Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a boot camp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, NotebookLM, and other generative AI platforms can support you in crafting learning objectives, writing exam questions, composing rubrics, and designing course content such as lesson plans, in-class activities, instructional videos, and more.

https://stonybrook.zoom.us/j/92511854285?pwd=QRTHfULqHMWxJYoVyt3piOhNxWLfvs.1

The International Neuroethics Society (INS) Speaker Series on AI & Consciousness

AI has existed as a tool for a long time, performing simple tasks such as sorting documents, suggesting music, and so on. But with the development of new generations of AI, the perception of its value to society has been increasing, as it can bring potential and promising benefits in many areas of human life. AI is known to have errors or biases that result in strange or even dangerous responses, but what happens when in AI-human interaction, the latter have errors or biases? cultural errors or biases? And what could be the implications for human relationships?

Speaker Bio

Dr. Karen Herrera-Ferrá is an independent and global consultant on ethical, medical, psychological, legal, social, cultural, policy-making, human rights and political issues and concerns on the development and use of neuroscience, neurotechnology and AI. She is a former member of the Board of Directors of the International Neuroethics Society.

Register here

https://umaryland.zoom.us/meeting/register/tJMvfuqsqDspG9BKMLfUU49UbuUyP_IEvXRh

Abstract: Modern technologies enable enhanced integrity and privacy guarantees not just for data, but also for computation. This is perhaps most emphatically demonstrated by the steady rise of zero-knowledge proofs, which are short certificates that attest to the correctness of computations (e.g., an age verification check) without revealing any secret inputs (e.g., the birth date on a digital ID). This subtly powerful technology enables anonymous credentials, privacy-preserving machine learning, anonymous blockchains, and much more--making the question of efficient zero-knowledge proofs fundamental to modern secure systems. Echoing Moore's law for computing, zero-knowledge proofs have improved on this front by ten orders of magnitude in the last two decades. In this talk, I will discuss our work on overcoming a key bottleneck that has emerged in this development: memory efficiency.

Speaker: Abhiram Kothapalli is a postdoctoral scholar at the University of California, Berkeley, hosted by Sanjam Garg. He is a recent graduate of Carnegie Mellon University, where he earned his Ph.D. in Computer Science, advised by Bryan Parno. Previously, he was at the University of Illinois at Urbana-Champaign, where he earned his B.S. in Computer Science and B.S. in Mathematics. Kothapalli's research develops cryptographic techniques aimed at scaling expressive privacy and integrity guarantees across the internet.

Location: NCS 120

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
The Center of Excellence in Wireless and Information Technology (CEWIT) will host the 16th International Conference on Emerging Technologies for a Smarter World (CEWIT2020) virtually on November 5, 2020. The conference will center on the four major fields which are penetrating our business and personal lives: Machine Learning, Artificial Intelligence, Blockchain and Computational Medicine. For more info visit: https://www.cewit.org/.  
Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a bootcamp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, and other generative AI platforms can support you in crafting learning objectives, writing exam questions, composing rubrics, and designing course content such as lesson plans, in-class activities, instructional videos, and more.

Register here.
How to Do Spectral Learning at Scale for Science and Engineering

Abstract: Spectral decompositions such as singular value decompositions (SVDs) and eigenvalue decompositions (EVDs) are central tools across a vast swath of scientific computing and machine learning, with abundant engineering applications. Yet many modern methods for learning such decompositions in high dimensions struggle with instability, bias, and poor scalability, even when approximation power is not the limiting factor. I argue that these difficulties are not intrinsic to spectral problems, but instead arise from a shared reliance on Rayleigh-quotient-based constrained optimization, which forces explicit orthogonality handling through penalties, normalization, or whitening.
To address these challenges, I present a reformulation based on unconstrained variational objectives that implicitly encode spectral structure, eliminating the need for orthogonalization and ad-hoc regularization. This perspective leads to a conceptually simpler and scalable parametric framework for learning ordered spectral representations via nested optimization. The resulting framework is well matched to diverse settings in science and engineering. As examples, I demonstrate its effectiveness on eigenvalue problems for linear PDEs such as the Schrödinger equation, spectral (Koopman) analysis of nonlinear dynamical systems such as molecular dynamics, and structured representation learning with deep neural nets. Collectively, these examples illustrate how abandoning Rayleigh-quotient-based formulations resolves long-standing optimization pathologies across domains.

Bio: Jongha (Jon) Ryu is a postdoctoral associate at MIT EECS. He received his Ph.D. in Electrical and Computer Engineering from UC San Diego. His research develops statistical and mathematical foundations for scientific machine learning, with a focus on scalable spectral methods, efficient generative modeling, and reliable uncertainty quantification for scientific and engineering systems.

Location: NCS 120
The Antonija Prelec Memorial Committee in collaboration with Stony Brook University Libraries are very excited to bring you the 2019 Prelec Memorial Lecture! This year, we are pleased to announce our speaker is Patricia Flatley Brennan, RN, PhD, Director of the National Library of Medicine.

No registration required. Find more information here.
As generative AI tools become increasingly prevalent in education, their impact on collegiate writing raises important questions about creativity, academic integrity, and effective teaching practices. This panel brings together faculty and students to share perspectives on the opportunities and challenges that AI presents in an academic setting. Through an open dialogue, participants will engage in meaningful conversations, allowing for a deeper understanding of each other's viewpoints and fostering collaboration. Students and faculty will explore diverse ways AI can be used in teaching and learning and seek solutions to utilize AI writing tools ethically. This exchange aims to build a community of trust and shared knowledge, ensuring that AI's role in education is both innovative and responsible.

Register here: https://stonybrook.zoom.us/meeting/register/tJAqdOitpjIpHtDGAsGBfEb3ah0YIzhIJolN