Join the Office of Educational Effectiveness' upcoming workshop on the transformative potential of AI tools to enhance program assessment. Learn how to leverage AI to create targeted learning objectives, detailed rubrics, and precise benchmarks that will elevate the quality and effectiveness of your program assessment process. Join in-person on Oct. 17 at 10:30 am or virtually on Oct. 21 at 12 pm.

Register in advance: https://calendar.stonybrook.edu/site/office-educational-effectiveness/event/leveraging-ai-in-assessment-zoom/
Abstract: As computing and society become increasingly inseparable, we confront a fundamental design challenge: creating AI systems where human-machine interactions authentically embody our diverse values while thoughtfully evolving our social relationships. The recursive nature of these interactions--where human behavior shapes technology design and technological affordances influence human behavior--presents both profound risks and transformative opportunities as we reimagine our collective digital future. What interaction patterns emerge when algorithmic systems become active participants in societal decision-making? How can we design human-AI collaboration that ensures algorithmic systems align with diverse community values while serving the public interest? Through Public Interest AI, we explore a Pluralistic Design Language that creates interaction models for value-sensitive algorithmic ecosystems, strengthening AI-society alignment in both technology design and policy development. Through collaborative interaction with communities, we create systems that augment human capabilities while embedding ethical principles into the sociotechnical design of AI itself--ultimately redefining possibilities at the intersection of technology, policy, and society. This talk will examine the challenges of designing meaningful human-AI systems within social contexts through real-world applications that combine value-sensitive interaction design, human-inspired computing, and societal development to create technologies that advance our shared commitment to the public good.

Bio: Neil Gaikwad is an Assistant Professor of Data Science and Computer Science at UNC Chapel Hill. Additionally, he serves on the Faculty Advisory Council of the UNC Parr Center for Ethics and is a Fellow at the MIT Dalai Lama Center for Ethics and Transformative Values. Neil holds a Ph.D. in Society-Centered AI from MIT and is an alumnus of Carnegie Mellon University's School of Computer Science. Neil's scholarship, published in prominent AI and HCI conferences, has been recognized with several prestigious honors, including the Facebook Research Fellowship, UIST Best Paper Honorable Mention, MIT Engineering Fellowship, Human Rights & Technology Fellowship, Graduate Teaching Award, and the Karl Taylor Compton Prize, MIT's highest student honor. He has been recognized as a Rising Star by both Stanford University and the University of Chicago. Translating research into real-world impact, Neil is a dedicated educator and mentor who has taught over 500 students throughout his career. He has guided more than 30 students to publish influential papers on AI fairness, secure prestigious fellowships, and contribute to shaping AI policy through public interest research. Neil is also the founder of the AI Policy Global Initiative, which has successfully brought together academia, industry, government, and communities to address critical challenges in AI governance and develop collaborative approaches to responsible AI.

Location: Old Computer Science, room 1310
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:
Large language models (LLMs) have transformed the way humans write code, bringing unprecedented automation to software development. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with a deeper, more comprehensive understanding of software programs. Finally, I will discuss the future of AI-driven software engineering, envisioning a new era of automation that is more reliable, intelligent, and cost-efficient.

Bio:
Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
Location:
NCS 120
Face Editing with Machine Learning presented by Zhixin Shu

ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.
Abstract: Facial emotion understanding aims to recognize, represent, and interpret human affect from facial behavior, and it is important for affective computing, human-computer interaction, digital humans, and mental health assessment. Existing work has represented facial emotion through discrete emotion categories, continuous affective dimensions such as valence and arousal, facial landmarks or geometry, and more recently semantic or language-based emotion descriptors. With the development of deep learning, supervised facial expression recognition has achieved strong performance on benchmark datasets, while self-supervised learning, multimodal large language models, and controllable facial generation have introduced new ways to learn emotion-related facial representations from images, videos, text, audio, and speech. However, many current models still rely heavily on manually annotated emotion labels, third-party perception judgments, or multimodal contextual cues, making it difficult to determine how much emotional information is captured directly from facial behavior itself, especially in naturalistic and clinically meaningful settings. Because these limitations make it challenging to evaluate whether facial representations capture emotionally meaningful behavior in real-world interactions, we propose to study facial emotion understanding through the relationship between facial behavior and language-derived emotional expression in psychiatric interview videos. Using a large dataset of mental health interviews, we extract multiple types of facial representations, including Action Unit features from FaceReader and OpenFace, non-AU facial behavior features from OpenFace, and 3D facial representations from EMOCA and SMIRK. We train segment-aligned transformer regressors to predict language-derived emotional targets, including valence, arousal, and RoBERTa-derived semantic-affective features from transcript segments. The results show that all facial representations achieve meaningful predictive performance across MSE and Pearson correlation metrics in both within-participant and between-participant evaluation settings. This indicates that facial behavior encodes information related to linguistic emotion at multiple levels: moment-to-moment emotional variation within individuals and broader affective differences across individuals. These findings suggest that structured facial representations can support vision-based emotion understanding in naturalistic mental health interviews and motivate future work on self-supervised, personalized, and controllable facial emotion models.

Speaker: Shao-Yu Chang

Zoom: https://stonybrook.zoom.us/j/3679036240?omn=98419305450
Abstract: Formalization of mathematics is the process by which pen-and-paper mathematics is translated into a strict chain of logical deductions down to the axioms of mathematics. The subject has seen renewed interest in the last decades thanks to the development of computer systems called proof assistants, which make this feasible in practice.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.

Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.

The deadline to apply for this workshop is January 24, 2026.

https://icerm.brown.edu/program/topical_workshop/tw-26-ttfa


Abstract: Generating high-fidelity EEG data at scale is essential for overcoming dataset scale limitation and satisfying privacy requirements in computational neuroscience. However, the predominant reliance on discrete denoising formulations fails to adequately capture the continuous temporal evolution and frequency-domain characteristics intrinsic to EEG recordings. Consequently, such approaches often compromise long-range temporal coherence and introduce structural discrepancies in both spectral and temporal domains.
In this work, We argue that faithful EEG synthesis demands generative models that directly characterize the continuous dynamics underlying neural signals. To this end, we propose a conditional flow matching framework that treats EEG as raw waveform sequences traversing continuous-time trajectories. Rather than relying on discretized denoising steps or handcrafted signal representations, our method learns a smooth velocity field mapping noise distributions to the target EEG manifold, thereby naturally preserving temporal continuity and transient neural phenomena. To enforce fidelity to fundamental EEG characteristics, we incorporate principled constraint terms that maintain spectral consistency, temporal stationarity, and signal-level statistical properties. Evaluated on large-scale benchmarks, our approach establishes new state-of-the-art results compared to competitive baselines. Comprehensive analyses further confirm that the proposed framework faithfully recovers core structural attributes of neural dynamics, offering a scalable and theoretically grounded solution for high-fidelity EEG generation.

Speaker: Yifan Wang

Location: https://stonybrook.zoom.us/j/95300279172?pwd=maxkSIma0Go2O8Wzre3CLYWHflnUi3.1&jst=2
Meeting ID: 953 0027 9172
Passcode: 418725
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.