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X-WR-CALDESC:Events for Halıcıoğlu Data Science Institute - UC San Diego
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DTSTART;TZID=America/Los_Angeles:20241008T130000
DTEND;TZID=America/Los_Angeles:20241008T140000
DTSTAMP:20260531T082238
CREATED:20241008T003616Z
LAST-MODIFIED:20241008T201835Z
UID:10000501-1728392400-1728396000@datascience.ucsd.edu
SUMMARY:The Critical Role of Cyber Infrastructure in City Innovation and Beyond
DESCRIPTION:Talk Abstract \n\nCities\, humanity’s greatest inventions\, offer vast opportunities for innovation in science and technology. The increasing availability of big data paints a promising future for our cities. Over the past decade\, my work has focused on applying AI to address real-world city challenges. Recent collaborations with city practitioners have deepened my understanding of these complexities and refined my vision for achieving city intelligence. \nIn this talk\, I will present my work on advanced AI techniques for city transportation problems\, e.g.\, reinforcement learning for traffic signal control. I will then expand on this to discuss the resource-centric concept of city intelligence\, using real-world practices to showcase its practical applications. Finally\, I will emphasize the urgent need for new cyber infrastructure\, vital not only for city innovations but for all scientific disciplines driven by big data and intensive computing. \n\nSpeaker Bio\nDr. Zhenhui (Jessie) Li currently serves as the chief scientist at the Yunqi Academy of Engineering\, a non-profit institution situated in Hangzhou\, China. Prior to this role\, she held a tenured associate professor position at Pennsylvania State University. She earned her doctoral degree in Computer Science from the University of Illinois at Urbana-Champaign. Her research has been primarily devoted to advancing computing technologies to unlock the potential of data for cross-disciplinary research\, with a specific emphasis on city applications. For further information\, you can visit her website at (https://jessielzh.com/).
URL:https://datascience.ucsd.edu/event/the-critical-role-of-cyber-infrastructure-in-city-innovation-and-beyond/
LOCATION:Computer Science & Engineering Building (CSE)\, Room 1202
CATEGORIES:Webinar
ATTACH;FMTTYPE=image/png:https://datascience.ucsd.edu/wp-content/uploads/2024/01/cropped-HDSI-UCSD-Image-e1712856546428.png
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241015T123000
DTEND;TZID=America/Los_Angeles:20241015T140000
DTSTAMP:20260531T082238
CREATED:20241008T002133Z
LAST-MODIFIED:20241008T003755Z
UID:10000500-1728995400-1729000800@datascience.ucsd.edu
SUMMARY:MathWorks Technical Seminar: AI & Machine Learning in Real-World Systems
DESCRIPTION:Beyond their use in traditional data analytics problems\, AI and Machine Learning techniques are changing the way real-world complex systems (like vehicles\, airplanes\, and even industrial production lines) are designed\, tested and fabricated. When building these systems\, engineers rely heavily on modeling and computer-assisted simulations. This seminar showcases how MATLAB and Simulink can help integrate AI models into the component and system level simulation stages of the design process\, and how they can help deploy the resulting control algorithms into the real-world systems. We will use an example where deep-learning and machine learning can be used to create a Virtual Sensor algorithm. Join us for a complementary seminar to see how this is done! Highlights include: \n\nDesigning and training machine learning components with Statistics and Machine Learning Toolbox\nDesigning and training deep learning components with Deep Learning Toolbox\nImporting trained TensorFlow models into MATLAB\nIntegrating machine learning and deep learning models into Simulink for system-level simulation\nGenerating library-free C code and performing PIL tests\nLSTM Model Compression using projection\n\nThis is the first in a 3 part seminar series in partnership with HDSI\, JSOE\, and SIO for this academic year. \nPlease note that this is open to all faculty\, staff\, and students (all grade & majors). \nRegister Here \n*Lunch is included in registration.
URL:https://datascience.ucsd.edu/event/mathworks-technical-seminar-ai-machine-learning-in-real-world-systems/
LOCATION:Halıcıoğlu Data Science Institute (HDSI)\, Room 123\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241021T130000
DTEND;TZID=America/Los_Angeles:20241021T140000
DTSTAMP:20260531T082238
CREATED:20241029T173423Z
LAST-MODIFIED:20241029T173423Z
UID:10000502-1729515600-1729519200@datascience.ucsd.edu
SUMMARY:HDSI Seminar -  Generative Social Choice | Ariel Procaccia
DESCRIPTION:Talk Information: \nWhen Monday Oct 21st 1:00pm\nWhere: HDSI MPR 123\nZoom Info: http://bit.ly/HDSI-Seminars \nTitle: Generative Social Choice \nAbstract: “The mathematical study of voting\, social choice theory\, has traditionally only been applicable to choices among a few predetermined alternatives\, but not to open-ended decisions such as collectively selecting a textual statement. This limitation is addressed by generative social choice\, a design methodology for open-ended democratic processes that combines the rigor of social choice theory with the capability of large language models to generate text and extrapolate preferences. I’ll introduce a framework that divides the design of AI-augmented democratic processes into two components: first\, proving that the process satisfies representation guarantees when given access to oracle queries; second\, empirically validating that these queries can be approximately implemented using a large language model. I’ll also discuss the application of this framework to the problem of summarizing free-form opinions into a proportionally representative slate of opinion statements. By providing rigorous guarantees\, generative social choice could alleviate concerns about AI-driven democratic innovation and help unlock its potential. \nBio: Ariel Procaccia is Gordon McKay Professor of Computer Science at Harvard University. He works on a broad and dynamic set of problems related to AI\, algorithms\, economics\, and society. He has helped create systems and platforms that are widely used to solve everyday fair division problems\, resettle refugees and select citizens’ assemblies. To make his research accessible to the public\, he regularly writes opinion and exposition pieces for publications such as the Washington Post\, Bloomberg\, Wired and Scientific American. He is a AAAI Fellow (2024) and a recipient of the ACM SIGecom Mid-Career Award (2024)\, Social Choice and Welfare Prize (2020)\, Guggenheim Fellowship (2018)\, IJCAI Computers and Thought Award (2015) and Sloan Research Fellowship (2015).
URL:https://datascience.ucsd.edu/event/hdsi-seminar-generative-social-choice-ariel-procaccia/
LOCATION:Halıcıoğlu Data Science Institute Room 123\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241024T100000
DTEND;TZID=America/Los_Angeles:20241024T110000
DTSTAMP:20260531T082239
CREATED:20241023T181207Z
LAST-MODIFIED:20241023T182044Z
UID:10000503-1729764000-1729767600@datascience.ucsd.edu
SUMMARY:Deep Learning: a Non-parametric Statistical Viewpoint
DESCRIPTION:ABSTRACT \nThe advent of deep learning has completely revolutionized how we perceive data to obtain superhuman performance across all fields of modern science. However\, despite the remarkable empirical successes of deep learners\, the theoretical guarantees for their statistical accuracy remain rather pessimistic. In particular\, the data distributions on which deep learners are generally applied\, such as natural images\, are often hypothesized to have an intrinsic low-dimensional structure in a typically high-dimensional feature space. However\, this is often not reflected in the derived rates in the state-of-the-art analyses. This talk aims to bridge the gap between the theory and practice of deep learning from a statistical perspective. We demonstrate that deep learners exhibit a convergence rate determined solely by the intrinsic dimensionality of the data\, rather than its nominal high-dimensional feature representation. Our work not only provides practical guidelines for selecting suitable network architectures but also connects the theoretical analyses of these models to established convergence rates in optimal transport and non-parametric statistics literature. In particular\, we derive the sharpest convergence rates for various learning scenarios\, including Generative Adversarial Networks (GANs)\, Wasserstein Autoencoders (WAEs)\, federated learning\, Bi-directional GANs\, and general deep supervised learners. Furthermore\, we introduce a novel measure\, called the entropic dimension\, to characterize the intrinsic dimension of probability measures and achieve the sharpest known approximation results for neural networks employing Rectified Linear Unit (ReLU) activation\, improving upon classical benchmarks. \nBIOGRAPHY \nSaptarshi Chakraborty is a fifth-year Ph.D. student in Statistics at the University of California\, Berkeley\, advised by Prof. Peter Bartlett. Prior to joining Berkeley\, he earned his M.Stat and B. Stat (Hons.) degrees in Statistics from the Indian Statistical Institute (ISI)\, Kolkata\, India. He is primarily interested in the theoretical and methodological foundations of machine learning\, especially\, deep learning theory\, unsupervised learning\, dimensionality reduction\, optimal transport\, and optimization. \nZOOM LINK: https://ucsd.zoom.us/j/93363424503
URL:https://datascience.ucsd.edu/event/deep-learning-a-non-parametric-statistical-viewpoint/
LOCATION:Atkinson Hall\, Fourth Floor
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20241030T130000
DTEND;TZID=America/Los_Angeles:20241030T143000
DTSTAMP:20260531T082239
CREATED:20241029T173302Z
LAST-MODIFIED:20241029T173302Z
UID:10000504-1730293200-1730298600@datascience.ucsd.edu
SUMMARY:HDSI Seminar - Maksim Kitsak -Modeling and Inference of Complementarity Mechanisms in Networks.
DESCRIPTION:Talk Information:\nWhen Wednesday Oct 30th 1:00pm\nWhere: HDSI MPR 123\nZoom Info: http://bit.ly/HDSI-Seminars \nTitle: Modeling and Inference of Complementarity Mechanisms in Networks. \nAbstract: “In many networks\, including networks of protein-protein interactions\, interdisciplinary collaboration networks\, and semantic networks\, connections are established between nodes with complementary rather than similar properties. What is complementarity?\nThe Oxford Dictionary asserts that “”two people or things that are complementary are different but together form a useful or attractive combination of skills\, qualities or physical features.”” Sadly\, our understanding of complementarity in networks does not\ngo far beyond definition. While complementarity is abundant in networks\, we lack mathematical intuition and quantitative methods to study complementarity mechanisms in these systems. Instead\, we routinely retreat to using available off-the-shelf methods developed in the first place for similarity-driven networks. \nIn my talk\, I will discuss my group’s recent achievements in the analysis of complementarity mechanisms in networks. I will first explain why existing similarity-based inference and learning methods are not readily applicable to systems where complementarity between interacting nodes plays a significant role. I will then deduce\, starting with the definition by the Oxford Dictionary\, a general complementarity framework for networks capable of describing any matching relations and containing both similarity and antitheses relations as special cases. Using the general framework\, I will formulate a minimal null model to learn complementarity embeddings of real networks via maximum-likelihood estimation. I will demonstrate how complementarity embeddings can be used to infer both complementary and similar nodes in a network\, enabling network inference tasks\, such as link prediction and community detection. I will conclude my talk with an outlook on the interplay of similarity and complementarity in the formation of networks\, arguing for a careful re-evaluation of existing similarity-inspired methods.” \nBio: “Maksim Kitsak is an Associate Professor of the Electrical Engineering\, Mathematics\, and Computer Science faculty of the Delft University of Technology\, the Netherlands. Prof. Kitsak has been working at the intersection of Network Theory\, Machine Learning\, and Statistical Physics. Prof. Kitsak is particularly interested in the fundamental principles behind non-Euclidean network embeddings and novel applications of network embeddings in communication and biological networks. His research is often published in prestigious journals\, such as Nature and Science Families. Prof. Kitsak gratefully acknowledges the financial support of the National Science Foundation (NSF\, USA)\, Army Research Office (ARO\, USA)\, and the Dutch Research Council (NWO\, NL).”
URL:https://datascience.ucsd.edu/event/hdsi-seminar-maksim-kitsak-modeling-and-inference-of-complementarity-mechanisms-in-networks/
LOCATION:Halıcıoğlu Data Science Institute\, 3234 Matthews Ln\, La Jolla\, CA 92093\, USA Room 123
CATEGORIES:Seminar
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