The LIDS Seminar Series features distinguished speakers in the information and decision sciences who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing.
Speaker: Matthew Salganik (Princeton University) The digital age has transformed the ways that researchers are able to study social behavior. These new opportunities mean that the future of social research will involve combining approaches from social scientists and data scientists, a hybrid that is often called computational social science. After providing some perspective on this…
Speaker: Andrej Risteski (Princeton University) A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the “best fit” model parameters from…
Speaker: Daron Acemoglu (MIT) Many online platforms present summaries of reviews by previous users. Even though such reviews could be useful, previous users leaving reviews are typically a selected sample of those who have purchased the good in question, and may consequently have a biased assessment. In this paper, we construct a simple model of…
Technology-enhanced project-based pedagogy for the promotion of scientific thinking
The advent of Big Data is stimulating the development of new networking architectures which facilitate the acquisition, transmission, storage, and computation of data. In particular, Named Data Networking (NDN) is an emerging content-centric networking architecture which focuses on enabling end users to obtain the data they want, rather than to communicate with specific nodes. By…
How Technology Design will Dictate Our Civic Future