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DTSTART;TZID=America/Los_Angeles:20220119T100000
DTEND;TZID=America/Los_Angeles:20220119T110000
DTSTAMP:20260403T125608
CREATED:20250904T173520Z
LAST-MODIFIED:20250904T173520Z
UID:7350-1642586400-1642590000@tilos.ai
SUMMARY:TILOS Seminar: Real-time Sampling and Estimation: From IoT Markov Processes to Disease Spread Processes
DESCRIPTION:Shirin Saeedi Bidokhti\, Assistant Professor\, University of Pennsylvania \nAbstract: The Internet of Things (IoT) and social networks have provided unprecedented information platforms. The information is often governed by processes that evolve over time and/or space (e.g.\, on an underlying graph) and they may not be stationary or stable. We seek to devise efficient strategies to collect real-time information for timely estimation and inference. This is critical for learning and control.\nIn the first part of the talk\, we focus on the problem of real-time sampling and estimation of autoregressive Markov processes over random access channels. For the class of policies in which decision making has to be independent of the source realizations\, we make a bridge with the recent notion of Age of Information (AoI) to devise novel distributed policies that utilize local AoI for decision making. We also provide strong guarantees for the performance of the proposed policies. More generally\, allowing decision making to be dependent on the source realizations\, we propose distributed policies that improve upon the state of the art by a factor of approximately six. Furthermore\, we numerically show the surprising result that despite being decentralized\, our proposed policy has a performance very close to that of centralized scheduling. \nIn the second part of the talk\, we go beyond time-evolving processes by looking at spread processes that are defined over time as well as an underlying network. We consider the spread of an infectious disease such as COVID-19 in a network of people and design sequential testing (and isolation) strategies to contain the spread. To this end\, we develop a probabilistic framework to sequentially learn nodes’ probabilities of infection (using test observations) by an efficient backward-forward update algorithm that first infers about the state of the relevant nodes in the past before propagating that forward into future. We further argue that if nodes’ probabilities of infection were accurately known at each time\, exploitation-based policies that test the most likely nodes are myopically optimal in a relevant class of policies. However\, when our belief about the probabilities is wrong\, exploitation can be arbitrarily bad\, as we provably show\, while a policy that combines exploitation with random testing can contain the spread faster. Accordingly\, we propose exploration policies in which nodes are tested probabilistically based on our estimated probabilities of infection  Using simulations\, we show in several interesting settings how exploration helps contain the spread by detecting more infected nodes\, in a timely manner\, and by providing a more accurate estimate of the nodes’ probabilities of infection.
URL:https://tilos.ai/event/tilos-seminar-real-time-sampling-and-estimation-from-iot-markov-processes-to-disease-spread-processes/
LOCATION:Virtual
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2021/09/ShirinSaeediBidokhti300x240.jpg
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DTSTART;TZID=America/Los_Angeles:20211215T100000
DTEND;TZID=America/Los_Angeles:20211215T110000
DTSTAMP:20260403T125608
CREATED:20250903T191352Z
LAST-MODIFIED:20250903T191352Z
UID:7363-1639562400-1639566000@tilos.ai
SUMMARY:TILOS Seminar: Closing the Virtuous Cycle of AI for IC and IC for AI
DESCRIPTION:David Pan\, Professor\, The University of Texas at Austin \nAbstract: The recent artificial intelligence (AI) boom has been primarily driven by three confluence forces: algorithms\, big-data\, and computing power enabled by modern integrated circuits (ICs)\, including specialized AI accelerators. This talk will present a closed-loop perspective for synergistic AI and agile IC design with two main themes\, AI for IC and IC for AI. As semiconductor technology enters the era of extreme scaling and heterogeneous integration\, IC design and manufacturing complexities become extremely high. More intelligent and agile IC design technologies are needed than ever to optimize performance\, power\, manufacturability\, design cost\, etc.\, and deliver equivalent scaling to Moore’s Law. This talk will present some recent results leveraging modern AI and machine learning advancement with domain-specific customizations for agile IC design and manufacturing\, including open-sourced DREAMPlace (DAC’19 and TCAD’21 Best Paper Awards)\, DARPA-funded MAGICAL project for analog IC design automation\, and LithoGAN for design-technology co-optimization. Meanwhile on the IC for AI frontier\, customized ICs\, including those with beyond-CMOS technologies\, can drastically improve AI performance and energy efficiency by orders of magnitude. I will present our recent results on hardware and software co-design for optical neural networks and photonic ICs (which won the 2021 ACM Student Research Competition Grand Finals 1st Place). Closing the virtuous cycle between AI and IC holds great potential to significantly advance the state-of-the-art of each other.
URL:https://tilos.ai/event/tilos-seminar-closing-the-virtuous-cycle-of-ai-for-ic-and-ic-for-ai/
LOCATION:Virtual
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2021/09/Pan-David300x240.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211117T100000
DTEND;TZID=America/Los_Angeles:20211117T110000
DTSTAMP:20260403T125608
CREATED:20250903T191205Z
LAST-MODIFIED:20250903T191205Z
UID:7364-1637143200-1637146800@tilos.ai
SUMMARY:TILOS Seminar: A Mixture of Past\, Present\, and Future
DESCRIPTION:Arya Mazumdar\, Associate Professor\, UC San Diego \nAbstract: The problems of heterogeneity pose major challenges in extracting meaningful information from data as well as in the subsequent decision making or prediction tasks. Heterogeneity brings forward some very fundamental theoretical questions of machine learning. For unsupervised learning\, a standard technique is the use of mixture models for statistical inference. However for supervised learning\, labels can be generated via a mixture of functional relationships. We will provide a survey of results on parameter learning in mixture models\, some unexpected connections with other problems\, and some interesting future directions.
URL:https://tilos.ai/event/tilos-seminar-a-mixture-of-past-present-and-future/
LOCATION:Virtual
CATEGORIES:TILOS Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/03/aryamazumdar_headshot-e1756926709113.jpg
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