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.


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:Deep Contextual Modeling for Natural Language Understanding, Generation, and Grounding Zoom instructions: Join Zoom Meeting https://stonybrook.zoom.us/j/645050299?pwd=TVJVRkc3dlhxdDF5d00xWGlDQkovZz09 Meeting ID: 645 050 299 Password: 810247 One tap mobile +16468769923,,645050299#,,#,810247# US (New York) +13126266799,,645050299#,,#,810247# US (Chicago) Dial by your location +1 646 876 9923 US (New York) +1 312 626 6799 US (Chicago) +1 301 715 8592 US +1 346 248 7799 US (Houston) +1 408 638 0968 US (San Jose) +1 669 900 6833 US (San Jose) +1 253 215 8782 US Meeting ID: 645 050 299 Password: 810247 Find your local number: https://stonybrook.zoom.us/u/aemTiJMXu6 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 Ph.D. 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, CoNLL. During his Ph.D., 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.

Description:

As artificial intelligence and data science reshape the global information landscape, libraries are emerging as key players in both technological innovation and ethical stewardship. This international Zoom discussion brings together library professionals and educators from the U.S., Philippines, and Hong Kong to explore how institutions are integrating AI and data into their pedagogy and services.

Panelists will share concrete examples from their own libraries--ranging from data literacy initiatives to increasing discoverability. The conversation will also examine regional trends in librarianship, spotlighting how institutions in Asia are navigating the evolving role of data and AI.

Join us for a global conversation that highlights the transformative potential of libraries as hubs for innovation and critical inquiry in the age of AI.

Register for this free Zoom panel.

Panelists:

Ahmad Pratama is a Faculty Member and Associate Librarian at Stony Brook University Libraries, where he is working to build a comprehensive, campus-wide data literacy program within the Libraries. As the Data Literacies Lead, his work focuses on empowering students, faculty, and staff to critically and ethically engage with data and AI, including the development of a credit-bearing course in Critical Data & AI Literacies supported by an EDGE Fund Award from the Provost's Office. Previously, Dr. Pratama served as an Associate Professor of Information Technology, and his research and teaching explore the intersections of technology, policy, and society with a focus on data, AI, and innovation in higher education.

Dan Anthony Dorado is a full-time faculty member at the U.P. School of Library and Information Studies, where he teaches information technology, management and marketing, research methodology, and quantitative research. He was also the director of the Diliman Learning Resource Center under the Office of the Vice Chancellor for Student Affairs. Before that, he was an Information Specialist at the College of Engineering Library, in charge of the System and Network Administration and The Learning Commons. He completed his master's degree at the Technology Management Center in U.P. Diliman and is currently pursuing his PhD in Data Science. As a member of Sync.Bio.Optics laboratory and the Publics, Archives, and Data (PANDA) Lab, his research specialization covers Computational Methods, Open Education, Critical Data Studies, and Radical Statistics.

Ryun LEE is Associate University Librarian at The Chinese University of Hong Kong Library, leading Digital Initiatives and Library IT and Systems. He drives digital innovation through emerging technologies, particularly artificial intelligence to enhance services, streamline operations, and support CUHK's mission in research, education, and knowledge advancement. With a background in cataloging and digital repository development, Ryun leads projects in digitization, OCR, data visualization, text and network analysis, GIS, and digital scholarship. He actively promotes knowledge graph applications in Hong Kong studies and oversees efforts to digitize and preserve resources related to Hong Kong and Southern China. His recent work focuses on creating seamless digital experiences and developing data-driven infrastructure. He is currently exploring AI-driven approaches to digitization workflows and entity extraction, aiming to improve access, discovery, and long-term preservation of library materials.



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
Optimization and Machine Learning - presented by Yifan Sun

Abstract: Optimization is a growing topic of interest in the machine learning community. It starts out as an option to check in Tensorflow (SGD? Adam? Adagrad?), but as we get more into the how and why of these options, we uncover many fundamental principles relating to operations research, control theory, and dynamical systems, dating back as far as the Cold World era. 

In this talk I will give a broad overview of some of the important optimization themes in machine learning. I will try to give connections between tools we are used to seeing in popular packages 
and fundamental optimization concepts like duality, convexity, contractive operators, etc. While we cannot hope to completely cover this diverse research area, I hope to provide a glimpse of this exciting research area that is permeating more and more into the machine learning world. 

Bio: Yifan Sun received her PhD in Electrical Engineering from the University of California Los Angeles in 2015, with research focusing on convex optimization and semidefinite programming. She was then Technicolor Research and Innovation, focusing on machine learning and 
data science applications. More recently, she completed two postdocs focusing on optimization, at the University of British Columbia in Vancouver, Canada and INRIA, in Paris, France.

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.

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room

Speakers

Jianda Chen, EBNN - Improving the stability and accuracy of PDE-ML hybrid AGCMs

Boyang Li, CDS - Accelerating Materials Discovery using Machine Learning

Jaehye on Do, NPP Isotopes - Using LLMs for Isotopes Research and Production

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382