Research Practices and Reproducible Research Harvard Catalyst Biostatistics Seminar The way research is selected for funding, designed, conducted, analyzed, and published can have a substantial impact on the reproducibility of scientific results. Empirical evidence suggests that the efficiency of many currently applied research practices is suboptimal, and there is wide variability across different scientific fields in this regard. This leads to a high prevalence of biased results. Dr. Ioannidis will peruse the current landscape and discuss different possibilities that have been proposed on... Read more about HCatalyst: John Ioannidis

Data Science Panel RSVP requested.

Colorful logN-logS

Regression-Robust Designs of Controlled Experiments

Statistical Analysis of Ordinal Data: Applications to Social Network Analysis and Causal Inference

Accelerating Exact MCMC with Subsets of Data

Gaussian Process Regression with Noisy Inputs

Health Care Analytics

ORC IAP Seminar 2015 Social Networks in Operations Research Social networks have grown in scale and popularity in recent years, with several social networks boasting more than one billion registered users. The MIT Operations Research Center has organized a day-long seminar on the role of social networks in operations research, exploring how data from these networks can be used to make decisions or to understand social phenomena. Three speakers, including those from both industry and academic settings, will present their work and experience on topics ranging from marketing to studying social... Read more about MIT ORC: Social Networks

Explore Careers in Big Data and Analytics (Registration is required)

Summarizing Large Data Sets

Bayesian approaches to the detection and analysis of unmodeled gravitational wave signals

How large is the norm of a random matrix?

Giridhar Gopalan Quantifying the Influence of the Prior and Likelihood in Bayesian Inference Edward Kao Causal Inference Under Network Interference: Model-Based Estimation of Peer Influence Effects

On the Role of Common Information In Decision-Making Under Uncertainty