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DTSTART;TZID=America/New_York:20250411T093000
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DTSTAMP:20260416T130805
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UID:88400-1744363800-1744367400@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Spatiotemporal Drought Analysis and Prediction: From Pattern Tracking to Impact Assessment
DESCRIPTION:The increasing frequency and severity of drought events worldwide demand innovative approaches that can effectively characterize\, track\, predict\, and assess impacts of the complex spatiotemporal dynamics of water scarcity. This presentation explores how integrated spatiotemporal methodologies enhance drought monitoring\, forecasting\, and impact assessment across diverse global regions. \nOur research demonstrates a logical progression from fundamental spatiotemporal drought tracking to advanced prediction methodologies and practical impact assessment. Beginning with approaches to construct drought tracks through space and time in India\, we identify key drought characteristics including onset location\, pathway\, duration\, severity\, and rotation. These spatial patterns reveal critical information about drought migration and evolution that provide insights into underlying climate and land surface drivers. \nBuilding on this foundation of spatiotemporal drought characterization\, we apply machine learning techniques to predict drought occurrence and intensity across Kazakhstan’s diverse climate zones. By utilizing non-contiguous drought analysis for feature extraction\, machine learning models achieve remarkable prediction accuracy at lead times of up to six months in these arid and semi-arid regions. Additionally\, deep learning techniques enhanced with ensemble empirical mode decomposition substantially improve soil moisture anomaly forecasting in China’s Huai River basin by better capturing the complex non-stationary time series characteristics of drought evolution. \nBeyond monitoring and forecasting\, we extend our approach to predict drought impacts on agriculture in Northeast India using spatiotemporal drought patterns as input for crop yield forecasting. This methodology integrates polynomial regression and artificial neural network models\, where the spatial extent and temporal evolution of drought areas serve as key predictive features. The results demonstrate that changes in drought area and its temporal aggregation provide an important pre-processing alternative for implementing machine learning models for drought impact prediction. \nThese integrated approaches\, spanning regions from Central Asia to South and East Asia\, contribute to a more robust understanding of drought dynamics by revealing how drought moves and evolves in space and time\, enabling more effective early warning systems and supporting agricultural decision-making in increasingly water-stressed environments. Across all applications and geographical contexts\, the spatiotemporal properties of drought emerge as key features that significantly enhance the performance of machine learning prediction models. \nBio: Dr. Gerald A. Corzo is an Associate Professor of Hydroinformatics at IHE Delft Institute for Water Education\, where he leads cutting-edge research that uses Artificial Intelligence and Machine Learning to address critical challenges in climate adaptation and water resource management. His research on hydrometeorological extremes has helped us better understand\, predict\, and respond to floods and droughts across the globe. As Principal Investigator on significant EU-funded projects like EU-WATCH\, Climate Intelligent (EU-CLINT) and EU-NAIADES\, Dr. Corzo has developed cutting-edge AI-driven tools that enhance decision-making in water management and climate resilience. He currently serves as an Editor of the Journal of Hydrology (Elsevier) and was recognized with the prestigious IAHS Tison Award for his contributions to hydrological sciences in 2012. Dr. Corzo’s interdisciplinary approach integrates environmental science with advanced computational technologies to create practical solutions aligned with sustainable development goals\, particularly for enhancing ecosystem resilience in vulnerable communities worldwide.
URL:https://c2smart.engineering.nyu.edu/event/seminar-spatiotemporal-drought-analysis-and-prediction-from-pattern-tracking-to-impact-assessment/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars
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DTSTART;TZID=America/New_York:20250416T130000
DTEND;TZID=America/New_York:20250416T140000
DTSTAMP:20260416T130805
CREATED:20250402T154334Z
LAST-MODIFIED:20250402T154334Z
UID:88242-1744808400-1744812000@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Investigating Vulnerabilities in Autonomous Vehicle Perception Algorithms
DESCRIPTION:Autonomous vehicles (AVs) rely on deep neural networks (DNNs) for critical tasks such as environment perception—identifying traffic signs\, pedestrians\, and lane markings—and executing control decisions like braking\, acceleration\, and lane changing. However\, DNNs are vulnerable to adversarial attacks\, including structured perturbations to inputs and misleading training samples that can degrade performance. This presentation begins with an overview of adversarial training\, emphasizing the impact of input sizes on DNNs’ vulnerability to cyberattacks. Subsequently\, I will share our recent findings that explore the hypothesis that DNNs learn piecewise linear relationships between inputs and outputs. This conjecture is crucial for developing both adversarial attacks and defense strategies in machine learning security. The last part of the presentation will focus on recent work on using error-correcting codes to safeguard DNN-based classifiers. \nDr. Saif Jabari is an Associate Professor of Civil and Urban Engineering at New York University Abu Dhabi (NYUAD) and a Global Network Associate Professor at the Tandon School of Engineering at NYU in Brooklyn\, NY. At NYUAD\, he is co-PI of the Center for Integrated Urban Networks (CITIES) and the Center for Stability\, Instability\, and Turbulence (SITE). He is an Associate Editor for Transportation Science and Area Editor with the new Elsevier journal Artificial Intelligence for Transportation. His research focuses on developing advanced computational methods and theoretical guarantees of performance for urban traffic management problems. The techniques integrate traffic data\, typically in high resolution\, with principles of traffic physics to address the rapidly evolving needs of the field. His current research focuses on understanding and addressing vulnerabilities in deep neural networks\, specifically as they relate to environment perception in autonomous vehicles.
URL:https://c2smart.engineering.nyu.edu/event/seminar-investigating-vulnerabilities-in-autonomous-vehicle-perception-algorithms/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars
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DTSTART;TZID=America/New_York:20250422T173000
DTEND;TZID=America/New_York:20250422T190000
DTSTAMP:20260416T130805
CREATED:20250410T193226Z
LAST-MODIFIED:20250410T193226Z
UID:88432-1745343000-1745348400@c2smart.engineering.nyu.edu
SUMMARY:AI: Forging the Future of the AEC Industry
DESCRIPTION:Artificial intelligence (AI) is revolutionizing the architecture\, engineering\, and construction (AEC) industry by driving innovation across planning\, design\, construction\, and operations. At the core of this transformation is the ability to collect\, process\, and act on vast amounts of data in ways that were previously unimaginable. \nTo explore these groundbreaking developments\, New York University and Professional Women in Construction of New York have assembled a panel of visionary leaders who will assess AI alternatives and discuss how they are shaping the future of the AEC industry. \nThis event will provide an invaluable opportunity to gain insights into how AI is redefining workflows and creating new possibilities for professionals in this field.\n \nEvent Details:\n Date: Tuesday\, April 22\, 2025\n Time:  5:30 PM – 7:00 PM (EDT)\n Location: Pfizer Auditorium\, 5 Metrotech\, Brooklyn\, NY\n \nPanelists:\nSemiha Ergan\, NYU Tandon School of Engineering\, Civil and Urban Engineering Department \nKiSeok Jeon\, STV\nMelissa Forstell McAneny. Trunk Tools\nMonica Nelson\, Gilbane Building Company\n\n \nModerator:\nMohamad Awada\, NYU Tandon School of Engineering\,Civil and Urban Engineering Department 
URL:https://c2smart.engineering.nyu.edu/event/ai-forging-the-future-of-the-aec-industry/
LOCATION:5 metro\, 5 MetroTech\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Seminars
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DTSTART;TZID=America/New_York:20250423T140000
DTEND;TZID=America/New_York:20250423T150000
DTSTAMP:20260416T130805
CREATED:20250304T192602Z
LAST-MODIFIED:20250304T192602Z
UID:87966-1745416800-1745420400@c2smart.engineering.nyu.edu
SUMMARY:Fireside Chat with David Hammer\, co-founder of Popwheels
DESCRIPTION:This student-moderated Fireside Chat will feature David Hammer\, co-founder of Popwheels\, a cutting-edge e-bike battery swap network. David will discuss his career trajectory\, the challenges of standing up urban-scale technology in NYC\, and more!
URL:https://c2smart.engineering.nyu.edu/event/fireside-chat-with-david-hammer-co-founder-of-popwheels/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events
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