Student Events
Events
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CUE Distinguished Seminar Series: Simulating the Physical and the Connected: Neural Models for Structures and Networks
C2SMART Center Viz Lab 6 Metrotech Center, Room 460, BrooklynAdvances in machine learning are transforming how we model and design engineered systems, from industrial components to large-scale infrastructure networks. This seminar presents recent developments in physics-based neural networks and
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Seminar: AI-empowered Digital Twin for Traffic Safety Analysis
C2SMART Center Viz Lab 6 Metrotech Center, Room 460, BrooklynAbstract: Traffic safety research faces the paradox of rarity—the most critical crashes occur so infrequently that passive methods relying on historical records cannot accurately estimate traffic risks. This talk first
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Student Learning Hub: Building a Scalable Multithreaded Processing System on AWS Cloud
This lecture introduces the design and implementation of a serverless multithreaded processing system using AWS Step Functions, AWS Lambda, and Amazon S3. The session covers how to orchestrate parallel tasks,
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AECOM Fireside Chat & Panel
C2SMART Center Viz Lab 6 Metrotech Center, Room 460, Brooklyn -
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Seminar: A Large-scale Simulation Platform and AI-driven Operational Strategies for On-demand Ride Services
C2SMART Center Viz Lab 6 Metrotech Center, Room 460, BrooklynAbstract: In this work, we develop a novel multi-functional and open-sourced simulation platform for on-demand ride service operations, which can simulate the behaviors and movements of various agents (including drivers
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Webinar: AI Agent 101 and Vibe Coding Demo
This talk introduces the core concepts behind modern AI agents—from (Large Language Models) LLMs and memory to tool integration, reusable agent skills, and autonomous workflows. Participants will gain a clear understanding
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SLH: Introduction to Bayesian Optimization and Its Applications in Transportation and AI
This talk introduces Bayesian Optimization (BO), a sample-efficient framework for optimizing expensive, noisy black-box functions. We will cover the core ideas behind surrogate modeling (e.g., Gaussian processes) and acquisition functions