UCBerkeley RISELab

In the RISELab (Real-time Intelligent Secure Explainable Systems), we develop technologies that enable applications to make low-latency decisions on live data with strong security.

Devin Petersohn

Aug 12, 2021

So you want to build an open source tool/library as a grad student

This is a collection of…

Feature Stores: The Data Side of ML Pipelines

We need a principled way of managing state in real-time ML pipelines.

Amir Gholami

Mar 29, 2021

AI and Memory Wall

Update: An extended version of this blogpost is published in IEEE Micro Journal and is available online here.

(This blogpost has been written in collaboration with Zhewei Yao, Sehoon Kim, Michael W. Mahoney, and Kurt Keutzer. The data used for this study is available…

Why every data scientist using pandas needs Modin — Bringing SQL to Dataframes

While recently speaking…

How to ensure a data scientist is never productive

We need to start placing a higher value on data…

Joe Hellerstein

Aug 13, 2020

A photo of the cloud from https://www.flickr.com/photos/kky/704056791

The State of the Serverless Art

The Hydro team in Berkeley’s RISELab is working to “put the state into…

Anastasios Angelopoulos

Jul 28, 2020

A directed acyclic graphical model illustrating dozens of biases incurred when collecting COVID-19 surveillance data.

Estimating the fatality rate is difficult but doable with better data

Rishabh Poddar

Jul 16, 2020

Secure Collaborative XGBoost on Encrypted Data

A library for multi-party training and inference of…

Jason Dai

Jul 8, 2020

Context-Aware Fast Food Recommendation at Burger King with RayOnSpark

Authors: Luyang Wang (lwang1@rbi.com)…

Alvin Wan

Apr 17, 2020

Making Decision Trees Accurate Again: Explaining what Explainable AI did not

Combining neural networks…

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