder. He is currently a visiting scholar at the Johns Hopkins University. His research interests are in Natural Language Processing, Machine Learning and Public Health. Particularly, he focuses on domain adaptation, cross-lingual transfer learning, user modeling and fairness.