Deep Neural Networks for Choice Analysis
2023-05-19 14:00 - 15:00 (America/New_York) | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Individual choice has been an enduring question across disciplines. Deep neural networks (DNNs) have demonstrated their high predictive power over the classical discrete choice models (DCMs) in many empirical studies. However, DNNs as a new modeling paradigm still present pressing challenges in interpretation, generalization, and robustness. This presentation introduces a deep choice framework that synergizes DNNs and DCMs to model individual travel decision. It demonstrates that the DNNs can provide economic information as complete as classical DCMs, including choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, heterogeneous values of time, among many others, thus partially resolving the interpretation challenge. It introduces how to use the prior behavioral knowledge to design a particular DNN architecture with alternative-specific utility functions, which improves the generalizability of DNNs with a domain-knowledge-based regularization method. It then extends the framework to deep hybrid models, which integrates classical numerical data and the unstructured data (i.e., imagery and graphs) to analyze travel behavior. Overall, this presentation lays out a new foundation of using DNNs to analyze travel demand, enhancing economic interpretation, architectural design, and robustness of deep learning through classical utility theory.

SPEAKER
Shenhao Wang is an assistant professor and the director of the Urban Artificial Intelligence Laboratory at the University of Florida. He is also a research affiliate to Urban Mobility Lab and Media Lab at the Massachusetts Institute of Technology. He seeks to develop fundamental theory for urban science using artificial intelligence. He develops deep choice models, which analyze individual decision-making by integrating discrete choice models and deep learning with applications to urban travel behavioral analysis. He also analyzes collective mobility networks by integrating classical network theory and graph neural networks to quantify risk and uncertainty, thus promoting resilient economic growth. Dr. Wang completed his interdisciplinary Ph.D. in Computer and Urban Science at Massachusetts Institute of Technology in 2020. He received B.A. in Economics from Peking University (2014) and B.A. in architecture and law from Tsinghua University (2011), Master of Science in Transportation, and Master of City Planning from MIT (2017).

ECE Seminar: Boundary Stabilization of Polynomial Reaction Diffusion Equations
2023-09-14 14:30 - 15:30 | 370 Jay Street, Room 1013, 370 Jay Street, Brooklyn
We show how to stabilize a polynomial reaction diffusion system using boundary control, integration by parts, completing the square and an infinite dimensional extension of Al’brekht’s method.
Presented by Alfred J. Krener.

Transportation & Urban Systems Fall 2023 Welcome Event
2023-09-14 17:00 - 18:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
C2SMARTER welcomes new and returning transportation students to the Fall 2023 academic year with a kick-off event. Learn about C2SMARTER, meet the faculty, and see the space. Come learn more about:

Monthly research presentations taught by consortium members and industry partners
Potential research opportunities
Transportation courses offered this semester:

Forecasting Urban Travel Demand
Traffic Operations & Control
Management of Transit Maintenance and Operations
Urban Transportation & Logistics Systems

Student Learning Hub: Introduction to Neural Networks (NNs): How and When to Use Recurrent NNs and Convolution NNs
2023-10-13 11:30 - 12:30 | NY
Instructor: Haggai D. Davis, III, New York University
Hands-on exercise: Yes
Beginner level: No prior experience required.
Schedule: Friday Oct 13th, 11:30am-12:30pm
Description: Machine learning is an incredibly broad term that encompasses a lot of different algorithms which can be applied to solve a variety of problems. Neural networks are one category of ML algorithms which consist of layers of nodes (or neurons) which will activate to transmit information when certain thresholds are met. RNNs and CNNs are subclasses of neural networks whose differing structures allow for the analysis of different types of datasets, usually things like time-series and pattern-recognition, respectively.

Student Learning Hub: Contextualising Cloud-based Geospatial Analytics Techniques
2023-10-20 12:00 - 13:00 | NY
Instructor: Vidisha Chowdhury, University of Pennsylvania & Philippe Schicker, Carnegie Mellon University
Hands-on exercise: Yes
Beginner level: No prior experience required.
Schedule: Friday October 20th, 12-1 pm

Description: Google Earth Engine (GEE) and other cloud-based geospatial analysis platforms can be powerful tools for data scientists and researchers to analyze vast amounts of satellite imagery and geospatial data. In this one-hour introductory course, we are going to provide you with a basic understanding of GEE, contrast it to traditional geospatial techniques, and highlight how GEE can be used for applications such as environmental monitoring, land cover classification, and more.

Student Learning Hub: Solving Large-Scale Optimization Problems: A Practical Guide to High-Performance Computing (HPC) and Using C++ APIs for CPLEX
2023-10-27 12:00 - 13:00 | NY
Instructor: Yuhao Liu, NYU Shanghai
Hands-on exercise: Yes
Beginner level: No prior experience required.
Schedule: Friday Oct 27th, 12-1pm

Description: This course is designed to equip the audience with the basic knowledge and skills necessary to navigate the world of high performance computing (HPC) and leverage IBM’s CPLEX optimization studio for tackling complex optimization challenges. In this course, you will learn 1) how to run computation tasks on HPC clusters; 2) how to solve optimization problems using the C++ API of CPLEX; and 3) how to deploy and run CPLEX programs on HPC clusters for large-scale optimization models and/or parallel optimization algorithms.

Fireside Chat- From Classroom to Career: How Students Can Harness Experiential Learning for Future Success
2023-11-08 16:00 - 17:00 | NY
C2SMARTER’S Dr. Jingqin Gao will moderate a discussion with Prof. Gabriela Gongora-Svartzman from Carnegie Mellon University.
In this insightful fireside talk, we explore the synergy between data-driven careers, industry, and education, emphasizing the transformative power of data literacy in decision-making. Prof. Gongora-Svartzman will provide insights on maximizing the value of academic and experiential opportunities, bridging the gap between education and real-world applications, and offering guidance on post-graduation career transitions. Additionally, Prof. Gongora-Svartzman will delve into the pivotal role of technologists in building public trust through technology in the public sector and promoting tech for social good. She will address the critical importance of ethical data analysis, highlighting the role of data storytelling and visualization in projects. Join us to shape a future where data-driven professionals play a vital role in fostering positive societal impact.
BIO
Prof. Gongora-Svartzman serves as an Assistant Teaching Professor for Heinz College of Information Systems and Public Policy, Carnegie Mellon University, where she teaches various courses, including Exploratory Data Analysis and Visualization with Python, Machine Learning Foundations with Python, and Data Analytics with Tableau. She also holds the role of faculty advisor for capstone projects for various graduate programs. Before joining CMU, she was an Adjunct Instructor at Stevens Institute of Technology’s Business Intelligence and Analytics Program and an Adjunct Associate Faculty at Columbia University’s Applied Analytics Program. Prof. Gongora-Svartzman’s research focuses on Urban Informatics, Data Visualization for Decision Making, and Resilience and Social Perception. Her work uniquely bridges technical service performance with human perceptions, covering areas like disaster preparedness through social media analysis, smart city service quality, social equity movements, transportation services, and decarbonization strategies. She actively contributes to the field as a member of INFORMS, leading sessions on diversity, equity, and inclusion and serving in various leadership roles. Beyond her academic pursuits, she champions women in STEM, diversity, equity, and inclusion in computing and mentors students in data competitions. She has also served as chair of the Early Career Teachers Network (ECTN 2021 and 2022), Secretary for Women in OR/MS (2021), current VP of Communications for Women in OR/MS (2022) and has been a mentor for the Women in OR/MS Mentorship program for 2019-2021.

Student Learning Hub: Essentials of Data Acquisition for Transportation Analysis
2023-11-10 12:00 - 13:00 | NY
Instructor: Vivian Otieno, Texas Southern University
Hands-on exercise: Yes
Beginner level: No prior experience required.
Schedule: Friday November 10th, 12-1pm

Description: Essentials of Data Acquisition for Transportation Analysis involves the collection, processing, and analysis of data about traffic patterns, vehicle movements, and other factors that can be useful in the transportation industry. This course will cover types of data acquisition systems, the technologies used in data acquisition, the advantages and disadvantages of data collection systems, and how the collected data is used in the transportation industry to improve the system and make it better.

Student Learning Hub: Fireside Chat
2024-02-21 15:30 - 16:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
Chris is a transportation planning professional with over 10 years of experience in the industry. He has led both regional and statewide transportation planning efforts, as well as managed several sub-area and corridor studies. In his role with AirSage, Chris advises public agency and consulting clients on how to leverage mobile device and connected vehicle data for more informed decision-making. With a diverse portfolio in developing cutting-edge decision-support tools and data-driven methodologies, he consistently champions innovative approaches to address complex transportation challenges. Chris holds a Bachelor’s degree in Environmental Studies and Economics, complemented by a Master’s in Urban Planning from the University of Kansas.

Light refreshments will be provided! If you cannot attend in person, join via Zoom

SLH: Introduction to database systems and SQL
2024-03-28 12:00 - 13:00 | NY
Instructor: Ziyue Feng, New York University
Hands-on exercise: Yes
Beginner level: No prior experience required.
Schedule: Thursday, March 28th, 12:00 pm -1:00 pm ET
Description: Databases have become indispensable in our daily lives. In this one-hour session, we’ll cover the fundamental concepts of database systems and Structured Query Language (SQL). Additionally, we are going to have a hands-on session with SQL to help you become familiar with data extraction techniques.
RSVP Link: Here

SLH: Building Interactive Maps with JavaScript, HTML, and CSS
2024-04-18 12:00 - 13:00 | NY
Instructor: Hannah Bonestroo, New York University
Hands-on exercise: Yes
Beginner level: No prior experience required. Basic website development knowledge is preferred.
Schedule: Thursday, April 18th, 12:00 pm -1:00 pm ET
Description: In this one-hour course, you will learn how to create a basic interactive online map using JavaScript, HTML, and CSS. These essential web development tools will allow you to showcase your research findings to a wider, non-technical audience. While this course will only cover the very basics, we will discuss how further mastering these tools can open up a world of opportunity for data visualization.
RSVP Link: Here

SLH: Analyzing scope and limitations of using GeoAI Toolbox available in ArcGIS Pro/Online
2024-04-25 12:00 - 13:00 | NY
Instructor: Komal Gulati, North Carolina A&T State University
Hands-on exercise: Yes
Beginner level: Basic GIS and AI knowledge is preferred.
Schedule: Thursday, April 25th, 1:00 pm -2:00 pm ET
Description: Geospatial Artificial Intelligence (GeoAI) offer transformative capabilities for handling and analyzing geospatial data. This course will introduce you to the essentials of GeoAI, including its significance and range of applications, followed by a detailed walkthrough of the ArcGIS Pro environment and the GeoAI Toolbox. You’ll gain hands-on experience in leveraging deep learning for geospatial analysis, from preparing training datasets to executing model inferencing, culminating in a practical exercise aimed at detecting infrastructure vulnerable to landslides in the island of Grenada.
RSVP Link: Here

CUE Minors Info Session
2024-09-17 12:30 - 14:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
The Civil and Urban Engineering Department is hosting a Minor Preview event on Tuesday, September 17th from 1230-2pm. This is a great opportunity for you to learn about the minors offered in our department that are available to all of you, no matter what your major is!
Registration is required, see link below.

Student Learning Hub: Comprehensive Urban Data Collection Using Drones: From Licensing to Advanced Sensor Applications
2024-10-11 13:00 - 14:00 | NY
Instructor: Tu Lan & Vivaldi Rinaldi, New York University
Beginner level: No prior experience required.
Schedule: Friday, October 11, 1:00 pm – 2:00 pm ET
Description: Drones are becoming an invaluable tool for urban data collection, offering a wide range of applications from traffic monitoring to environmental analysis. This course introduces participants to the full scope of using drones for traffic data collection, starting with the necessary FAA licensing and airspace regulations, followed by practical guidance on drone operation and control. We’ll explore the different sensors and gimbals that can be equipped on drones, such as cameras, thermal imaging, and LiDAR, and their respective applications in collecting urban data. This session provides a comprehensive understanding of how drones can be utilized for diverse data-driven urban analyses.

Student Learning Hub: Where Are All the Robots, and Why Aren't There More of Them?
2024-10-23 13:00 - 14:00 | NY
Instructor: Irving Fang, New York University
Beginner level: No prior experience required.
Schedule: Wednesday, Oct 23th, 2024 1:00 PM – 2:00 PM
Description: In the talk, we will discuss some of the latest progress in robotic applications over the last few years and the technology behind them. We will also discuss the current obstacles to an even wider spread of robots and the technical details involved.

TransportationCamp NYC 2024
2024-10-26 09:30 - 17:00 | 370 Jay Street
TransportationCamp NYC will be held on Saturday, October 26th at NYU’s Tandon School of Engineering in Downtown Brooklyn. Join us to discuss all things transportation and connect with professionals, students, and enthusiasts alike!
TransportationCamp is an unconference – every session is planned, proposed, and led by attendees like you. It’s the perfect opportunity to share your transportation passion with a diverse gathering of individuals from our multidisciplinary industry.
TransportationCamp welcomes all attendees, whether you’re a planner, software developer, engineer, student, transportation professional, community member, policymaker, or just excited about mobility!

STEM Day 2024!
2024-11-08 10:00 - 15:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201

Student Learning Hub: Online Optimization Meets Urban Transportation
2024-11-08 13:00 - 14:00 | NY
Instructor: Tao Li, New York University
Beginner level: No prior experience required. Basic optimization knowledge would be helpful but not required.
Description: Urban transportation networks are complex and dynamic, and as a result, offline planning alone may not ensure effective real-time management. Therefore, this course introduces online optimization methods with a focus on transportation applications. We’ll start with gradient descent in conventional convex optimization, move to online gradient descent, and briefly explore multi-agent online learning and associated equilibrium convergence. We’ll also discuss challenges in deploying online optimization in urban transportation.

Student Learning Hub: Causal Inference – Difference in Differences Regression and Its Application in Transportation Research
2024-11-15 13:00 - 14:00
Instructor: Yanchao Li, New York University
Beginner level: No prior experience required.
Schedule: Friday, Nov 15th, 2024 1:00 PM – 2:00 PM
Description: In this course, we will discuss the Difference-in-Differences (DiD) method, a causal inference tool widely used across various research fields to estimate the impact of policy changes or other interventions while controlling for confounding factors. You will learn about its application in transportation research, with a recent study evaluating the effectiveness of congestion policies as an example.

The Story of the BQE
2024-12-05 18:00 - 20:00 | El Puente, 211 S 4th Street, Brooklyn, NY, 11211, United States
You are cordially invited to El Puente for a gathering to listen together to oral histories from elders and youth in communities across Brooklyn and Queens. Oral history facilitators Mitra Bonshahi and Adriana Gallardo will present audio excerpts from conversations between friends, neighbors, and family members about the impact of the BQE on their lives. Participants will be invited to share their experiences and hopes for the future of their neighborhoods. Space is limited and registration is required. This event is jointly organized by El Puente and the Institute for Public Architecture.

Ikos x C2SMARTER: The Future of Railways
2024-12-11 17:00 - 20:00 | Kimmel Center, Room 914, 60 Washington Square South, New York, NY, 10012, United States
This engaging event will take place on December 11th at 5pm, concluding with a cocktail reception, where you can connect with industry leaders and peers. During the conference, we’ll explore the innovations and challenges shaping the future of the railway sector.
Join top experts as they share insights on sustainable practices, emerging technologies, and what lies ahead for the industry. Seats are limited, save yours today to be part of these forward-thinking discussions and exchange ideas with top professionals in the field.

SLH: Understanding Travel Demand through Passively-generated Mobile Data: a Python-based Mobility Analysis Workshop
2025-03-07 13:00 - 14:00 | NY
The ubiquity of GPS-equipped mobile devices has enabled the collection of human mobility data with high spatiotemporal granularity. Indeed, there now exists an ecosystem of both data providers and consulting agencies centered around collecting, processing, and extracting insights from this type of data. Though much of the raw data cannot be made public due to privacy-preserving agreements, the academic community can still access geographically aggregated data. This workshop will cover the (Python-based) preprocessing and wrangling of aggregated mobility data in New York state provided by SafeGraph. It will highlight concepts including (but not limited to) origin-destination matrices, the four-step travel demand model, self-selection bias in passively-generated data, and demand prediction. It will also describe how to fuse such data with the American Community Survey (ACS) providing sociodemographic information at the census block group-level. Relevant packages that will be leveraged and explained include GeoPandas and scikit-Mobility. Intermediate familiarity with Python, data structures, and object-oriented programming is recommended.

2025 Women in Transportation Panel & Networking
2025-03-12 18:00 - 19:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
This year’s panelists are Dr. Semiha Ergan, Professor at NYU; Geline Canayon, Senior Project Manager at Aimsun & Vice President of ITE MET Section; and Alia Kasem, Senior Data Scientist at NYC DOT & Adjunct Lecturer at Hunter College. The discussion will be moderated by NYU PhD Candidate Zerun Liu.

Light refreshments will be provided! Register for the Zoom here.

SLH: An Introduction to Multi-agent Driving Simulation with GPUDrive
2025-03-14 12:00 - 13:00 | NY
In this seminar, I will introduce GPUDrive, a high-performance, data-driven driving simulator that operates at 1 million FPS. GPUDrive is built on the Madrona Game Engine and uses GPU acceleration to enable scalable multi-agent simulation. I will discuss how this simulator can be used to train reinforcement learning agents efficiently, drawing from a recent paper where we demonstrate its application to the Waymo Open Motion Dataset. Additionally, I will walk through tutorials on how to set up and use the simulator.
The repository can be found at https://github.com/Emerge-Lab/gpudrive/tree/main

SLH: Discrete Choice Modeling for Travel Behavior Analysis: From Multinomial Logit to More Advanced Forms
2025-04-11 12:00 - 13:00 | NY
Abstract: In this course, we will discuss the decision theory of random utility maximization and discrete choice models (DCMs) including multinomial logit (MNL), nested logit (NL), mixed logit (MXL), and agent-based mixed logit (AMXL). You will learn about their applications in travel behavior analysis (e.g., travel mode choice, activity scheduling choice, etc.) and how to build DCMs with long-shape and wide-shape choice datasets in R and Python. A recent study on New York State travel mode choice will be introduced as an example.

Fireside Chat with David Hammer, co-founder of Popwheels
2025-04-23 14:00 - 15:00 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201
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!

Summer Programs Welcome Lunch!
2025-07-01 12:30 - 13:30 | C2SMART Center Viz Lab, 6 Metrotech Center, Room 460, Brooklyn, 11201

Seminar: Improving the Quality of Urban Public Transport: Lessons Learned from a Novel Use of GPS Data
2025-07-08 13:00 - 14:00 | NY
In Italy, public transport is typically operated by private companies under service contracts including penalties for non-compliance. Therefore, detecting anomalous traffic days is key to fairly assigning delay responsibility for buses operating in mixed traffic: operators should not be penalized on days of exceptional traffic conditions. To address this, harmonic average speeds of vehicles are used to cluster hours of the day exhibiting similar traffic patterns. For any of such traffic states, a Generalized Additive Model is fit to the available speed percentiles in order to detect anomalous days. A case study relying on GPS data is presented for the city of Trieste.

Presented by Andrea Mecchina, PhD candidate, University of Trieste, Italy

New York Reinforcement Learning Workshop (NYRL)
2025-09-12 | NY
Join leading researchers from academia and industry for the inaugural NYRL Workshop. Connect, collaborate, and explore the latest advances in reinforcement learning with the vibrant New York metropolitan RL community.
Co-organized by Amazon, Columbia Business School (CBS), and NYU Tandon School of Engineering.

2025 Sustainable Cities Summit: Day 2
2025-09-25 08:00 - 18:00 | Kimmel Center, Room 914, 60 Washington Square South, New York, NY, 10012, United States
We are proud to support the second annual Sustainable Cities Summit in partnership with Climate Group and NYC Climate Week. This event brings together various experts from multiple organizations to discuss climate-forward cities and mainstream climate action for a sustainable future.
Discover how design, data, and collaboration are reshaping New York’s future—and inspiring cities around the world. As part of #ClimateWeekNYC, Sidara companies are joining forces with NYU to host a full-day summit focused on implementable solutions for healthier, more sustainable urban living. The event feature leading experts from TYLin, Introba, Perkins & Will, and Currie & Brown, alongside thought leaders from NYU, MIT, C40 Cities, the City of New York, and the Los Angeles Cleantech Incubator.
