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X-WR-CALNAME:Halıcıoğlu Data Science Institute - UC San Diego
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X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
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SUMMARY:AI in the Enterprise
DESCRIPTION:Are you interested in the exciting world of AI startups and industry? Are you interested in helping strengthen bridges between industry and academia?\n\n\n\n\n\n\n\n\nUCSD HDSI and RapidFire AI are delighted to announce a virtual panel discussion event titled “AI in the Enterprise.” The goal is to bridge industry and academia at the cutting edge of practical AI by discussing the recent rapid evolution of AI\, its implications for enterprises\, how startups and tech companies can empower enterprises to succeed with AI\, and how AI-related academic curricula should evolve in this new era.\n\n\n\n\n\n\n\nThe panel will be moderated by professor Arun Kumar of CSE and HDSI\, also the CTO and cofounder of RapidFire AI. The panelists span UCSD faculty/alumni and AI industry leaders in the San Diego area:\n\nAli Arsanjani\, Google\nRohan Paul\, Illumina\nHao Zhang\, Snowflake and UCSD\n\n\nPlease RSVP by EOD Friday\, November 15 on this Google Form: https://forms.gle/o3L6Tb6ih5u12SiY7 \, The Zoom link will be sent to the registrants soon afterward.
URL:https://datascience.ucsd.edu/event/ai-in-the-enterprise/
LOCATION:Virtual
CATEGORIES:Webinar
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DTSTART;TZID=America/Los_Angeles:20240827T150000
DTEND;TZID=America/Los_Angeles:20240827T160000
DTSTAMP:20260714T234925
CREATED:20240718T211250Z
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SUMMARY:2024 HDSI Virtual Industry Open House
DESCRIPTION:The Halıcıoğlu Data Science Institute (HDSI) at UC San Diego is excited to welcome all employers\, partners\, campus colleagues\, and the broader community to our 2024 Virtual Industry Open House. Join us to learn about the latest developments at HDSI\, including our innovative programs\, engagement opportunities\, and how we are preparing the next generation of data science talent in this new era of AI. \nAgenda Highlights: \n\nFaculty overview of undergraduate and graduate programs\nInsights on how and why to recruit our talented students\nOpportunities for engagement with students\, alumni\, and career services\nIndustry Partnership Alliance\nQ&A session\n\nDate and Time: Tuesday\, August 27th\, from 3:00 PM to 4:00 PM \nRegistration Link: Click here\n\nWe look forward to your participation in shaping the future of data science together.
URL:https://datascience.ucsd.edu/event/2024-hdsi-virtual-industry-open-house/
LOCATION:Virtual
ATTACH;FMTTYPE=image/jpeg:https://datascience.ucsd.edu/wp-content/uploads/2024/07/HDSI_Virtual_Industry_OH_Flyer.jpg
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DTSTART;TZID=America/Los_Angeles:20240417T100000
DTEND;TZID=America/Los_Angeles:20240417T110000
DTSTAMP:20260714T234925
CREATED:20240409T185225Z
LAST-MODIFIED:20240409T185225Z
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SUMMARY:TILOS Seminar: Transformers learn in-context by (functional) gradient descent
DESCRIPTION:Transformers learn in-context by (functional) gradient descent\nXiang Cheng\, TILOS Postdoctoral Scholar at MIT\nHDSI 123 and Zoom: https://ucsd.zoom.us/j/99334315002 \nAbstract: Motivated by the in-context learning phenomenon\, we investigate how the Transformer neural network can implement learning algorithms in its forward pass. We show that a linear Transformer naturally learns to implement gradient descent\, which enables it to learn linear functions in-context. More generally\, we show that a non-linear Transformer can implement functional gradient descent with respect to some RKHS metric\, which allows it to learn a broad class of functions in-context. Additionally\, we show that the RKHS metric is determined by the choice of attention activation\, and that the optimal choice of attention activation depends in a natural way on the class of functions that need to be learned. I will end by discussing some implications of our results for the choice and design of Transformer architectures.
URL:https://datascience.ucsd.edu/event/tilos-seminar-transformers-learn-in-context-by-functional-gradient-descent/
LOCATION:Virtual
CATEGORIES:Seminar
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DTSTART;TZID=America/Los_Angeles:20240308T120000
DTEND;TZID=America/Los_Angeles:20240308T130000
DTSTAMP:20260714T234925
CREATED:20240226T212933Z
LAST-MODIFIED:20240318T225308Z
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SUMMARY:TILOS Webinar: AI Ethics in Research
DESCRIPTION:The Ethics and Early Career Committee would like to invite you to our upcoming webinar on AI Ethics in Research. This will take place virtually through Zoom on Friday\, March 8th at noon Pacific\, 2pm Central\, 3pm Eastern (https://nu.zoom.us/j/2183621123). \nPlease join Dr. Nisheeth Vishnoi from Yale and Dr. David Danks from UC San Diego who will discuss their Research in AI Ethics. Professor Danks develops practical frameworks and methods to incorporate ethical and policy considerations throughout the AI lifecycle\, including different ways to include them in optimization steps. Bias and fairness have been a particular focus given the multiple ways in which they can be measured\, represented\, and used. Professor Vishnoi uses optimization as a lens to study how subjective human and societal biases emerge in the objective world of artificial algorithms\, as well as how to design strategies to mitigate these biases. \nThis event is a great opportunity to learn about the constantly evolving issues of AI Ethics in research and the societal impact of AI. It will also provide a platform for students to gain insights and valuable advice that can help them in their future career pursuits. \nNisheeth Vishnoi is the A. Bartlett Giamatti Professor of Computer Science and a co-founder of the Computation and Society Initiative at Yale University. He studies the foundations of computation\, and his research spans several areas of theoretical computer science\, optimization\, and machine learning.  He is also interested in understanding nature and society from a computational viewpoint. Here\, his current focus includes understanding the emergence of intelligence and developing methods to address ethical issues at the interface of artificial intelligence and humanity. \nDavid Danks is Professor of Data Science & Philosophy and affiliate faculty in Computer Science & Engineering at University of California\, San Diego. His research interests range widely across philosophy\, cognitive science\, and machine learning\, including their intersection. Danks has examined the ethical\, psychological\, and policy issues around AI and robotics across multiple sectors\, including transportation\, healthcare\, privacy\, and security. He has also done significant research in computational cognitive science and developed multiple novel causal discovery algorithms for complex types of observational and experimental data. Danks is the recipient of a James S. McDonnell Foundation Scholar Award\, as well as an Andrew Carnegie Fellowship. He currently serves on multiple advisory boards\, including the National AI Advisory Committee.
URL:https://datascience.ucsd.edu/event/tilos-webinar-ai-ethics-in-research/
LOCATION:Virtual
CATEGORIES:Webinar
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DTSTART;VALUE=DATE:20240304
DTEND;VALUE=DATE:20240307
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SUMMARY:EnCORE Workshop | Old Questions and New Directions in Theory of Clustering
DESCRIPTION:We are hosting an EnCORE workshop on Old Questions and New Directions in Theory of Clustering at UCSD from March 4th to 6th\, 2024. While in person registration is closed due to limited seats availability\, you can register to attend the workshop virtually here: https://sites.google.com/view/clusteringinsandiego \nWe have a stellar lineup of speakers and we hope the workshop will generate many new questions to work on. \n 
URL:https://sites.google.com/view/clusteringinsandiego
LOCATION:Virtual
CATEGORIES:Workshops
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240205T140000
DTEND;TZID=America/Los_Angeles:20240205T150000
DTSTAMP:20260714T234925
CREATED:20240205T170247Z
LAST-MODIFIED:20240205T170333Z
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SUMMARY:Scaling Data-Constrained Language Model
DESCRIPTION:Extrapolating scaling trends suggest that training dataset size for LLMs may soon be limited by the amount of text data available on the internet. In this talk we investigate scaling language models in data-constrained regimes. Specifically\, we run a set of empirical experiments varying the extent of data repetition and compute budget. From these experiments we propose and empirically validate a scaling law for compute optimality that accounts for the decreasing value of repeated tokens and excess parameters. Finally\, we discuss and experiment with approaches for mitigating data scarcity.\n \nBio: Alexander “Sasha” Rush is an Associate Professor at Cornell Tech and a researcher at Hugging Face. His research interest is in the study of language models with applications in controllable text generation\, efficient inference\, and applications in summarization and information extraction. In addition to research\, he has written several popular open-source software projects supporting NLP research\, programming for deep learning\, and virtual academic conferences. His projects have received paper and demo awards at major NLP\, visualization\, and hardware conferences\, an NSF Career Award and Sloan Fellowship. He tweets at @srush_nlp.\n\n\n \n \n 
URL:https://datascience.ucsd.edu/event/scaling-data-constrained-language-model/
LOCATION:Virtual
CATEGORIES:Seminar
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