Publications

Hashing is a widely used technique for creating uniformly random numbers from arbitrary input data. It is a core component in relational data systems, key-value stores, compilers, networks and many more areas used for a wide range of operations including...

We present a self-designing key-value storage engine, Cosine, which can always take the shape of the close to “perfect” engine architec- ture given an input workload, a cloud budget, a target performance, and required cloud SLAs. By identifying and...

We present Stacked Filters, a new probabilistic filter which is fast and robust similar to query-agnostic filters (such as Bloom and Cuckoo filters), and at the same time brings low false positive rates and sizes similar to classifier-based filters (such...

Convolutional neural networks are utilized to solve increasingly more complex problems and with more data. As a  result, researchers and practitioners seek to scale the representational power of such models by adding more parameters. 
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However, increasing...

Ensembles of deep neural networks significantly improve generalization accuracy. However, training neural network ensembles requires a large amount of computational resources and time. State-of-the-art approaches either train all networks from scratch...

Key-value stores are everywhere. They power a diverse set of data-driven applications across both industry and science. Key-value stores are used as stand-alone NoSQL systems but they are also used as a part of more complex pipelines and systems such as...

We introduce Rosetta, a probabilistic range filter designed specifically for LSM-tree based key-value stores. The core intuition is that we can sacrifice filter probe time because it is not visible in end-to-end key-value store performance, which in turn...

We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models...

We survey new opportunities to design data systems, data structures and algorithms that can adapt to both data and query workloads. Data keeps growing, hardware keeps changing and new applications appear ever more frequently. One size does not fit all...

Data-intensive key-value stores based on the Log-Structured Merge-Tree are used in numerous modern applications ranging from social media and data science to cloud infrastructure. We show that such designs exhibit an intrinsic contention be- tween the...

We propose a solution based on first principles and AI to the decades-old problem of data structure design. Instead of working on individual designs that each can only be helpful in a small set of environments, we propose the construction of an engine, a...

Data-intensive analytical applications need to support both efficient reads and writes. However, what is usually a good data layout for an update-heavy workload, is not well-suited for a read-mostly one and vice versa. Modern analytical data systems rely...