I School Lectures

Nina Beguš will introduce “artificial humanities,” an approach that integrates the study of humanities into the creation of technologies

How the definition, production, and leveraging of information are shaped by caste, class, and gender, and the implications for development.

Behind the census’s neat grid of numbers is a collage of messy, human stories — you just have to know how to read them.

Bryan Pardo discusses his lab’s work bridging the gap between digital audio software interfaces and the musicians, podcasters, and sound artists who use the tools.

Dr. Doris Lee presents her dissertation research enabling data analysts to identify trends and patterns, generate and verify hypotheses, and detect outliers and anomalies.

Richmond Wong presents his dissertation researchon the relationships between design practices and social values.

Cash transfers to people in extreme poverty have become a policy tool of choice; should policy-makers go all the way to Universal Basic Income?

Can widespread misperceptions about others’ beliefs sustain social norms? How do social norms change when new information becomes available?

How team identity boosts worker engagement in a gig economy.

New research shows that “naming and shaming” can be counterproductive and even dangerous.

Peter Schwartz is one of the world’s most experienced, insightful, and entertaining analysts of the digital revolution.

Gracen Brilmyer, a 2016 CTSP fellow and MIMS 2016 alum, presents research for their doctoral dissertation.

Requiring biometric authentication excluded and inconenienced legitimate beneficiaries of a social protection program.

What people think machines can know about the mind, and why their beliefs matter

Distraction versus learning with mobile devices in the classroom and student success

As AI and ML shape technology, how do we ensure that products are designed by and for people?

Dr. Andrew L. Brooks is the Chief Data Scientist at the National Geospatial-Intelligence Agency

The One Laptop per Child project failed. So why do the same utopian visions that inspired it still motivate other projects to “disrupt” education and development?

How good data models facilitate optimization and generalization in machine learning.