Among the Lab’s current projects:

• The “Electome”—A new form of election analysis (in this case the 2016 presidential election) that comprehensively maps the content and network connections among the campaign’s three core public sphere voices: the Candidates, the Media, and the Public.
• The “Foodome”—Similar to the Electome, a mapping of the public, media and industry conversation about food-related topics using NLP, network analysis and data visualization.
• Social Literacy Learning—Child-driven and machine-guided “social learning” technologies that empower human networks to promote literacy growth.
• Aerial Imaging and Network Analysis—A machine learning pipeline that will discover and predict links between the visible structure of villages and cities (using satellite and aerial imaging) and their inhabiting social networks.
• Human Atlas—Mapping and analysis of the publicly knowable social connections of various communities, allowing us to gain unprecedented insights about the social dynamics in such communities.

Social Machines group students apply through the Media Lab’s graduate program application process, detailed here. Postdocs are also welcome to apply.

Areas of Interest

Image Analysis / Computer Vision—Applicants should have experience with state of the art image analysis / computer vision techniques based on machine learning and applicable to large datasets. We will have a focus on satellite image analysis.

Spoken Language Processing—Applicants should have experience with state of the art spoken language and speech processing techniques including speech recognition, speech synthesis, and design of spoken language and audio interfaces. We will have a focus on Indian languages.

Network Analysis—Applicants should have experience with state of the art network analysis techniques (applied to social networks, and other networks derived from unstructured data) including design and analysis of randomized experiments on networks, and preferably software engineering skills and experience working with large heterogeneous datasets.

Mobile App Development—Applicants should have experience with UI/UX design skills, experience developing apps for mobile devices (especially on Android), experience developing web services, and preferably experience working with large heterogeneous datasets.

Game Design—Applicants should have experience in the design and implementation of games or “game-ification” for behavior change apps. Our focus will be on language and literacy learning.

Data Visualization—Applicants should have experience with state of the art data visualization and interaction techniques, and preferably software engineering skills and experience working with large heterogeneous datasets.

Machine Learning & Pattern Analysis—Applicants should have experience with modern statistical machine learning and pattern analysis techniques (applied to unstructured and structured data), and preferably software engineering skills and experience working with large datasets.