Research & Teaching

I’m a Research Associate in the UC Berkeley RAD Lab, where I work on applying statistical machine learning (SML) techniques to the challenges of developing, deploying and operating datacenter-scale Internet systems.

I also teach undergraduate and graduate courses in this area, and I have an irrational interest in the history of computing and in retrocomputing.

Personal interests, hobbies, having a life

Outside of work, I do a lot of stuff with music, musical theater (the only major indigenous American musical form other than jazz), and lots more, which you can learn about on my personal site.

Current research projects & papers

Here’s a list of all my papers in PDF format; recent ones also appear below.

  • AWE: using machine learning to simultaneously predict multiple aspects of resource utilization of long-running workloads (e.g. database queries, MapReduce jobs). Joint work with HP Labs.
  • SCADS: a new data/query model for horizontall-scalable interactive applications. We start with the fundamental constraints imposed by scalability and work our way backwards to a usable query model that forbids expressing too-expensive queries.
  • Internet service modeling: using machine learning to model the variations in response times of multi-tier Internet applications. Joint work with Microsoft Research Silicon Valley.
  • Console log mining: combining text mining techniques with source code analysis to find markers for hard-to-find bugs in the console logs of complex applications.
  • Scaling Ruby on Rails: investigating the scalability limits, bottlenecks, and developing best practices for RoR on modern (read: multicore) hardware. Joint work with Sun Microsystems.