Futures are an attractive way to structure parallel computations. When a thread creates an expression with a keyword future, a new thread is spawned to …
A natural language parser recovers the latent grammatical structures of sentences. In many natural language processing (NLP) applications, parsing is applied to sentences first and …
In today's world data is ubiquitous. Increasingly large and complex datasets are gathered across many domains. Data analysis - making sense of all this data …
Automated program repair (APR) aims to save people time and effort by repairing a faulty program automatically. A significant branch of current APR techniques are …
Today’s tools to improve the reliability and manageability of networks can be generally classified into two different classes: before-the-fact network verification and after-the-fact network troubleshooting. …
Robotic perception is a key step in any autonomous robotic task including manipulation, localization and planning. The more precise the perception system is, the more …
Decentralized finance (or DeFi) has become a booming area of applied distributed computing. Over the course of the last several years, many mechanisms and distributed …
In the past, streaming data management systems (SDMSs) have eschewed transactional management of shared mutable state in favor of low-latency processing of high-velocity data. Streaming …
Cancer is an evolutionary process where cells acquire somatic mutations over time. As a result of this process, tumors are often highly heterogeneous — containing …
The current dominant way to use neural networks for language applications is to pretrain a language model (LM): Given a large corpus, randomly mask out …
Neural networks continually surprise us. Many of these surprises are cases of impressive generalization. Others, however, are embarrassing failures. For example, large language models can …
In recent years, the rising popularity and availability of Virtual Reality (VR) systems has enabled the use of immersive display systems for scientific data exploration. …
Extracting and inferring valuable knowledge from raw data requires training in programming and statistics, and more importantly, domain expertise. Yet domain experts, the people who …