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DTSTART;TZID=America/New_York:20230915T120000
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SUMMARY:CS3 Monthly Research Exchange
DESCRIPTION:At each CS3 Monthly Research Exchange\, three faculty and student researchers in the field of smart urban planning will take the stage to share their latest findings\, breakthroughs\, and urban projects. \nAbhishek Adhikari\, M.S/Ph.D Student at Columbia University \n\nPresentation Abstract: Beyond-5G and 6G wireless networks can sense the nearby environment in addition to performing traditional communication responsibilities via Joint Communications and Sensing (JCAS). A potential application of JCAS could be to sense vehicles crossing a street intersection and communicate to pedestrians who may be visually impaired. In this talk\, we share preliminary vehicle detection results at a street intersection in NYC using a Nokia Bell Labs 28 GHz channel sounder traditionally used for propagation modeling in communication.\n\nStepan Mazokha\, Ph.D. Student at Florida Atlantic University \n\nPresentation Abstract: In this presentation\, I will discuss the details of my recent paper entitled\, “MobLoc: CSI-based Location Fingerprinting with MUSIC”. The objective of the project has been to implement and evaluate a passive WiFi localization method using Channel State Information. The latter has been designed using a fingerprinting method using a 1D MUSIC algorithm and was able to achieve meter-level localization in several indoor environments.\n\nJorge Ortiz\, Assistant Professor at Rutgers University \n\nPresentation Abstract: This talk focuses on human-AI interaction through multimodal learning and interaction\, highlighting my lab’s past and future work on intelligent agents that utilize and integrate multimodal learning techniques to infer human intent and enable innovative forms of interaction. We explore interaction within vehicles\, robots that assist disabled individuals\, and investigate methods to close the learning loop through interactive learning agents. Additionally\, we examine various forms of intervention in a vehicular context and assess how these techniques translate to future Streetscape interactive systems.\n\nThe CS3 Monthly Research Exchanges are internal and open only to CS3 affiliated students\, faculty\, and staff. If you are interested in learning more about the research happening at CS3\, please contact our team at streetscapes@columbia.edu.
URL:https://cs3-erc.org/event/cs3-monthly-research-exchange-2/
LOCATION:Hybrid
CATEGORIES:SLC
ATTACH;FMTTYPE=image/png:https://cs3-erc.org/wp-content/uploads/2023/09/9.15.23-CS3-Monthly-Research-Exchange.png
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DTSTART;TZID=UTC:20230818T120000
DTEND;TZID=UTC:20230818T130000
DTSTAMP:20260418T174527
CREATED:20230814T180728Z
LAST-MODIFIED:20241210T231302Z
UID:829-1692360000-1692363600@cs3-erc.org
SUMMARY:CS3 Monthly Research Exchange
DESCRIPTION:At each CS3 Monthly Research Exchange\, three faculty and student researchers in the field of smart urban planning will take the stage to share their latest findings\, breakthroughs\, and urban projects. \nSharon Di\, Associate Professor at Columbia University \n\nPresentation Abstract: Transportation digital twins have become increasingly popular tools to improve traffic efficiency and safety. However\, the majority of effort nowadays is focused on the “eyes” of the digital twin\, which is object detection using computer vision. I believe the key to empowering the intelligence of a transportation digital twin lies in its “brain\,” namely\, how to utilize the information extracted from various sensors to infer traffic dynamics evolution and devise optimal control and management strategies with real-time feedback to guide the transportation ecosystem toward a social optimum. My research aims to employ tools including machine learning and game theory to develop an urban transportation digital twin.\n\nAbigail Joseph\, Undergraduate Student at Florida Atlantic University \n\nPresentation Abstract: Integrating Unity with real data\, AI technology\, and C# scripting is used to a create digital twin environment of West Palm Beach\, where multiple agents’ traveling behaviors are defined at a streetscape level\, including walking at a particular speed\, stopping at an intersection to look for cars before crossing\, and avoiding other pedestrians. It is anticipated that the methods which produced these results will be used to simulate the interactions between vehicles and pedestrians with substantial accuracy.\n\nCarl Vondrick\, Associate Professor at Columbia University \n\nPresentation Abstract: Computer vision algorithms need to combine many skills — spatial\, physical\, mathematical\, geometrical\, and cognitive — in order to accurately analyze the visual world. In this talk\, I will show how large generative models equip neural networks with these skills\, thereby providing versatile representations for reconstructing 3D\, answering questions\, and recognizing objects. Through a series of experimental results\, I will moreover show how this approach naturally provides inherent explainability of the decision making process\, while also achieving state-of-the-art zero-shot performance across different tasks and benchmarks. In some cases\, this framework can even perform super-human perception.\n\nThe CS3 Monthly Research Exchanges are internal and open only to CS3 affiliated students\, faculty\, and staff. If you are interested in learning more about the research happening at CS3\, please contact our team at streetscapes@columbia.edu.
URL:https://cs3-erc.org/event/cs3-monthly-research-exchange/
LOCATION:Hybrid
CATEGORIES:SLC
ATTACH;FMTTYPE=image/png:https://cs3-erc.org/wp-content/uploads/2023/08/8.18.23-CS3-Monthly-Research-Exchange-1.png
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