International Summer Learning | NYU Tandon School of Engineering

International Summer Learning

CUSP partners with academic institutions worldwide to host summer programs on urban science, and to sponsor its M.S. and Ph.D. track students for participation in partner programs.

 

 


A group photo of 27 students and faculty, each holding a "certificate of summer school completion" document.

2025 Summer School in Maldonado, Uruguay: Overview

The International Summer School: Urban Data Science will take place from Wednesday, August 13 – Friday, August 29, 2025, in Maldonado, Uruguay. The program will explore the potential of data science to enhance the quality of urban life and create more sustainable, resilient, and equitable global cities. 

Organized by a global partnership of six universities – NYU Tandon School of Engineering's Center for Urban Science + Progress along with the Externado University of Colombia, National and Kapodistrian University of Athens, the Technological University of Uruguay, the University of Lisbon, and the University of Tuscia – the International Summer School aims to bring together students from around the world to collaborate in-person at the Technological University of Uruguay and contribute to a global dialogue about the role of data in urban governance. 

The International Summer School: Urban Data Science in Maldonado, Uruguay will take place from Wednesday, August 13 – Friday, August 29, 2025. More information about the faculty and courses will be announced soon.


The International Summer School: Urban Data Science will be hosted by the Technological University of Uruguay and take place in Maldonado, located in southeastern Uruguay. 



2025 Summer School in Norcia, Italy: Overview

Summer School DataSLO will take place from Monday, June 23 – Friday, June 27, 2025, in Norcia, Italy. The program will explore key topics related to the core methodologies that drive data science, machine learning, and computational optimization – the three foundational pillars of modern artificial intelligence. It will include lectures by internationally recognized researchers, providing a balanced mix of theoretical insights and practical applications. A roundtable discussion open to the audience will also take place on the final day.

Beyond the technical program, Summer School DataSLO offers opportunities to: 

  • Networking with faculty and students;
  • Improve general research skills; and
  • Enjoying a nice stay in Norcia! 

The Scientific Coordinator organizing the Summer School DataSLO is Valentino Santucci, affiliated with the Perugia Stranieri University, Italy.

Visit the DATASLO page on the Perugia Stranieri University website for more information.

Summer School DataSLO will take place from Monday, June 23 – Friday, June 27, 2025, with an excursion planned for Saturday, June 28. Five 5-hour courses held over five days, scheduled in the mornings and early afternoons. More information about the program will be announced soon.


Metaheuristics alongside Neural Models for Combinatorial Optimization
Josu Ceberio, University of the Basque Country, Spain

  • Metaheuristic algorithms have set the standard in the field of combinatorial optimization for decades. However, in recent years, due to the deep learning revolution, a large number of works have attempted to address combinatorial problems. In fact, one of the most promising lines in the research of new algorithms lies in proposing algorithms that combine ideas from both worlds.
    In this course, we will begin with the fundamentals of combinatorial optimization and a review of the most recognized metaheuristic algorithms, to delve into the most current neural proposals.
     

Heuristics for Continuous Optimisation – Design, Replicability & Behaviour
Fabio Caraffini, Swansea University, UK

  • Optimisation tools are essential in modern computing, with heuristic algorithms widely used for their adaptability and reliance on fitness feedback. However, the complex dynamics of these algorithms remain largely unexplored, partly because studies often emphasise only the final outcomes, neglecting valuable data produced throughout their execution.
    The recent proliferation of hard-to-replicate, metaphor-based algorithms adds further complexity. This short course will cover foundational heuristics for continuous optimisation, examine modular frameworks for testing various configurations, and highlight the importance of principles enabling thorough analysis of algorithmic processes and dynamics.
    We will also address methods for enhancing results replicability and for benchmarking algorithmic behaviours, including the identification of structural biases.

Mastering the Data Science Pipeline: from Acquisition to Insights
Paolo Mengoni, Hong Kong Baptist University, Hong Kong

  • This course offers a comprehensive overview of the data science pipeline, emphasizing data acquisition, cleaning, transformation, and knowledge discovery through data mining tools.
    Participants will engage in hands-on learning, applying theoretical concepts to practical case studies using Python programming. Through collaborative projects and real-world datasets, students will develop the ability to extract meaningful insights and enhance their problem-solving skills.
    This short course will equip the students with the foundational knowledge necessary to tackle more complex data science challenges in future studies.

AnyToVec – Learning Vector Representations
Alfredo Milani, Link Campus University, Italy

  • This lecture explores innovative approaches to embedding complex relationships into dense vector representations, a foundational component in current machine learning and data analysis, with data-driven application across varied domains.
    From the Word2Vec, which transforms words into semantic vectors based on context, and gave recent rise to Large Language Models systems, we will explore methods like Node2Vec, Graph2Vec, and other similar frameworks that map the relationships found in various data forms, and how these models support sophisticated data interpretations across linguistic, biological, social, and other structured domains.
    By embedding diverse data points into a common vector space, these techniques facilitate applications from recommendation systems to predictive modeling, where learning the "meaning" or "relationship" of data points within a network or sequence is crucial.

Causal Discovery from Time Series
Maurizio Porfiri, New York University, USA

  • Discovering cause-and-effect relationships between coupled dynamical systems from time-series is a critical problem in data and network science. In this short course, students will learn how to tackle this problem through the information-theoretic concept of transfer entropy.
    The first half of the short course will include a primer on information theory, statistical analysis, data pre-processing, and hypothesis-testing. Theoretical insights will be put in practice in the second half of the course, when students will create their own computer code for causal discovery and apply it to the study of real datasets.

The DataSLO Summer School will be held in the beautiful historic town of Norcia, located in the Umbria region of central Italy. An excursion will be held on Saturday to explore the stunning landscapes and rich heritage of Norcia.



Application Process

Please use this Google Form to apply for the 2025 Summer School in Norcia, Italy, and/or the 2025 Summer School in Maldonado, Uruguay.

Students enrolled in CUSP’s M.S. in Applied Urban Science and Informatics program or the Urban Science Doctoral Track are eligible to apply. Following an internal application process, CUSP will select a number of graduate students to participate in the Summer School DataSLO in Norcia, Italy and in the 2025 Summer School in Maldonado, Uruguay. The airfare will be covered by CUSP, while accommodation will be provided by the organizers. There is no fee to participate.


Previous Summer Learning Opportunities

From June 16 – July 6, 2024, the Center for Urban Science + Progress at NYU Tandon hosted an international summer school in partnership with the National and Kapodistrian University of Athens, the Technological University of Uruguay, the University of Lisbon, and the University of Tuscia. The program aimed to bring together students from around the world to collaborate in person at NYU Tandon and contribute to a global dialogue about the role of data in urban governance. Students were trained in Time Series Analysis, Complex Systems, Data Science, Machine Learning, and Satellite Image Analysis in relation to urban topics. The program was open to students pursuing a degree at NYU Tandon School of Engineering, the National and Kapodistrian University of Athens, the Technological University of Uruguay, the University of Lisbon, or the University of Tuscia.

Discover more about the program in an article on Tandon News.

Faculty 

  • Maurizio Porfiri, Institute Professor of Mechanical and Aerospace Engineering and Biomedical Engineering; Director of CUSP, NYU Tandon
  • Constantinos Cartalis, Professor of Environmental and Climate Physics at National and Kapodistrian University of Athens; Member, European Scientific Advisory Board on Climate Change
  • Juan Marrero, Director, Center for Digital Transformation, Technological University of Uruguay
  • Paulo Ferrão, Distinguished Professor of Instituto Superior Técnico, University of Lisbon; Member, Mission Board on Climate Neutral and Smart Cities; President, IN+ Center for Innovation, Technology and Policy Research
  • Patricia Baptista, Assistant Professor of Instituto Superior Técnico, University of Lisbon; Senior Researcher, IN+ Center for Innovation, Technology and Policy Research
  • Salvatore Grimaldi, Professor of Hydrology, University of Tuscia; President, European Cooperation in Science and Technology

Curriculum

Week 1: Urban Hydrology

Students explored rainfall models and simulations, focusing on urban hydrology and extreme events. The curriculum covers hydrological time series characterization, rainfall phenomena observations, and the application of various models at different time scales, including linear parametric models and disaggregation multiplicative cascade methods. Emphasis was placed on the definition of extreme events, utilizing common instrumentation and innovative techniques for rainfall observations. Statistical notions and hands-on experience with R code were provided for monthly scale rainfall simulation using linear parametric models. Students also explored the importance of sub-hourly rainfall simulation, highlighting the necessity for a robust and user-friendly approach, incorporating COSMOS and the multifractal disaggregation method. Overall, the module equipped students with a comprehensive understanding of urban hydrology and practical skills in rainfall modeling.

Week 2: Urban Metabolism

Sustainable urban metabolism refers to the efficient and balanced management of resources, energy, and waste, within urban environments to promote long-term ecological viability, social well-being, and economic prosperity. It involves understanding, analyzing, and optimizing the flows of materials and energy in cities to minimize negative environmental impacts and enhance resilience. This workshop enabled students to understand the intricate dynamics of urban metabolism, providing valuable insights into resource flow pathways through the economy, its environmental impacts, and sustainable development strategies essential for fostering resilient and thriving cities. Starting from the application of urban metabolism concepts to nations, followed by a narrowing down to cities, two types of spatial resolutions were considered (aggregate versus neighborhood scale) to evaluate alternative strategies toward climate-neutral cities.

Week 3: Urban Thermal Environment

Urban areas exhibit higher temperatures than the surrounding areas because of their positive thermal balance. Global climate change is synergistically affecting urban temperatures, increasing the magnitude of overheating. About 13,000 cities are exhibiting overheating problems, measured as up to 10.0 °C, and more than 1.7 billion people live under severe overheating conditions. Projections about future urban climatic conditions have shown that minimum and maximum temperatures in urban areas may increase substantially due to changes in urban form, urban functions, and urban fabric on the one hand and climate change impacts on the other (for instance, heat waves are expected to increase in frequency, duration, and intensity). Urban overheating increases air pollution (ground ozone concentration) as well as the cooling energy consumption of buildings and the peak electricity demand, resulting also in higher emissions of greenhouse gasses; on the other hand, it decreases human productivity and leads to surges in heat-related mortality and morbidity while also intensifying mental health problems. 

This module enabled students to understand the main mechanisms that control the urban thermal environment; understand how urban form, urban functions, and urban fabric influence urban heat; realize the synergistic impact of climate change on the state of the urban thermal environment; assess urban heat at varying temporal and spatial scales; produce composite urban heat-related indicators (for instance the urban heat risk); and learn and explore urban heat mitigation measures. Ground and satellite data were used, combined with machine learning for big data analysis.