https://stonybrook.zoom.us/j/94414957054?pwd=V1JMc2EwSnVGMFdaUlNobE9DSHU4dz09#success
ID: 94414957054
Password: 094758

Speaker: Heather J. Lynch


Bio:  Dr. Heather J. Lynch is an Associate Professor of Ecology & Evolution at Stony Brook University. Prior to Stony Brook, Dr. Lynch was an Adjunct Professor of Applied Math and Statistics at UC Santa Cruz and a Research Scientist in the Biology Department at the University Maryland. Dr. Lynch received her A.B. in Physics from Princeton University in 2000, an A.M. in Physics from Harvard University in 2004, and a Ph.D. in Organismal and Evolutionary Biology from Harvard University in 2006. Dr. Lynch's research is focused on spatial population dynamics of Antarctic penguins, with a particular focus on statistical and mathematical models to integrate patchy time series with remote sensing imagery. These data will allow Dr. Lynch and colleagues to develop mathematical models to explore how coloniality constrains the colonization and extinction of individual habitat patches and, ultimately, the metapopulation dynamics of colonial seabirds.   
We live in a new scientific paradigm: the Big Data era, in which a lot of data is available for almost anything. In this new paradigm, the driving force is to use data directly to learn about chemical and physics systems employing artificial intelligence. This paradigm has proven helpful in simulating realistic physical, biological, and chemical models, yielding impressive results. Similarly, the insight gained in these situations can be used to improve our understanding of fundamental processes. In that regard, we want to answer the question: Can a machine learn chemistry? The answer to this question is still debatable, but we will show our ideas and methods to find the answer. We will also discuss our results on predicting atom-diatom reactions and other avenues and work in progress in our group.

Please register for the STEM Speaker Series: Can a Machine learn Chemistry here.

Abstract:
Deep learning models have achieved remarkable success across a wide range of computer vision tasks, including image classification, semantic segmentation, etc. However, such success highly relies on a large amount of annotated data, which are expensive to obtain. Moreover, their performance often degrades when there exist distribution shifts between training and test data. Domain Adaptation overcomes these issues by transferring knowledge from a label-rich source domain to a related but different target domain. Despite its popularity, domain adaptation is still a challenging task, especially when the data distribution shifts are severe, while the target domain has no or few labeled data.

In this thesis, I develop four efficient domain adaptation approaches to improve model performance on the target domain. Firstly, inspired by the large-scale pretraining of Vision Transformers, I explore Transformer-based domain adaptation for stronger feature representation and design a safe training mechanism to avoid model collapse in the situation of a large domain gap. Secondly, I observe that source models have low confidences on the target data. To address this, I focus on the penultimate activations of target data and propose an adversarial training strategy to enhance model prediction confidences. Thirdly, I study using weak supervision from prior knowledge about target domain label distribution. A novel Knowledge-guided Unsupervised Domain Adaptation paradigm is devised, and a plug-in module is designed to rectify pseudo labels. Lastly, I step into the task of Active Domain Adaptation, where the labels of a small portion of target data can be inquired. I propose a novel active selection criterion based on the local context and devise a progressive augmentation module to better utilize queried target data. The robustness of domain adaptation approaches, in addition to accuracy, is critical yet under-explored. To conclude the thesis, I empirically study set prediction in domain adaptation using the tool of conformal prediction and conformal training.


Location: New Computer Science Bldg., Room 120
Zoom Link: https://stonybrook.zoom.us/j/92736258273?pwd=ipDdh1CTG6dRYmqa3ltUvooei8OfaT.1Meeting ID: 927 3625 8273
Passcode: 466399
Abstract:

What is the nature of linguistic knowledge, and how is it acquired from limited data? In recent years, the program of subregular linguistics has identified formal language classes expressive enough to account for most phenomena in natural language but also sufficiently limited to be efficiently learned from positive data. An advantage to these formal learning algorithms is that they come with mathematically proven guarantees about their performance, and it is easy to reason about how and why they behave the way they do.

In this talk, I discuss the Multi Tier-based 2-Strictly Local Inference Algorithm (MT2SLIA), which probably learns the syntactically relevant class of 2-Factor Muti Tier-based Strictly Local (2FMSTL) tree languages. This algorithm efficiently learns from a polynomially-sized sample of positive data by identifying missing substructures and generalizing these as constraints over tiers in a principled manner.

I will introduce a working prototype implementation of this algorithm and demonstrate its behavior on a curated sample of natural language data to show how it can learn relevant syntactic patterns.

Bio:

Logan Swanson is a third year PhD student in the Department of Linguistics at Stony Brook University. He is advised by Dr. Jefferey Heinz and Dr. Thomas Graf. His interests include learning theory, computational syntax, and language change. His current research focuses on understanding the learning-theoretic elements of natural language by designing, implementing, and testing learning algorithms for linguistically relevant formal language classes.

*Please note: this seminar will be held in person (IACS Seminar Room w/ food provided) and online.

Join Zoom Meeting
https://stonybrook.zoom.us/j/95707958315?pwd=6ITUJ0ffCXjRJb4wpt0KMDTApfSLZ0.1

Meeting ID: 957 0795 8315
Passcode: 920473
Abstract: As we enter the AI era, domain scientists face a critical question: What can we do to harness AI effectively for scientific discovery? AI has demonstrated remarkable capabilities, from accelerating simulations to uncovering hidden patterns in complex datasets. While these advancements offer unprecedented opportunities, they also raise concerns--AI models often function as black boxes, making it difficult to connect their outputs to established scientific principles. This lack of interpretability can undermine trust and limit adoption, particularly in fields like meteorology where physical understanding is critical.
In this talk, I will explore how interpretable AI can bridge this gap, highlighting its potential to generate explicit, physically meaningful equations rather than opaque neural networks. Through four case studies from my lab, I will showcase how interpretable AI can enhance scientific understanding:
  1. Satellite Precipitation Retrieval: Using AI-based approaches to interpret precipitation retrieval algorithms from AMSU data, we identified critical microwave channels (89 and 150 GHz) that directly link to physical processes in the atmosphere.
  2. Quantitative Precipitation Estimation (QPE): By applying symbolic regression models to polarimetric radar data, we derived mathematical expressions that outperform traditional Z-R relationships and existing QPE algorithms, offering new insights into rainfall microphysics.
  3. Tornado Probability Prediction: Leveraging reinforcement learning-based symbolic deep learning models, we developed interpretable equations that outperform the traditional Significant Tornado Parameter (STP) index, providing a clearer understanding of the relationships between key atmospheric variables and tornado risk.
  4. Domain-Aware Symbolic Regression for Scientific Equations: In our latest work, we introduced a symbolic regression framework that incorporates domain-specific symbol priors extracted from thousands of scientific publications. By encoding common mathematical structures--such as the prevalence of trigonometric functions in physics or logarithmic forms in biology--into a tree-structured reinforcement learning model, we improved both the accuracy and interpretability of discovered equations. This approach accelerates convergence, enforces physical plausibility, and reveals new governing relationships in climate and geophysical data.
Through these examples, I hope to spark discussion on the evolving role of domain scientists in the AI era and inspire new ways to integrate AI with physical understanding in atmospheric research.

IACS Seminar Speaker: Yixin Wen, University of Florida

Location: IACS Seminar Room or Zoom

Join Zoom Meeting: https://stonybrook.zoom.us/j/97596399106?pwd=0PBvElFLqov3biO6OlQxSWLWudkIuH.1
Meeting ID: 975 9639 9106
Passcode: 096213
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 Ph.D. program or (2) receive permission from the instructors. Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend. The seminar will be taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu.

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

AI and Edge Processing Co-Design for Radiation Detectors

Abstract: Artificial Intelligence (AI) offers exciting new opportunities for enhancing the performance of radiation detectors, ultimately leading to improved physics outcomes. Furthermore, with the explosive growth in data rates being seen by next-generation radiation detectors, deployment of AI algorithms at the edge by embedding intelligence within or near the detector front-end can be transformative. Such integration enables real-time data filtering, noise suppression, feature extraction, and adaptive control, while reducing downstream bandwidth and power consumption. This talk will cover three efforts that bring AI to the forefront of detector technology. First, we demonstrate how AI-based algorithms can be used for position reconstruction in virtual Frisch-grid (VFG) detectors by compensating for charge transport distortions and detector non- uniformities, leading to significantly enhanced fidelity in imaging of gamma-ray interactions. Second, we present a smart readout application specific integrated circuit (ASIC) that combines digital signal processing with co-designed artificial neural networks to enable on-chip regression and classification of detector signals, while meeting stringent constraints on accuracy, speed, and area. Finally, we introduce our recent efforts related to the development of electro-photonic processing architectures that integrate CMOS electronics and silicon photonics for near-sensor AI acceleration. These architectures aim to leverage cross-disciplinary co-design from algorithms to hardware, to achieve low latency and energy-efficient processing of detector data.

Biography: Dr. Prashansa Mukim is an early-career researcher in the Instrumentation Department at BNL, where she works on the design of front-end electronics for extreme environments and the development of co-design methodologies for novel processing modalities and beyond-CMOS technologies. Prior to joining BNL, she was a post-doctoral researcher at the National Institute of Standards and Technology (NIST) in Maryland, where she focused on characterizing the properties of CMOS circuits at cryogenic temperatures and applications of spintronic devices for neuromorphic computing. She received her Ph.D. in Electrical and Computer Engineering from the University of California, Santa Barbara, in 2021.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1608585935?pwd=UemgEkqijfNf3vIJIGuOa2MdjsunaT.1

Meeting ID: 160 858 5935
Passcode: 076033

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)