Admissions for 2016

The Laboratory for Social Machines is seeking graduate students who will join a cross-disciplinary team with the goal of inventing tools and technologies designed to dramatically change how society, news organizations, and government interact. We hope to enable human-machine solutions to impact gender equality and literacy with field experiments in India and USA. The new lab will build on the work of Roy’s previous research into foundations of language and semantics, but will push off in significantly new directions that incorporate social network and media analysis. Areas of interest:
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.