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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250321T100000
DTEND;TZID=America/New_York:20250321T110000
DTSTAMP:20260511T183729
CREATED:20250305T192043Z
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SUMMARY:Rethinking Digital Marketing: How Gen AI is Redefining the Future of Search
DESCRIPTION:The rapid evolution of Generative AI is transforming the digital marketing landscape\, with search at the forefront of this revolution. Join us to explore how Gen AI is reshaping search advertising\, enabling unparalleled personalization\, and unlocking new opportunities for marketers to connect with audiences. Register now to secure your spot! \n\n\n\nSpeaker: Udayan Bose\, Founder and CEO\, NetElixir
URL:https://njbda.org/event/rethinking-digital-marketing-how-gen-ai-is-redefining-the-future-of-search/
LOCATION:Virtual
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/webp:https://njbda.org/wp-content/uploads/2025/03/Udayan-Bose.webp
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241206T120000
DTEND;TZID=America/New_York:20241206T130000
DTSTAMP:20260511T183729
CREATED:20241119T212039Z
LAST-MODIFIED:20241126T185750Z
UID:2885-1733486400-1733490000@njbda.org
SUMMARY:The Impact of AI on Education
DESCRIPTION:Presented by Lukman Ramsey\, Ph.D.Head of AI Solutions\, Public Sector and EducationGoogle \n\n\n\nThis presentation will explore the transformative role of artificial intelligence in the educational sector. Drawing on Dr. Ramsey’s extensive experience\, he will outline the key challenges currently being addressed by AI technologies and will delve into specific applications\, particularly in AI-driven tutoring and personalized learning experiences. The discussion will highlight innovative solutions developed by Google\, demonstrating recent advancements and envisioning the future trajectory of AI in education. \n\n\n\nPlease join us for this informative session and feel free to share the event information with your networks.  \n\n\n\nPlease contact Forough Ghahramani for additional information.
URL:https://njbda.org/event/the-impact-of-ai-on-education/
LOCATION:Virtual
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/11/Impact-of-AI-on-Education.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241112T120000
DTEND;TZID=America/New_York:20241112T133000
DTSTAMP:20260511T183729
CREATED:20241031T151835Z
LAST-MODIFIED:20241031T151839Z
UID:2845-1731412800-1731418200@njbda.org
SUMMARY:Language model-guided anticipation and discovery of unknown metabolites
DESCRIPTION:Speaker: Michael Skinnider\, Princeton University \n\n\n\nLunch is available beginning at 12 PM \n\n\n\nSpeaker to begin promptly at 12:30 PM \n\n\n\nAbstract: Despite decades of study\, large parts of the human metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of unidentified small molecules within human tissues and biofluids\, but structure elucidation of novel metabolites remains a low-throughput endeavour. Here\, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet\, a language model that learns the latent biosynthetic logic embedded within the chemical structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We demonstrate the potential for language models to accelerate the mapping of the metabolome by harnessinging DeepMet to discover several dozen mammalian metabolites. 
URL:https://njbda.org/event/language-model-guided-anticipation-and-discovery-of-unknown-metabolites/
LOCATION:Bendheim House\, Princeton University\, 26 Prospect Avenue\, Princeton\, New Jersey\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/10/mike_skinnider.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241108T103000
DTEND;TZID=America/New_York:20241108T120000
DTSTAMP:20260511T183729
CREATED:20241031T150426Z
LAST-MODIFIED:20241031T150430Z
UID:2833-1731061800-1731067200@njbda.org
SUMMARY:Belief transport: The mathematical theory of learning agents
DESCRIPTION:Abstract:  Learning agents\, which include humans and (ideally) AI agents\, take action in the world and learn from the outcomes. I will present our recent efforts toward an integrated theory of learning agents that span learning\, planning\, and social reasoning. The talk will focus on cooperative communication as an extended case study\, and suggest directions\, implications\, and limitations of the proposed approach.  \n\n\n\nBio: Dr. Patrick Shafto is Professor of Mathematics and Computer Science at Rutgers University – Newark and Program Manager at DARPA’s I2O office. He spent the previous two years as Member of the School of Mathematics at the Institute for Advanced Study. Research in his lab focuses on mathematical foundations of learning in humans and machines. He has received honors and awards including an NSF CAREER award\, chair in Data Science\, and outstanding reviewer awards at NeurIPS and ICML. His research has been supported by NSF (EHR\, CISE\, SBE)\, DARPA\, DoD\, NIH\, and the intelligence community and is a fellow of the Cognitive Science Society.  \n\n\n\nLight breakfast will be served.
URL:https://njbda.org/event/belief-transport-the-mathematical-theory-of-learning-agents/
LOCATION:Princeton University Press\, 41 William St\, Princeton\, New Jersey\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/10/PatrickShafto.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241106T160000
DTEND;TZID=America/New_York:20241106T180000
DTSTAMP:20260511T183729
CREATED:20241031T153014Z
LAST-MODIFIED:20241031T153017Z
UID:2854-1730908800-1730916000@njbda.org
SUMMARY:Art by Humans and Machines
DESCRIPTION:Join us for this Campus Point Connection\, where Larry O’Gorman of Nokia Bell Labs will consider how art and technology fuse to create interactive experiences. \n\n\n\nO’Gorman will trace the evolution of tech-enhanced artworks by examining the histories of video\, audio\, biometrics and machine learning. Expect to hear about Bell Labs’ earliest collaborations with visionaries like Robert Rauschenberg and Merce Cunningham to today’s cutting-edge creations. \n\n\n\nGateway South: Room 216 \n\n\n\nABSTRACT \n\n\n\nIn the last decade\, the art and theater worlds have increasingly endeavored to create immersive experiences for their audiences. In many cases\, this entails the use of cameras and other sensors\, combined with recognition techniques so audience input can modify the artwork. Recent machine learning advancements have also increased artists’ enthusiasm for creating technology-enhanced artworks. \n\n\n\nIn this talk\, Larry O’Gorman of Nokia Bell Labs will discuss interactive methodologies using patterns from video\, audio\, biometrics\, and machine learning. O’Gorman will also show a sampling of the interactive artworks starting with the Experiments in Art and Technology that involved New York artists such as Robert Rauschenberg and Merce Cunningham working with Bell Labs engineers in the 1960s\, and up to present day.
URL:https://njbda.org/event/art-by-humans-and-machines/
LOCATION:Gateway South\, Stevens Institute of Technology\, 607 River Terrace\, Hoboken\, New Jersey\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/10/Stevens-Arts-.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240305T123000
DTEND;TZID=America/New_York:20240305T133000
DTSTAMP:20260511T183729
CREATED:20240209T141857Z
LAST-MODIFIED:20240209T142021Z
UID:2339-1709641800-1709645400@njbda.org
SUMMARY:Amy Zhang – Sociotechnical Designs for Democratic and Pluralistic Governance of Social Media and AI
DESCRIPTION:In-person attendance is open to Princeton University faculty\, staff\, students and alumni. This talk is open to the public via Zoom.  \n\n\n\nDecisions about policies when using widely-deployed technologies\, including social media and more recently\, generative AI\, are often made in a centralized and top-down fashion. Yet these systems are used by millions of people\, with a diverse set of preferences and norms. Who gets to decide what are the rules\, and what should the procedures be for deciding them—and must we all abide by the same ones? This talk draws on theories and lessons from offline governance to reimagine how sociotechnical systems could be designed to provide greater agency and voice to everyday users and communities. This includes the design and development of: 1) personal moderation and curation controls that are usable and understandable to laypeople\, 2) tools for authoring and carrying out governance to suit a community’s needs and values\, and 3) decision-making workflows for large-scale democratic alignment that are legitimate and consistent. \n\n\n\nBio:\n\n\n\nAmy X. Zhang is an assistant professor at University of Washington’s Allen School of Computer Science and Engineering\, where she leads the Social Futures Lab\, dedicated to reimagining social and collaborative systems to empower people and improve society. Her work has received awards at ACM CHI and ACM CSCW\, and she has been a Google Research Scholar\, a Belfer Fellow at the ADL\, a Berkman Klein Fellow\, a Google PhD Fellow\, and an NSF CAREER recipient and Graduate Research Fellow. \n\n\n\nHer work has been profiled in BBC’s Click television program\, CBC radio\, and featured in articles by ABC News\, The Verge\, New Scientist\, and Poynter. Besides her work at UW\, she is also a research consultant at AI2 on the Semantic Scholar team\, and prior to UW\, she was a Stanford postdoctoral researcher after completing a Ph.D. at MIT CSAIL\, where she received the George Sprowls Best Thesis Award at MIT in computer science. She received an MPhil in Computer Science at the University of Cambridge on a Gates Fellowship and a BS in Computer Science at Rutgers University\, where she was captain of the Division I Women’s tennis team. \n\n\n\nThis talk will be recorded and posted to the CITP website\, YouTube channel and to Media Central. \n\n\n\nIf you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.
URL:https://njbda.org/event/amy-zhang-sociotechnical-designs-for-democratic-and-pluralistic-governance-of-social-media-and-ai/
LOCATION:Zoom
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/png:https://njbda.org/wp-content/uploads/2024/02/Zhang-Amy-e1701108329409.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240212T110000
DTEND;TZID=America/New_York:20240212T115000
DTSTAMP:20260511T183729
CREATED:20240209T141031Z
LAST-MODIFIED:20240209T142439Z
UID:2332-1707735600-1707738600@njbda.org
SUMMARY:Performance Engineering for Scalable AI
DESCRIPTION:Speaker: Xuhao Chen\, Research Scientist\, MIT CSAIL\n\n\n\nHosted by Stevens Institute of Technology \n\n\n\nLocation: Gateway North 303 (with virtual option)\n\n\n\nABSTRACT\n\n\n\nDue to massive size\, complex algorithms and irregular data structures\, AI applications are expensive and hard to scale\, which poses great challenges in computing system design. \n\n\n\nIn this talk\, I will discuss my approach called cross-stack performance engineering\, to address this challenge. I will describe software and hardware system design principles\, optimization techniques and automation methodologies\, across different layers of the system stack. I will show promising results to demonstrate that this cross-stack approach is effective to make AI scalable. \n\n\n\nBIOGRAPHY\n\n\n\nXuhao Chen is a Research Scientist at MIT CSAIL. Dr. Chen is broadly interested in parallel systems and architectures\, with a focus on AI and big-data applications. His recent work aims to make AI scalable by designing efficient algorithms\, software systems and hardware accelerators. His work has been published in OSDI\, ISCA\, MICRO\, VLDB\, ICS\, etc. \n\n\n\nZoom link: https://stevens.zoom.us/j/91365277622
URL:https://njbda.org/event/performance-engineering-for-scalable-ai/
LOCATION:Gateway Academic Center\, 6th Street Lot\, 601 Hudson St\, Hoboken\, New Jersey\, 07030\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/02/microchips.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240206T143000
DTEND;TZID=America/New_York:20240206T153000
DTSTAMP:20260511T183729
CREATED:20240202T165810Z
LAST-MODIFIED:20240202T170109Z
UID:2317-1707229800-1707233400@njbda.org
SUMMARY:NJIT Data Science Seminar Series
DESCRIPTION:Data Science Seminar Series in collaboration with the Department of Data Science \n\n\n\n“Structure-Enhanced Text Mining for Understanding and Augmenting Scientific Discovery” \n\n\n\nYu Zhang University of Illinois Urbana-Champaign \n\n\n\nLocation: Guttenberg Information Technologies Center (GITC) Building\, Room 4402 (4th floor lecture hall) (Coffee served at 2:15 PM)  \n\n\n\nZoom Meeting Link \n\n\n\nHosted by Shuai Zhang  \n\n\n\nLanguage models pre-trained on large-scale text corpora have achieved remarkable success in building text mining systems. Meanwhile\, text is usually accompanied by various types of structural signals\, such as document metadata\, concept ontologies\, and citation networks\, that can potentially benefit the understanding of text. To enhance the effectiveness of text mining methods\, my research focuses on teaching language models to exploit structural information for both fundamental tasks and advanced domain-specific applications\, with an emphasis on understanding and augmenting scientific discovery. In the first part of the talk\, I will present structure-aware classification algorithms that can predict relevant categories of a scientific paper from hundreds of thousands of candidate classes. These methods have been adapted into the Microsoft Academic Graph production pipeline. The second part of the talk will introduce seed-guided topic mining approaches that find category-indicative entities and structural signals. In the third part\, I will discuss how to leverage multi-task language model pre-training techniques to facilitate advanced applications in the scientific domain\, such as patient-to-article retrieval and paper-reviewer matching. Finally\, I will outline future research directions\, including structure-aware usage of large language models\, flexible translation between different types of scientific data\, and data mining for accelerating science and innovation.  \n\n\n\nYu Zhang is a Ph.D. candidate in the Department of Computer Science at the University of Illinois Urbana-Champaign\, advised by Prof. Jiawei Han. Prior to UIUC\, he received his B.Sc. degree in Computer Science from Peking University. Yu’s research focuses on structure-enhanced text mining and its applications in scientific literature understanding. His first-authored papers have been published in top-tier venues in the fields of data mining\, natural language processing\, and information retrieval. Yu has been awarded the UIUC Dissertation Completion Fellowship and the Yunni & Maxine Pao Memorial Fellowship.
URL:https://njbda.org/event/njit-data-science-seminar-series/
LOCATION:Guttenberg Information Technologies Center (GITC)\, 218 Central Ave\, Newark\, New Jersey\, 07102\, United States
CATEGORIES:lectures/talks
ATTACH;FMTTYPE=image/jpeg:https://njbda.org/wp-content/uploads/2024/02/GITC-update-2-Large.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T183000
DTEND;TZID=America/New_York:20211209T193000
DTSTAMP:20260511T183729
CREATED:20210720T012050Z
LAST-MODIFIED:20211203T145923Z
UID:941-1639074600-1639078200@njbda.org
SUMMARY:Distinguished Lecture Series in Business Analytics\, Seton Hall University
DESCRIPTION:Jeffrey S. Noto\, M.S.\, SVP and CFO\, Global Network and Technology\, Verizon  \n\n\n\nAnalytics from the CFO’s PerspectiveThe presentation will discuss analytical applications focused on improving the financial outcomes in a corporate environment\, including how improved operational data leads to advancements in financial outcomes – business volume insights\, investment analytics\, profitability insights\, financial forecasting and overall improved financial results. \n\n\n\nREGISTER NOW
URL:https://njbda.org/event/distinguished-lecture-series-in-business-analytics-seton-hall-university-4/
LOCATION:Virtual
CATEGORIES:lectures/talks
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211209T130000
DTEND;TZID=America/New_York:20211209T170000
DTSTAMP:20260511T183729
CREATED:20211124T170618Z
LAST-MODIFIED:20220202T194709Z
UID:1002-1639054800-1639069200@njbda.org
SUMMARY:Enabling Protein Structure Prediction with AI
DESCRIPTION:Artificial Intelligence/Machine Learning (AI/ML) methods are being used for de novo protein structure prediction. In addition to expert lectures\, this crash course will provide hands-on training on AlphaFold2 and RosettaFold. These methods made a big impact in the field of protein prediction by solving a 50-year-old grant challenge. Please check the registration page (https://iqb.rutgers.edu/node/245)  for more details.
URL:https://njbda.org/event/enabling-protein-structure-prediction-with-ai/
CATEGORIES:lectures/talks,workshops
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