Understanding accuracy decay in online image retrieval systems within the context of open-set classification and unsupervised clustering

Image retrieval systems are extremely useful to political scientists and human rights advocates attempting to understand the scope and spread of disinformation in massive datasets. However, in standard image retrieval tasks the corpus of images is unchanging as time moves forward. When considering online disinformation this is clearly not the case. Image retrieval in an online system can essentially be modeled as an open-set problem, where there is no guarantee that the classes of images seen before will have any correspondence to the classes of images seen at present or in the future.

Advanced proactive caching for heterogeneous storage systems

This project targets improving the performance of distributed storage systems, such as Ceph and NooBaa, by developing novel caching frameworks that (1) take into account request heterogeneity, and (2) perform proactive caching decisions (a.k.a., speculative...

Automated detection of memory safety vulnerabilities in Rust

In comparison to C, the Rust language provides significant memory safety guarantees through its concept of lifetimes and its borrow-checker. However, Rust has an “unsafe” keyword which suspends some of the compiler’s safety checks within a specified code...

Tuning the Linux kernel

The Linux kernel is a complicated piece of software with multiple components interacting with each other in complex ways. The performance and resource consumption of a particular application depends, among many things, on configuration/tuning parameters and policies...