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DTSTART;TZID=America/Los_Angeles:20251114T130000
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SUMMARY:Prof. Ruben Vescovo (Tohoku University): Modeling Japan’s DMAT Framework: an agent-based model for disaster medical mobility at scale
DESCRIPTION:For this week’s IGS seminar\, we’ll have Prof. Ruben Vescovo\, a visiting collaborator from Tohoku University to present his work.\n\nTime & Location:\n\n\nFriday Nov 14\, 2025\, 1:00 PM – 2:00 PM\nDonald Bren Hall 3011\, ICS\, UC Irvine \nLunch will be provided. \nTitle:\nModeling Japan’s DMAT Framework: an agent-based model for disaster medical\nmobility at scale \nAbstract: \nDisasters provide us with a unique set of conditions: they are both incredibly destructive\nand very infrequent. Statistically speaking\, when we consider disasters\, we are thinking\nabout distribution outliers with significant magnitude\, which are often not in distribution\nof one another. Hence\, studying mobility for a disaster is non-deterministic\, causal\nsystem that is highly coupled with the co-disaster or post-disaster condition.\n\nDisaster management frameworks that deal with the co-disaster and post-disaster\nlogistics are often devised in the mitigation and preparedness stages of the disaster-\ncycle\, but remain unproven until the next disaster\, which inevitably prompts a review of\nthe framework resulting from unaccounted-for disaster-driven circumstances.\nOne such framework is the Japanese Disaster Medical Assistance Team (DMAT)\, a\ngovernment taskforce comprised of medical professionals (doctors\, nurses\, logistics\npersonnel) which is prescribed to operate in the post disaster window (0 to 72 hours)\nafter a disaster event. DMAT is a centrally managed\, strictly regulated\, command &\ncontrol organization which is tasked with assisting hospital operations by facilitating\npatient processing\, transfers\, and transport.\n\nTo better understand the limitations of the DMAT system in context\, IRIDeS is\ncollaborating with the Japanese Government to reproduce the DMAT framework as an\nAgent-Based “Digital Shadow” to test various post-disaster conditions\, infrastructure\narrangements\, and DMAT supply & support flows. Due to the scale of operations\, the\nmodel is being developed as a high-performance parallel system designed to operate\non vector processing units and wide CPU throughput configurations.\n\nBio: \n\n\n\nRuben is Assistant Professor at the International Research Institute of Disaster Science\n(IRIDeS) at Tohoku University. Currently his research focus is Agent Based Model applications to disaster management and disaster science\, with an emphasis on decision making agents under uncertain conditions. Ruben’s previous work centered on statistical and probabilistic machine learning applications to uncertainty-aware predictive hazard-to-vulnerability models for\ninfrastructure in disasters.
URL:https://isg.ics.uci.edu/event/prof-ruben-vescovo-tohoku-university-modeling-japans-dmat-framework-an-agent-based-model-for-disaster-medical-mobility-at-scale/
LOCATION:DBH 3011
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DTSTART;TZID=America/Los_Angeles:20251121T130000
DTEND;TZID=America/Los_Angeles:20251121T140000
DTSTAMP:20260520T130022
CREATED:20251004T082112Z
LAST-MODIFIED:20251028T011209Z
UID:2271-1763730000-1763733600@isg.ics.uci.edu
SUMMARY:Dr. Matteo Interlandi (Microsoft): Query Processing on Tensor Computation Runtimes
DESCRIPTION:The Department of Computer Science\, Information Systems Group\, UC Irvine \nWELCOMES \nDr. Matteo Interlandi \nGray Systems Lab (GSL) at Microsoft \nQuery Processing on Tensor Computation Runtimes \nNovember 21\, 2025\, Friday\, 1 – 2 pm\, DBH 3011\, UC Irvine \nThe huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in new hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices\, which are now part of the offerings of major cloud providers. Meanwhile\, by hiding the low-level complexity through a tensor-based interface\, AI frameworks such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this talk\, we will present how databases can ride the wave of innovation happening in the AI space thanks to Tensor Query Processor (TQP). TQP is the first AI-native SQL query processor leveraging AI frameworks for: (1) efficiently running SQL queries on GPUs; (2) scale out query execution on clusters of GPU nodes; and (3) bring new multi-modal capabilities into SQL. \nMatteo Interlandi is a Principal Scientist Manager in the Gray Systems Lab (GSL) at Microsoft. Before Microsoft\, he was a Postdoc at the University of California\, Los Angeles. Matteo received his Ph.D. from the University of Modena and Reggio Emilia. Matteo’s work has received a best demo award at VLDB 2022\, and honorable mentions at SIGMOD 2021 and VLDB 2023/24.
URL:https://isg.ics.uci.edu/event/dr-matteo-interlandi-microsoft-query-processing-on-tensor-computation-runtimes/
LOCATION:DBH 3011
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