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DTSTART;TZID=America/Los_Angeles:20260202T104000
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DTSTAMP:20260417T181604
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SUMMARY:ECE Seminar: Advanced Packaging as the Engine of the AI Systems Era
DESCRIPTION:Presenter: Tolga Acikalin\, System and Package Architect\, Lumilens \nDescription: The rapid rise of artificial intelligence and machine learning—most notably recent breakthroughs in large language models—is reshaping the trajectory of the semiconductor industry and ushering in a new era of system innovation. As performance scaling at the device level slows\, heterogeneous integration (HI) has emerged as a foundational technology to sustain advances in computing and communication. By integrating separately manufactured components with diverse functions into a single system\, HI enables new levels of functionality\, performance\, and efficiency that are no longer achievable through traditional scaling alone. \nRealizing the full potential of heterogeneous systems demands a shift toward holistic system-level co-design\, with advanced packaging assuming a central and strategic role. This talk will briefly review the evolution of packaging technologies and then focus on advanced packaging architectures that enable heterogeneous integration.Topics will include advances in 2D and 3D interconnect technologies\, the introduction of novel packaging materials such as glass substrates\, and the growing role of photonic links\, including co-packaged optics enabled by silicon photonics. The talk will conclude with a discussion of power delivery and thermal management as system-level challenges and opportunities that will shape the next generation of high-performance\, energy-efficient systems. \nBio: Tolga Acikalin received his Bachelor of Science degree in Mechanical Engineering from Middle East Technical University in Ankara\, Turkey\, and his Master of Science and Ph.D. degrees from Purdue University in West Lafayette\, Indiana. \nHe joined Intel in 2007 as a Research and Development Engineer\, working on assembly and test pathfinding projects within the Technology and Manufacturing Group in Chandler\, Arizona. From 2013 to 2025\, he was a Principal Engineer at Intel Labs in Santa Clara\, California\, where he led and influenced innovative strategies for heterogeneous system integration\, spanning package- to wafer-scale solutions\, with a strong emphasis on next-generation interconnect technologies. Tolga is currently a System and Package Architect at Lumilens\, where he focuses on next-generation photonic interconnect solutions\, ranging from near-packaged optics to co-packaged optics. \nHis technical interests include co-packaged optics and silicon photonics\, optical and sub-THz to THz RF high-speed interconnects and the associated advanced package architectures\, novel advanced packaging solutions such as glass substrates\, and optical computing. Tolga has authored or co-authored more than 15 peer-reviewed journal and conference publications in leading APS\, ASME\, and IEEE venues\, including best paper awards at IEEE RFIC and JSCC. He holds nine issued patents and more than 27 additional patent filings. \nHosted by: Professor Soumya Bose\, ECE Department \nZoom Link: https://ucsc.zoom.us/j/97975378707?pwd=ljcgaCfhMmhZ88Vt5dqQUBVQRjehOx.1
URL:https://events.ucsc.edu/event/ece-seminar-advanced-packaging-as-the-engine-of-the-ai-systems-era/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260202T120000
DTEND;TZID=America/Los_Angeles:20260202T130000
DTSTAMP:20260417T181604
CREATED:20260122T191932Z
LAST-MODIFIED:20260128T171007Z
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SUMMARY:Statistics Seminar: Mathematical Foundations for Machine Learning from a Nonlinear Time Series Perspective
DESCRIPTION:Presenter: Jiaqi Li\, William H. Kruskal Instructor\, University of Chicago \nDescription:Modern machine learning (ML) algorithms achieve remarkable empirical success\, yet providing rigorous statistical guarantees remains a major challenge\, particularly in distributional theory and online inference methods. In this talk\, we will introduce a novel framework to provide mathematical foundations for ML by bringing powerful tools in nonlinear time series. First\, we focus on the stochastic gradient descent (SGD) with constant learning rates. By interpreting the SGD sequence as a nonlinear AR(1) process\, we can establish the geometric moment contraction (GMC) for SGD regardless of initializations. By this GMC property\, we can derive refined asymptotic theory of SGD and its averaging variant\, including general moment convergence\, quenched central limit theorems\, quenched invariance principles\, and sharp Berry- Esseen bounds. Then\, we extend this theoretical framework to SGD with dropout regularization\, a widely used but theoretically underexplored technique in deep learning. By establishing GMC under explicit learning-rate and dimensional scaling regimes\, we obtain asymptotic normality and invariance principles for dropout SGD and its averaged version. These results enable online inference\, for which we introduce a fully recursive estimator of the long-run covariance matrix appearing in the limiting distributions. The proposed online confidence intervals with asymptotically correct coverage can be generalized to many other ML algorithms. Overall\, viewing online learning algorithms as nonlinear time series provides a powerful toolkit for deriving statistical guarantees in modern ML\, with implications for high-dimensional stochastic optimization and real-time uncertainty quantification. \nBio:Jiaqi Li is a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago. She obtained her PhD in Statistics from Washington University in St. Louis in 2024. Her research focuses on developing theoretical guarantees and statistical inference methods for machine learning algorithms. She also works on time series data\, especially in the high- dimensional settings with complex temporal and cross-sectional dependency structures. She also\ncollaborates with neuroscientists on applications in fMRI and EEG data. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
URL:https://events.ucsc.edu/event/statistics-seminar-mathematical-foundations-for-machine-learning-from-a-nonlinear-time-series-perspective/
LOCATION:https://ucsc.zoom.us/j/96647674332?pwd=rCHfeGpKslaGS5iIPP5Jh29mQiMJID.1
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260202T160000
DTEND;TZID=America/Los_Angeles:20260202T170000
DTSTAMP:20260417T181604
CREATED:20260128T184233Z
LAST-MODIFIED:20260128T184233Z
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SUMMARY:AM Seminar: Are Graph Learning Methods Actually Learning?
DESCRIPTION:Presenter: Seshadhri Comandur\, Professor of Computer Science\, UCSC \nDescription: There has been a lot of literature on graph machine learning over the past few years\, and a bewildering array of new methods. This talk is based on a series of results making a provocative argument. Maybe many graph machine learning methods are not really that effective\, and the progress we are seeing is an artifact of experimental design and measurement. I will talk about some results showing that low-dimensional embeddings with dot product similarity (arguably the most common graph ML technique) cannot capture salient aspects of real-world graphs. Follow-up work demonstrates that simple benchmarks seem to outperform fancier methods\, and that there are significant shortcomings in existing accuracy measurement. \nBio: C. Seshadhri (Sesh) is a professor of Computer Science at the University of California\, Santa Cruz and an Amazon scholar. Prior to joining UCSC\, he was a researcher at Sandia National Labs\, Livermore in the Information Security Sciences department\, during 2010-2014. His primary interest is the theoretical study of algorithms\, especially those with a mix of graphs and randomization. By and large\, Sesh works at the boundary of theoretical computer science (TCS) and data mining. His work spans many areas: sublinear algorithms\, graph algorithms\, graph modeling\, scalable computation\, and data mining. In the theory world\, his work has resolved numerous open problems in monotonicity testing and graph property testing. A number of his papers in the interface of TCS and applied algorithms have received paper awards at KDD\, WWW\, ICDM\, SDM\, and WSDM. He received the 2019 SDM/IBM Early Career Award for Excellence in Data Analytics. Sesh got his Ph.D from Princeton University and spent two years as a postdoc in IBM Almaden Labs. \nHosted by: Ashesh Chattopadhyay\, Applied Mathematics Department
URL:https://events.ucsc.edu/event/am-seminar-are-graph-learning-methods-actually-learning/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260204T120000
DTEND;TZID=America/Los_Angeles:20260204T130000
DTSTAMP:20260417T181604
CREATED:20260128T170858Z
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SUMMARY:Statistics Seminar: Statistical Inference for Multi-Modality Data in the AI Era
DESCRIPTION:Presenter: Qi Xu\, Postdoctoral Researcher\, Department of Statistics & Data Science\, Carnegie Mellon University \nDescription: Multi-modality data are increasingly common across science medicine and technology\, such as imaging\, text\, sensors\, and genomics. These modalities are often high dimensional or unstructured and naturally exhibit blockwise (nonmonotone) missingness where different samples observe different subsets of modalities. Such missingness creates a major obstacle for statistical analyses since classical methods either discard large portions of data or rely on strong modeling assumptions. Recent advances in AI make it possible to generate or predict unobserved modalities from observed ones\, opening new opportunities for data integration. In this talk\, I will focus on statistical inference for blockwise-missing multi-modality data\, while rigorously incorporating modern AI tools. Rooted in semiparametric theory\, there is a long-term open problem that theoretically optimal estimating function under non-monotone missingness is computationally intractable\, even under the missing completely at random mechanism. I introduce a tractable approximation to the optimal estimating equation through a novel Restricted ANOVA hierarchY or RAY decomposition and its almost-eigen-operator property. This leads to a new class of estimators that leverage predictive or generative AI models to borrow information across datasets while remaining unbiased and asymptotically normal. Motivated by the property of the RAY estimator\, we extend the RAY estimator to a class of unbiased\, consistent\, and computationally tractable estimators. The most efficient estimator in this class is then derived\, named as Adaptive RAY estimator\, which optimally integrating all available data and prediction from AI. Simulation studies and a single cell multi-omics application demonstrate that the proposed framework enables stable and efficient inference for complex multi modality data in the AI era. This is a joint work with Lorenzo Testa\, Jing Lei and Kathryn Roeder\, and the paper is available on arXiv: https://arxiv.org/abs/2509.24158 \nBio: Qi Xu is a postdoctoral researcher in the Department of Statistics & Data Science at Carnegie Mellon University. His research interests lie broadly in statistics and machine learning\, especially in data integration and AI for statistics\, with their applications in genomics and mobile health. He received his Ph.D. from the Department of Statistics at University of California\, Irvine\, and the Master degree from University of Illinois Urbana Champaign\, and the Bachelor degree (with honors) from Tongji University. \nHosted by: Statistics Department \nZoom link: https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
URL:https://events.ucsc.edu/event/statistics-seminar-statistical-inference-for-multi-modality-data-in-the-ai-era/
LOCATION:https://ucsc.zoom.us/j/91740050783?pwd=joK9hfwvM7FZ48acaiow8OY4ZlBDXA.1
CATEGORIES:Lectures & Presentations,Seminars
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DTSTART;TZID=America/Los_Angeles:20260206T121500
DTEND;TZID=America/Los_Angeles:20260206T130000
DTSTAMP:20260417T181604
CREATED:20260112T191838Z
LAST-MODIFIED:20260112T191838Z
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SUMMARY:GDAC Portfolio Workshop
DESCRIPTION:Workshop\n\nPart of the GDA Conference on campus – come and learn best practices for creating a portfolio to use in the gaming industry! \n  \nKNOW OUR POLICIES \nJob postings and employer announcements are made without endorsement\, direct or implied\, by Career Success or UCSC. Career Success educates students about various opportunities and ensures equity of access to campus recruiting activities for all employers who abide by our Employer Policies. Individual students are encouraged to determine which employers align with their diverse talents\, values\, and interests. \n  \nYOU BELONG HERE\nPrograms and services are open to all\, consistent with state and federal law\, as well as the University of California’s nondiscrimination policies. Every initiative—whether a student service\, faculty program\, or community event—is designed to be accessible\, inclusive\, and respectful of all identities. To learn more\, please visit UC Nondiscrimination Statement or Nondiscrimination Policy for UC Publications. \nOnline Safety Tips \nUC Santa Cruz Career Success〡Hahn 125 \nEmail Career Success: csuccess@ucsc.edu \nVisit Career Success Website: https://careers.ucsc.edu
URL:https://events.ucsc.edu/event/gdac-portfolio-workshop/
LOCATION:Cultural Center – Merrill College\, 641 Merrill Rd\, Santa Cruz\, 95064\, United States
CATEGORIES:Lectures & Presentations,Meetings & Conferences,Seminars,Workshop
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