An NSF Expedition Project
REAL-TIME INTELLIGENT SECURE EXPLAINABLE SYSTEMS
In the RISELab, we develop technologies that enable applications to make low-latency decisions on live data with strong security.
“RAMP-TAO: Layering Atomic Transactions on Facebook’s Online TAO Data Store” Wins Best Industry Paper Award at VLDB 2021October 25, 2021
Congratulations to RISELab’s Audrey Cheng, Natacha Crooks, Ion Stoica, Peter Bailis and their Facebook...
Professor Alvin Cheung talks with Justin Gottschlich, Principal AI Scientist and Director/Founder of Machine...
Security Seminar: Semantic Techniques for Information-Flow Languages with Andrew Hirsch, Friday Nov. 5th, 12 PM PDT
Title: Semantic Techniques for Information-Flow Languages Speaker: Andrew Hirsch Time: Friday Nov 5 at 12PM Zo...
Title: The Tale of a Success with Ali Ghodsi Speaker: Ali Ghodsi (CEO and Co-Founder of DataBricks and EECS ...
Dissertation Talk by Devin Petersohn: Dataframe Systems: Theory, Architecture, and Implementation; 3 PM, Monday, August 9
Title: Dataframe Systems: Theory, Architecture, and Implementation Speaker: Devin Petersohn Advisor: Anthony...
Originally posted on Medium: Written by Raluca Ada Popa on September 16, 2021 How to collaborate…
This is a collection of experiences and recommendations for building an open source community as…
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.
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.
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.
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.
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.