## SSS Abstracts |

In this talk, I will introduce convex relaxation hierarchies and explain how to understand their computational power. Using several fundamental optimization problems like Max-Cut and Sparsest-Cut as examples, I will illustrate: 1) how to design approximation algorithms via hierarchies, 2) how certain problems are resistant to hierarchies, and their significance, and 3) a surprising connection between hierarchies and the theory of algebraic proof complexity.

In the end, I will propose directions to further explore the power of convex relaxation hierarchies, which hopefully will lead to answer the most important question in approximation algorithms research -- whether the simplest semidefinite programming relaxation is optimal for many optimization problems.

Motivated by social networking sites, I have created a new logic for logic programming which addresses several usability issues in a typical linear logic. The extensions include temporality (which can be used to handle global information), name generation and list comprehension. I also showed that there is a sound and complete compilation from this logic to a known linear logic. This talk will primarily focus on the definition and the usage of the new logic.

Presented in Partial Fulfillment of the CSD Speaking Skills Requirement

In this talk I will present a new form of separation logic which provides a particularly simple and elegant treatment of both concurrency and aliasing by changing how we look at the heap. I move away from the traditional separation logic approach of talking about the precise contents of heap locations, and instead talk about predicates that those contents must satisfy. This shift in viewpoint is realized by a new form of assertion called a heap predicate. Heap predicates enable local reasoning about aliasing and concurrency, including concurrent programs with data races. As an example, I will demonstrate how locks can be programmed using more primitive operations, and specified using heap predicates.

Presented in Partial Fulfillment of the CSD Speaking Skills Requirement

Joint Work with Karl Crary and Ligia Nistor

In this work, we present a novel method that uses prior biological knowledge to boost the statistical power of detecting genetic variants associated with traits. Specifically, we use biological knowledge about groups of correlated genetic variants (e.g. genetic variants in linkage disequilibrium) and groups of correlated traits (e.g. co-expressed genes). Given the grouping information, we assume that a group of correlated traits may be affected by the common genetic variants, or a group of genetic variants may affect the common traits. Under such assumptions, we incorporate the biological knowledge into a sparse regression model using L1/L2 penalties. We illustrate our approach with examples, and show how prior biological knowledge helps increase the power to detect associations between genetic variants and traits.

This is joint work with Eric Xing.

This will be presented in Partial Fulfillment of the CSD Speaking Skills Requirement.

In this work, I identify countries that extensively host cyber-criminal infrastructure, and reasons why these countries are attractive for hosting such infrastructure. In my analysis, I use Symantec WINE data which consist of attack reports from more than 10 million Symantec customer computers worldwide. I also present a methodology for assessing countries' cyberwarfare and bioweapon capabilities. My methodology assesses both countries' motivations and latent abilities. In my assessment, I use socio-political data, and measures of countries' cyber and bio expertise and infrastructure.

Joint work with Robert Harper

In partial fulfillment of the Speaking Requirement

In this talk, we will explore partitioning and update scheduling of variables in distributed machine learning algorithms, in order to improve their memory efficiency and speed up convergence without compromising correctness. Specifically, I will describe how partitioning and scheduling strategies can scale up machine learning models including Lasso, Matrix Factorization, and Latent Dirichlet Allocation with many variables. The experiments will demonstrate the speed and scalability of our approach in comparison to other parallel machine learning methods.

Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.