Virtual Talk: Contextual Modeling for Natural Language Understanding, Generation and Grounding by Rui Zhang

Zoom link to come.

Abstract: Natural language is a fundamental form of information and communication. In both human-human and human-computer communication, people reason about the context of text and world state to understand language and produce language response. In this talk, I present 
several deep-neural-network-based systems that first understand the meaning of language grounded in various contexts where the language is used, and then generate effective language responses in different forms for information access and human-computer communication. First, 
I will introduce Speaker Interaction RNNs for addressee and response selection in multi-party conversations based on explicit representations for different discourse participants. Then, I will 
present a text summarization approach for generating email subject lines by optimizing quality scores in a reinforcement learning framework. Finally, I will show an editing-based multi-turn SQL query generation system towards intelligent natural language interfaces to databases. 

Bio: Rui Zhang is a final-year PhD student at Yale University advised by Professor Dragomir Radev. His research interest lies in various natural language processing problems in understanding, generation, and grounding. He has been working on (1) End-to-End Neural Modeling for Entities, Sentences, Documents and Multi-party Multi-turn Dialogues, (2) Text Summarization for Emails, News and Scientific Articles, (3) Cross-lingual Information Retrieval for Low-Resource Languages, (4) Context-Dependent Text-to-SQL Semantic Parsing in Human-Computer Interaction. Rui Zhang has published papers and served as Program Committee members at top-tier NLP and AI conferences including ACL, NAACL, EMNLP, AAAI and CoNLL. During his PhD, he has done research internships at IBM Thomas J. Watson Research Center, Grammarly Research and Google AI. He was a graduate student at the University of Michigan and got his Bachelor's degrees at both the University of Michigan and Shanghai Jiao Tong University from the UM-SJTU Joint Institute.

Designed for faculty, staff, presidents, provosts, academic leaders, student affairs professionals, IT specialists, librarians, researchers, administrators, institutional decision-makers, and other higher education stakeholders, the conference highlights practical strategies institutions can implement now while exploring longer-term governance, policy, and ethical considerations. Participants will leave with concrete tools, cross-institutional insights, and collaborative connections that support mission-aligned AI innovation.

Hosted by: AAC&U

Location: Atlanta, GA and Virtual

Register here.

University Libraries Presents: The Library AI Club is a welcoming space for students, faculty, and staff to explore AI in a supportive, low-pressure environment. Meeting every two weeks, the club features discussions, collaborative projects, guest speakers, and hands-on experiments. Join us to learn, share ideas, and engage with AI responsibly and creatively. We'd love to see you at an upcoming meeting! Location: Melville Library, Scholarly Communication Seminar Room

Join us for an engaging panel discussion featuring researchers who participated in our inaugural AI JAM session on February 26th. Our panelists will share their firsthand experiences using large language models to tackle complex scientific problems, with a special focus on prompt engineering strategies, discussing both breakthroughs and challenges encountered during this collaborative initiative. Learn how these cutting-edge AI tools are being applied to real-world research questions and discover insights that could inform your own scientific endeavors. Attendees are encouraged to come prepared with questions about prompt engineering for the panel discussion.

Moderator: Adolfy Hoisie, Deputy Director, Computing and Data Sciences

Kevin Yager, Group Leader, AI-Accelerated Nanoscience, Center for Functional Nanomaterials
Lingda Li, Associate Computational Scientist, Systems, Architecture and Computing Technologies, Computing and Data Sciences
Liguo Wang, Director of Scientific Operations, Laboratory for BioMolecular Structure (LBMS), National Synchrotron Light Source II
Weiguo Yin, Physicist, Condensed Matter Theory, Condensed Matter Physics and Materials Science Department

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1606837837?pwd=Tc0mwQqLXpDfYOIaoaurmpLD2mMlzS.1 (Meeting ID)

Passcode: 822553

Abstract: The advent of ChatGPT has redrawn the boundary of pedagogical discourse, where the dyadic configuration of teacher-student has, for many, become triadic -- one that includes AI as an relevant third party, not to be missed or dismissed. Within applied linguistics, AI-focused research has predominantly targeted the teaching and learning of writing (Fang & Han, 2025). The work on AI and speaking, on the other hand, has largely involved perception studies documenting its positive impact on learners' willingness to communicate (Goh & Aryadoust, 2025). In this talk, I explore the role of AI in the teaching and learning of speaking, and in particular, the development of interactional competence. Based on a corpus of learner-AI interactions, I demonstrate the ways in which ChatGPT excels and fails at acting as a useful conversation partner, with a view towards furthering our ongoing deliberation on the affordances and constraints of AI in language education.

Speaker: Hansun Zhang Waring (Teachers College, Columbia University)

Hansun Zhang Waring is Professor of Linguistics and Education at Columbia University and founder The Language and Social Interaction Working Group (LANSI). As an applied linguist and a conversation analyst, Hansun is interested in all things interaction -- (second language) pedagogical interaction, communication with the public, parent-child interaction, and human-AI interaction (HAI). Her work has appeared in leading journals in applied linguistics and discourse analysis as well as numerous book volumes, some of which she (co-)authored or co-edited. She is on the editorial boards of Chinese Language and Discourse (CLD), Classroom Discourse (CD), and International Review of Applied Linguistics (IRAL).

Location: Wang Center, Lecture Hall #1

If you need special accommodation, please contact chikako.nakamura@stonybrook.edu.


Get hands-on with data cleaning techniques using Python and AI tools. Join SBU Libraries' Data Literacies Lead, Ahmad Pratama, to learn how to identify and rectify errors, handle missing data, and prepare your dataset for analysis. This workshop introduces you to powerful yet easy-to-use tools and techniques that make data cleaning efficient and effective, turning chaotic data into valuable insights.

Please register for the Data Cleaning with Python and AI here.
Title: AI-Driven Target Selection Methods for Touch and Gaze Input

Abstract: Accurately selecting targets is an essential aspect of  Human-Computer Interaction. Erroneous selections can cause tedious undo and redo actions. Additionally, some selection errors are non-reversible and can lead to undesirable consequences. However, high-accuracy target selection remains a challenge on touchscreen devices due to the small target size and imprecise touch inputs, and in gaze interaction because of the gaze tracking noise and no easy-to-use selection action. We first propose ReLM, a Reinforcement Learning-based Method for touchscreen target selection. ReLM can automatically show suggestions and require a second touch if the input is ambiguous, and can directly select a target candidate when the input is certain. Our empirical evaluation shows that ReLM reduces the error rate from 6.92% to 1.63%, and the selection time from 2.23s to 1.59s over Shift, an existing suggestion-based method. Compared to BayesianCommand, a direct selection-based method, our ReLM reduces the error rate from 3.64% to 0.89%, while increasing the selection time by only 200 ms. Secondly, we investigate how to improve target selection performance for gaze interaction. We propose BayesGaze, an eye-gaze based target selection method. It accumulates the signal of each gaze point for selecting a target calculated by Bayes Theorem, and uses a threshold mechanism to determine the target selection. Our investigation shows that BayesGaze improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping method.

All are welcome. Here  is the zoom meeting link:
https://stonybrook.zoom.us/j/93130953411?pwd=Rm5IRlVPQ3M0cHJsTXpCVFljUlFGUT09Meeting ID: 931 3095 3411Passcode: 999413
The Future Histories Studio will host Young Maeng, an artist and professor at California State University, Fresno, for a talk exploring the intersection of artificial intelligence (AI) and traditional painting, examining how two seemingly disparate fields can converge to create new artistic expressions.

The lecture is part of the Future History Studio series at Stony Brook University, a platform dedicated to examining the evolving relationship between technology, art, and society.

Young will discuss her innovative approach to expanded painting, an integration of AI-generated images and traditional techniques such as Korean ink and acrylic painting. Through this fusion, she visualizes complex philosophical and ethical questions about the coexistence of humans, nature, and AI companion robots. The lecture will highlight the broader implications of AI in the art world, touching on how AI technologies challenge conventional notions of creativity and human-centric perspectives in art.

Speaker Bio:

Young Maeng is an artist and professor at California State University, Fresno, whose work explores the intersection of artificial intelligence (AI) and traditional painting techniques such as Korean ink and acrylic.

Maeng's innovative approach to expanded painting blends AI technology with traditional methods to visualize complex philosophical and ethical questions surrounding the coexistence of humans, nature, and AI companion robots.

Location: Future Histories Studio
Register here: https://www.eventbrite.ca/e/ai-and-painting-tickets-1021050809457?aff=oddtdtcreator
University Libraries Present: Analyzing quantitative data can feel overwhelming without the right tools. In this workshop, SBU Libraries' Data Literacies Lead, Ahmad Pratama will show you how to master the basics of exploratory data analysis for quantitative data using Python. This workshop covers several techniques to help you uncover patterns and insights in your datasets.

Online RSVP via link: https://stonybrook.zoom.us/meeting/register/vEPycmDrQoGjFqkmsYHgxw