Search

Title

Trustworthy Machine Learning

Algorithm designers often introduce random choices into their algorithms in an effort to improve efficiency. However, random bits cannot necessarily be produced for free, so deterministic algorithms are preferable to randomized algorithms, all else being equal. Is randomness ever truly necessary for efficient computation?
What, ultimately, is the role of randomness in computing?

Sources of big data are now near-universal, e.g., across social media, financial markets, Internet of things, or scientific data. Thus understanding how to effectively and efficiently summarize data is a crucial step toward both understanding past events as well as predicting

In this talk, I will present an algorithmic foundation of parallel paging and an overview of our recent results on performing general linear stencil computations significantly faster than state-of-the-art algorithms. Classical problems such as paging have been very well understood in the sequential setting for decades. However, the paging problem has remained wide open for more than two decades in the parallel setting. In the parallel paging problem, p processors share a cache (small, fast memory) of size k.

Machine Learning (ML) techniques have been increasing adopted for Software Engineering (SE) tasks, such as code completion and code summarization. However, existing ML models provide limited value for SE tasks, because these models do not take into account the key characteristics of software: software is executable and software constantly evolves. In this talk, I will present my insights and work on developing execution-guided and evolutionaware ML models for several SE tasks targeting important domains, including software testing, verification, and maintenance.