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

Learn how to summarize docs with AI, output a PowerPoint from AI, & Create professional visuals

Unlock greater efficiency and impact in your university role with AI productivity tools. This workshop is your introduction to a few ways that I have found to make our daily tasks more efficient. Discover how easily you can create presentations (that outputs to a PowerPoint format), summarize content using AI, and get information from images. These AI tool tips are invaluable resources designed to streamline your work processes. Start working smarter today!

In this session, you will

  1. Summarize docs with AI
  2. Output a PowerPoint from AI
  3. Gather information from visuals

Register here.

Abstract: My presentation will be focused on introducing the use of Screenomics, a passive sensing approach that directly collects time-intensive data from participants' smartphones, to observe and analyze adolescents' digital behaviors across multiple timescales. I will present our completed and ongoing efforts using Screenomics to (1) evaluate the biases of self-reports of screen time and app use, (2) describe how adolescents use their smartphones during school hours and overnight, (3) examine longitudinal associations between adolescents' social media use and mental health, and (4) capture adolescents' communication pattern with parents. I will also introduce the theoretical framework and study plan for a new NIH-funded project that aims to identify adolescents' social media management strategies (SMMS) and how SMMS are related to adolescents' actual social media use and mental health. I will conclude with a discussion of future directions for interventions to promote healthy digital practices among adolescents.

Bio: Xiaoran Sun, Ph.D., is an assistant professor in the Department of Family Social Science, College of Education and Human Development at University of Minnesota (UMN). She is the director of the UMN Technology, Teens, and Families Lab and a core faculty of the Learning Informatics Lab. She is also affiliated with the UMN Data Science Initiative and the Minnesota Population Center. Her research is mainly focused on using innovative approaches, such as passive sensing and machine learning, to examine children's and parents' use of technology and the implications for their wellbeing. Her work is being funded by the U.S. National Institute of Mental Health and the Spencer Foundation.

This workshop synthesizes the latest research on the impact of AI usage in education so that you could make informed decisions on whether and how to use AI to facilitate your learning. You might have seen conflicting reports on whether the use of AI is good for learning. In this workshop, we are going to tease out, drawing on the latest research, which types of AI usage are beneficial or harmful for different kinds of learning. At the end of the workshop, you should walk away with more clarity on when and how to use AI for your own learning. Join PRODIG+ fellow on critical AI, Zheng Fu, in this informative workshop.

Register for this Zoom workshop.

Understand Prompting the crucial part to interface with models

Discover how to prompt effectively by exploring the details behind your AI interactions. This isn't just about basic prompting; it's about understanding how to articulate your ideas clearly. We'll showcase a few prompts and how they work. Discover how giving AI the right details can truly boost your productivity and help you reclaim valuable time in your day.

In this session, you will

  1. Utilize AI models effectively
  2. Understanding different prompts
  3. Find out tips that we use with AI

Register: https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_dht1o3rNzlZhHka?source=event+manager&session=0805251000ai

Join us to share your thoughts about teaching, learning, and AI!

The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.

This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.

We'll be exploring critical questions like:

  • In the age of AI, what are the opportunities you see for enriching the classroom and curriculum? How can it enhance student learning or your professional practice?

  • What are the most significant challenges and concerns that AI raises for you regarding academics, student integrity, or your workload?

  • What resources (tools, training, technical support, policy guidance, etc.) do you need to feel confident and successful in the age of AI?

Dates/Times:

  • Tuesday, 2/3 at 2pm

  • Friday, 2/6 at 9:30am

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

We understand schedules are tight. If you cannot attend the live discussion, you can still share your thoughts! Join our AI Zoom Room to share your thoughts via video recording or email rose.tirotta-esposito@stonybrook.edu with your comments and ideas.

Videos will not be shared publicly and comments will only be shared in aggregate.

Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!

  • 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)

Abstract: Many scientific and engineering challenges, such as the design of materials or molecules or the control of experimental systems, rely on the existence of fast predictive models that can evaluate potential designs or control policies. Traditionally this has been accomplished through numerical simulation; more recently data-driven machine learning methods have been applied. However, both approaches leave gaps: physical modeling can be accurate and extrapolates well to previously-unstudied conditions, but it is often computationally expensive and relies on physics approximations that may not be valid. Machine learning can generalize from massive amounts of real-world or simulation data, but suffers from physical grounding and extrapolation into new regimes, as well as in settings where large data sets do not exist.
In this talk I explore an intermediate regime, which is hybrid reduced order models: fast simplified physics approximations where some of the unknown or approximated equations are replaced with data-driven machine learning components. Examples include coarse-grained models where the full macroscopic equations cannot be derived from first-principles microscopic equations, multiscale models with unknown closure terms or sub-grid parameterization schemes, and low-order or latent dynamical systems that learn governing equations on a low-dimensional reduced state space. I discuss how such reduced systems can be identified from very limited data, much less than is often needed in traditional machine learning but at much lower time-to-solution than traditional numerical modeling. This facilitates not only system design and control but also uncertainty quantification approaches that search the space of possible equations for predictive models that can explain the data. I will focus on an example from materials science concerning the design of self-assembling block copolymer nanomaterials.

Speaker: Dr. Nathan Urban, Applied Mathematics Department, Brookhaven National Laboratory

Location: Laufer 101

Zoom: https://stonybrook.zoom.us/j/96090260834?pwd=mw8QTHbMOw9oeU9hazZeoq8bN4VIfH.1
Meeting ID: 960 9026 0834 Passcode: 374969
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.