EMNLP 2020 - The 2020 Conference on Empirical Methods in Natural Language Processing
Online format - https://2020.emnlp.org/
The University at Albany will host a national gathering of professionals and academics that will focus on the transformative potential of AI while addressing the ethical, technical and institutional challenges posed by AI in education.
The Symposium will feature dynamic keynotes, hands-on workshops and engaging conversations with other participants and subject matter experts.
Topics
- Teaching, Learning and Workforce Development
- Research, Creative Arts, and Practice
- Ethics, Governance and Academic Administration
For event information and registration, visit Events@Albany.
ChatGPT for Developing Interactional Competence: Affordances and Constraints
MIC Distinguished Lecture Series
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 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: HUM 1006
You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks 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.
Speakers
Kriti Chopra, Computing & Data Sciences (CDS)
Thomas Flynn, Computing & Data Sciences (CDS)
Wenjie Liao, Chemistry Division
Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1
Meeting ID: 161 528 9117
Passcode: 991382
Join the Center of Excellence in Wireless and Information Technology (CEWIT) and their co-host IEEE-USA for a livestream panel discussion on Generative Artificial Intelligence (Gen AI). In this engaging livestream, we will dive into the technologies that continue to transform what is possible and explore the dynamic intersection of innovation, creativity, ethics, and Gen AI.
CEWIT is joined by Stony Brook University experts who will provide their insights and perspectives on this rapidly changing technology.
Meet the Panel
Laura Lindenfeld, PhD
Executive Director
Alan Alda Center for Communicating Science®
Dean
School of Communication & Journalism
BIO
Margaret Schedel, PhD
Associate Professor
Composition and Computer Music
Co-Founder
Lyrai
BIO
Steven Skiena, PhD
Interim Director
AI Innovation Institute
Distinguished Professor
Computer Science
BIO
Vivian Zhang
CTO/School Director
NYC Data Science Academy
Chief Data Officer
GoDental.ai
BIO
Register here.
CEWIT is joined by Stony Brook University experts who will provide their insights and perspectives on this rapidly changing technology.
Meet the Panel
Laura Lindenfeld, PhD
Executive Director
Alan Alda Center for Communicating Science®
Dean
School of Communication & Journalism
BIO
Margaret Schedel, PhD
Associate Professor
Composition and Computer Music
Co-Founder
Lyrai
BIO
Steven Skiena, PhD
Interim Director
AI Innovation Institute
Distinguished Professor
Computer Science
BIO
Vivian Zhang
CTO/School Director
NYC Data Science Academy
Chief Data Officer
GoDental.ai
BIO
Register here.
Ready to stop taking notes and make your virtual meetings work for you? Join our upcoming workshop to master Zoom's built-in AI tools and streamline your workday. You'll learn how to generate instant AI Meeting Summaries to capture key action items effortlessly, use live in-meeting Q&A to ask Zoom AI questions and catch up on missed details in real time, and prompt Zoom to build sleek, professional virtual backgrounds on the fly. Please click the link below to register. We hope to see you there!
https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_a9PVlBw0E1Bal1A?source=campus%20announcements&session=100910261600zoom
https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_a9PVlBw0E1Bal1A?source=campus%20announcements&session=100910261600zoom
Abstract: Generative visual models like Stable Diffusion and Sora generate photorealistic images and videos that are nearly indistinguishable from real ones to a naive observer. However, their grasp of the physical world remains an open question: Do they understand 3D geometry, light, and object interactions, or are they mere pixel parrots of their training data? Through systematic probing, I will demonstrate that these models surprisingly learn fundamental scene properties--intrinsic images such as surface normals, depth, albedo, and shading (à la Barrow & Tenenbaum, 1978)--without explicit supervision, which enables applications like image relighting. But I will also show that this knowledge is insufficient. Careful analysis reveals unexpected failures: inconsistent shadows, multiple vanishing points, and scenes that defy basic physics. All these findings suggest these models excel at local texture synthesis but struggle with global reasoning: a crucial gap between imitation and true understanding. I will then conclude by outlining a path toward generative world models that emulate global and counterfactual reasoning, causality, and physics.
Bio: Anand Bhattad is a Research Assistant Professor at the Toyota Technological Institute at Chicago. He earned his PhD from the University of Illinois Urbana-Champaign in 2024 under the mentorship of David Forsyth. His research interests lie at the intersection of computer vision and computer graphics, with a current focus on understanding the knowledge encoded in generative models. Anand has received Outstanding Reviewer honors at ICCV 2023 and CVPR 2021, and his CVPR 2022 paper was nominated for a Best Paper Award. He actively contributes to the research community by leading workshops at CVPR and ECCV, including Scholars and Big Models: How Can Academics Adapt? (CVPR 2023), CV 20/20: A Retrospective Vision (CVPR 2024), Knowledge in Generative Models (ECCV 2024), and How to Stand Out in the Crowd? (CVPR 2025). For more details, visit https://anandbhattad.github.
AI for Conservation: AI and Humans Combating Extinction Together by Daniel I. Rubenstein of Princeton University
ABSTRACT: The state of our planet is not good. We have lost more than 60% of the world's wildlife. Stopping the decline remains a challenge, especially since acquiring appropriate knowledge is expensive, time consuming and risky. Visual observations following the fates of a few individuals was the currency of the realm. But GPS technology and now machine learning provide a non-invasive scalable alternative. Photographs, taken by field scientists, tourists, automated cameras and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation coordinated by a non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high-resolution information databases, enabling scientific inquiry, conservation and citizen science.
BIO: Dan Rubenstein is the Class of 1877 Professor of Zoology. He is currently Director of Princeton's Environmental Studies Program and is former Chair of Princeton University's Department of Ecology and Evolutionary Biology and Director of Princeton's Program in African Studies. He is a behavioral ecologist who studies how environmental variation and individual differences shape social behavior, social structure, sex
roles and the dynamics of populations. He has special interests in all species of wild horses, zebras and asses, and has done field work on them throughout the world identifying rules governing decision-making, the emergence of complex behavioral patterns and how these understandings influence their management
and conservation. In Kenya he also works with pastoral communities to develop and assess impacts of various grazing strategies on rangeland quality, wildlife use and livelihoods. He has also developed a scout program for gathering data on Grevy's zebras and created curricular modules for local schools to raise awareness about the plight of this endangered species. He engages people as 'Citizen Scientists' and has recently extended his work to measuring the effects of environmental change, including issues pertaining to the global commons
and changes wrought by management and by global warming, on behavior.
ABSTRACT: The state of our planet is not good. We have lost more than 60% of the world's wildlife. Stopping the decline remains a challenge, especially since acquiring appropriate knowledge is expensive, time consuming and risky. Visual observations following the fates of a few individuals was the currency of the realm. But GPS technology and now machine learning provide a non-invasive scalable alternative. Photographs, taken by field scientists, tourists, automated cameras and incidental photographers, are the most abundant source of data on wildlife today. Wildbook, a project of tech for conservation coordinated by a non-profit Wild Me, is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high-resolution information databases, enabling scientific inquiry, conservation and citizen science.
BIO: Dan Rubenstein is the Class of 1877 Professor of Zoology. He is currently Director of Princeton's Environmental Studies Program and is former Chair of Princeton University's Department of Ecology and Evolutionary Biology and Director of Princeton's Program in African Studies. He is a behavioral ecologist who studies how environmental variation and individual differences shape social behavior, social structure, sex
roles and the dynamics of populations. He has special interests in all species of wild horses, zebras and asses, and has done field work on them throughout the world identifying rules governing decision-making, the emergence of complex behavioral patterns and how these understandings influence their management
and conservation. In Kenya he also works with pastoral communities to develop and assess impacts of various grazing strategies on rangeland quality, wildlife use and livelihoods. He has also developed a scout program for gathering data on Grevy's zebras and created curricular modules for local schools to raise awareness about the plight of this endangered species. He engages people as 'Citizen Scientists' and has recently extended his work to measuring the effects of environmental change, including issues pertaining to the global commons
and changes wrought by management and by global warming, on behavior.
Abstract: In recent years, we have been developing generative AI methods to design increasingly complex objects. Our goal is to improve performance while ensuring that these objects remain controllable. This requires addressing several challenging problems, including:
Speaker: Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996, where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist.
His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 400 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of the IEEE journal Transactions For Pattern Analysis and Machine Intelligence. He often serves as the program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies.
- Modeling composite objects in a way that preserves consistency under deformation.
- Estimating the uncertainty of the surrogate models used to predict performance during optimization.
- Co-designing objects for both high performance and ease of control.
Speaker: Pascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and a Ph.D. in Computer Science from the University of Orsay in 1989. He joined EPFL (Swiss Federal Institute of Technology) in 1996, where he is a Professor in the School of Computer and Communication Science and head of the Computer Vision Lab. Before that, he worked at SRI International and at INRIA Sophia-Antipolis as a Computer Scientist.
His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 400 publications in refereed journals and conferences. He has received several ERC grants. He is an IEEE Fellow and has been an Associate Editor of the IEEE journal Transactions For Pattern Analysis and Machine Intelligence. He often serves as the program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies.
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.