News
Alvin Cheung awarded the 2020 Intel Outstanding Researcher Award
March 2, 2021Congratulations Alvin! Originally posted on EECS News and Intel.com: EECS Assistant Profs. Alvin Cheung and...
Alessandro Chiesa awarded the 2021 Sloan Research Fellowship
February 19, 2021RISELab faculty member Alessandro Chiesa–along with five other Berkeley assistant professors–was...
David Patterson wins Frontiers of Knowledge Award
February 10, 2021Congratulations Dave! Reposted from EECS News: CS Prof. Emeritus David Patterson has won the 13th BBVA...
Events
RISE Seminar 3/5/21: Building Storage Systems for New Applications and New Hardware, talk by Vijay Chidambaram, UT Austin
Title: Building Storage Systems for New Applications and New Hardware Time: 12-1 PM Pacific Time, Friday Mar...
RISE Seminar 2/26/21: Vignettes from Applied Research @ Splunk, a talk by Ram Sriharsha
Title: Vignettes from Applied Research @ Splunk Time: 12-1 PM Pacific Time, Friday February 26th, 2021 YouTu...
RISELab Poster Session / BEARS Symposium – Thursday February 11, 2021
We are pleased to announce that RISELab will hold a virtual poster session as part of the BEARS 2021 Researc...
Blog
Cutting edge hyperparameter tuning with Ray Tune
August 19, 2019
by Richard Liaw – Ray Tune is a Python library that accelerates hyperparameter tuning by allowing you to leverage cutting edge optimization algorithms at scale.
RayOnSpark: Running Emerging AI Applications on Big Data Clusters with Ray and Analytics Zoo
July 29, 2019
by Jason Dai, Zhichao Li – AI has evolved significantly in recent years. In order to gain insight and make decisions based on massive amounts of data, we need…
Two missing links in Serverless Computing: Stateful Computation and Placement Control
May 12, 2019
by Devin Petersohn – Source by Ion Stoica and Devin Petersohn – Serverless computing is rapidly gaining in popularity due to its ease of programmability and management. Many see it as the next general purpose computing…
RISELab Story
Berkeley’s computer science division has an ongoing tradition of 5-year collaborative research labs. In the fall of 2016 we closed out the most recent of the series: the AMPLab. We think it was a pretty big deal, and many agreed.
One great thing about Berkeley is the endless supply of energy and ideas that flows through the place — always bringing changes, building on what came before. In that spirit, we’re fired up to announce the Berkeley RISELab, where we will focus intensely for five years on systems that provide Real-time Intelligence with Secure Explainable decisions.
Context
RISELab represents the next chapter in the ongoing story of data-intensive systems at Berkeley; a proactive step to move beyond Big Data analytics into a more immersive world. The RISE agenda begins by recognizing that there are big changes afoot:
- Sensors are everywhere. We carry them in our pockets, we embed them in our homes, we pass them on the street. Our world will be quantified, in fine detail, in real time.
- AI is for real. Big data and cheap compute finally made some of the big ideas of AI a practical reality. There’s a ton more to be done, but learning and prediction are now practical tools in the computing toolbox.
- The world is programmable. Our vehicles, houses, workplaces and medical devices are increasingly networked and programmable. The effects of computation are extending to include our homes, cities, airspace, and bloodstreams.
In short, the loop between data generation, computation, and actuation is closing. And this is no longer a niche scenario: it’s going to be a standard mode of technology going forward.
Mission
Our mission in the RISELab is to develop technologies that enable applications to interact intelligently and securely with their environment in real time.
As in previous labs, we’re all in — working on everything from basic research to software development, all in the Berkeley tradition of open publication and open source software. We’ll use this space to lay out our ideas and progress as we go.
Commitment to Diversity
RISELab is guided by Berkeley’s Principles of Community and is committed to providing a safe and caring research environment for every member of our community. We believe that a diverse student body, faculty, and staff are essential to the open exchange of ideas that RISELab was founded on.
Sponsors
In addition to NSF expedition, we’re extremely fortunate at Berkeley to be supported by — and working with — some of the world’s biggest and most innovative companies. The RISELab’s 13 founding sponsors are quite the crew: Amazon Web Services, Ant Group, Capital One, Ericsson, Facebook, Google, Intel, Microsoft Research, Scotiabank, Splunk and VMware. Thanks to all.
We RISE.
Featured Project
Tune is a powerful library for distributed hyperparameter tuning developed in the RISELab. Built on top of Ray, Tune allows users to easily leverage hyperparameter optimization algorithms including ASHA and Population-Based Training at scale. Tune integrates with the Ray autoscaler to seamlessly launch fault-tolerant distributed hyperparameter tuning jobs on Kubernetes, AWS or GCP. Tune supports any machine learning framework, including PyTorch, TensorFlow, XGBoost, LightGBM, and Keras.
Tune also powers many other research projects across the Berkeley AI Research Lab, including Population-based Data Augmentation and Softlearning.
To learn more about Tune, visit the Tune project page. Tune is packaged as part of Ray and can be found here on GitHub.