Research Projects
The Center for Collective Learning is a multidisciplinary research laboratory focused on understanding the growth, accumulation, and value of socially embedded knowledge. For decades, our team has contributed to understanding patterns of economic diversification, growth, and development. We also have new areas of research focused on digital democracy and people’s perception of technology.
Economic Complexity
For decades, economics has relied on the use of statistics as its battle-horse for empirical analysis. Today, machine learning tools, from recommender systems to dimensionality reduction techniques, provide a new way to understand economic development.
Digital Democracy
Can digital technologies augment democracy?
At the Center for Collective Learning we design, test, and develop tools to support civic participation. Our team consists of talented engineers, lawyers, scientists, and designers, experienced in the design, testing, and development of digital tools.
Collective Memory
Collective memory is vital for our species: it shapes our identity, fosters social cohesion, and provides a sense of belonging. It allows us to learn from our past experiences, enabling informed decision-making and can even stops us from repeating past mistakes.
By learning more about the way we cummulate knowledge, we might just get a few steps closer to understanding the world around us.
Global Language Network (2013) is a project that uses the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures.
Global Language Network (paper) was written by Shahar Ronen, Bruno Gonçalves, Kevin Z. Hu, Alessandro Vespignani, Steven Pinker, and César A. Hidalgo.
Pantheon (2013) is an effort to map our species collective memory by structuring data on the biographies of globally famous indivdiuals. Pantheon 1.0 was developed by Amy Yu, in collaboration with Kevin Hu and Shahar Ronen.
City Science
Starting in 2010, our team has been working to understand how people perceive the urban environment and the implication of such perception.
In less than a decade, our contributions went from showing that urban perception could be quantified, to showing the impact of urban perception for urban activity and physical urban change. Today, scholars in various academic communities continue to be inspired by these ideas and methods.
Place Pulse (2011) is a crowdsourcing effort to map urban perception. By asking users to select images from a pair, Place Pulse collects the data needed to evaluate people's perceptions of urban environments. This data is also the data used to train Streetscore.
Place Pulse was developed by Phil Salesses as part of his requirement to complete his master thesis. The present version of Place Pulse was re-engineered by Daniel Smilkov and Deepak Jagdish.
Streetscore (2014) is a computer vision algorithm that estimates people's perception of urban environments. We have used Streetscore to create high resolution maps of urban perception with the goal of studying the social impact of urban perception, and also, to study urban change.
Streetscore was created together with Nikhil Naik. The Streetscore website was created together with Nikhil Naik and Jade Philipoom.
Teams & Organizations
Immersion (2013) The current interface of emails is designed around time, and messages, pushing people to focus on what is more recent rather than important. Immersion is a design experiment that centers the email interface on people and the networks that people form.
Immersion was developed by Daniel Smilkov and Deepak Jagdish as part of their requirement for a Masters in Media, Arts, and Sciences.
OpenTeams (2018) is an open source platform to visualize team data. It is designed for email metadata, and also, includes validated surveys regarding personality (big five) and morals (moral foundation). You can access OpenTeams.
Academic Impact
Rankless (2024) is an interactive platform designed to explore and highlight the unique contributions of thousands of universities worldwide. By moving beyond rankings, the project offers a fresh perspective on how universities influence each geography and topic, emphasizing diverse forms of impact and providing a richer understanding of academic influence.
Data Integration and Visualization
DIVE (2017) is a data integration and visualization engine that allows users to transform data into stories, by facilitating visualizations through recommendations, and providing an statistical tool including multivariate statistics. DIVE was created by Kevin Hu, as part of his Masters and PhD work at the Collective Learning group at the MIT Media Lab.