Hi, I'm Dan Calacci

I'm a PhD student and research assistant at the Human Dynamics group at the MIT Media Lab.

Right now, I'm working on using location data from mobile phones to measure segregation & inequality in cities. I'm also thinking a lot about the ethics and use of location data and surveillance technology.

I'm also the cofounder of a startup, Riff Learning, where we measure interaction patterns to help people learn to be more attentive and effective collaborators & team members.

We give concrete examples of how location data collected from mobile phones is used in the marketplace and could be used for public interest work. We offer a critique of existing privacy & risk literature that characterizes 'utility' and 'risk' as just attributes of data, rather than use. We then explore a bit about how location data collected from mobile phones by Location Based-Service providers is quite different from historical location data sources.

Dan Calacci, Alex Berke, Kent Larson, Alex (Sandy) Pentland

Oxford/LSE Connected Life 2019,lkData and Disorder

We used lessons from collective intelligence of human groups to inform the design of evolutionary optimization algorithms for deep reinforcement learning tasks. Human-inspired, sparse network topologies provide a multiplicative effect on learning speed and performance in a benchmark reinforcement learning task over the state of the art.

Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Yan Leng, Peter Krafft, Esteban Moro, and Alex (Sandy) Pentland

NIPS, Fall 2017, poster

Using high-resolution location data collected from mobile phones, we develop a novel measure of social activity-space segregation in urban space. We find that different categories of places exhibit different segregation patterns, and that exposure to people of different income and race is mediated by the median income of a users' home census tract.

Dan Calacci, Esteban Moro, Xiaowen Dong, and Alex (Sandy) Pentland

CCS 2017, talk

We developed an open-source set of tools that are capable of augmenting and measuring face to face communication between people at scale.

Dan Calacci, Oren Lederman, David Shrier, and Alex (Sandy) Pentland

SBP-BRiMS 2016, poster

We developed an open-source set of tools that are capable of augmenting and measuring face to face communication between people at scale.

Dan Calacci, Oren Lederman, David Shrier, and Alex (Sandy) Pentland

SBP-BRiMS 2016, poster

Using simple unsupervised machine learning techniques to discover the dynamics of political "framing" between parties and congresspeople. We found that Republicans tend to have higher party discipline, that they tend to talk more about economy and budget, and that Democrats have a more varied set of common vocabulary.

Oren Tsur, Dan Calacci and David Lazer

ACL 2015, talk

Using topic modeling and autoregressive distributed lag models to make sense of the public statements released by congresspeople.

Oren Tsur, Dan Calacci, and David Lazer

Computational Journalism @ Columbia, 2014, poster

Using sentiment analysis to understand the networked naming relations between actors accused of being communists during the McCarthy era.

Dan Calacci, Oren Tsur, and David Lazer

ACL 2014, poster

Segregation is more than just where you live.

Built With: Node, Carto, React

Spring 2019

Submision for Hack MIT 2014, won "Best use of Jawbone UP API". Turn data you generate daily into an experience you can consume. Life as Music is a generative algorithm that creates original music from your twitter and jawbone UP fitness data.

Built With: Flask, Overtone

Fall 2015

This was created as part of a research project to map the similarities between congresspeople's language. Each node is a congressperson, and a link between congresspeople indicates that they said that same phrase within 3 weeks of one another.

Built With: d3

Fall 2014

An interactive visualization of Boston's Hubway bike sharing system. Each node is a hubway station, and there's an edge between two stations if there were more than 50 trips between them in the dataset. Colors represent communities detected by the Louvain community detection algorithm.

Built With: d3 and flask

Summer 2014

I trained a simple n-gram model on the public statements of all congresspeople from 2014, with some fun results.

Built With: Flask

Spring 2014

Most Innovative at Hack Beanpot 2014. Adventur uses an n-gram based geotrace model to predict your future movements and quantify how predictable you are. It then uses this information to help you become more adventurous by recommending new places to visit that will optimize your "adventurousness" score.

Built With: Angular, Flask, Mongo

Spring 2014

A way to quickly and easily log meals. Built for a class final project, the design of this app was developed through paper prototyping and extensive user testing.

Built With: Android and a key-value store

Winter 2012