Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …

Efforts to make machine learning more widely accessible have led to a rapid increase in
Auto-ML tools that aim to automate the process of training and deploying machine learning …

Many organizations are shifting to a data management paradigm called the" Lakehouse,"
which implements the functionality of structured data warehouses on top of unstructured …

With the increasing commoditization of computer vision, speech recognition and machine
translation systems and the widespread deployment of learning-based back-end …

Reinforcement learning (RL) algorithms involve the deep nesting of distinct components,
where each component typically exhibits opportunities for distributed computation. Current …

The query engines of most modern database systems are either based on vectorization or
data-centric code generation. These two state-of-the-art query processing paradigms are …

Although compiling queries to efficient machine code has become a common approach for
query execution, a number of newly created database system projects still refrain from using …

The Internet of Things (IoT) presents a novel computing architecture for data management: a
distributed, highly dynamic, and heterogeneous environment of massive scale. Applications …

Recurrent neural networks (RNNs) are an important class of deep learning (DL) models.
Existing DL frameworks have unsatisfying performance for online serving: many RNN …

Since its invention, data-centric code generation has been adopted for query compilation by
various database systems in academia and industry. These database systems are fast but …