This talk presents a POPL 2011 paper of the same name by Johg-hoon (David) An, Avik Chaudhuri, Jeffrey S. Foster, and Michael Hicks. The paper can be found at http://www.cs.umd.edu/~avik/projects/distr/. It introduces “constraint-based dynamic type inference, a technique that infers static types based on dynamic program executions.” The authors develop and formalize an algorithm to generate constraints on fields and method arguments as they are used at runtime, which can be solved to infer types for these entities. They are also able to show a soundness result: “if training runs cover all syntactic paths through each method of a class, then the inferred type for that class is sound.” Finally, they discuss their implementation of the algorithm, called Rubydust.