“Once you've captured this notion in code, you can start doing things like incorporating it into active measurement loops,” Jonas said. “If I get a spectrum and my structure elucidation method says it's one of these five structures, well now I'm going to try to devise an experiment that will uniquely tell me which of these five. You can start doing this closed-loop, thus accelerating the measurement process. That's really the goal of all of this work.”
Along the way, Jonas also found a productive side project in PyWren, a package he wrote to help less CS-savvy scientists more easily utilize cloud computing resources. Services such as AWS Lambda allow users to almost instantaneously access hundreds or thousands of cores to run computing jobs in parallel, but often require deep technical knowledge to deploy. In another provocative paper titled “Occupy the Cloud: Distributed Computing for the 99%,” Jonas argued for software that reduces these barriers, a challenge he has attempted to address with PyWren and its sibling, NumPyWren.
“We think of simulation often as this thing that lives in these big HPC environments, but in fact, everyone who writes a probabilistic model of their data is basically doing some form of simulation,” Jonas said. “All that simulation really is, is the computer executing your model of how the world works, and it's almost always embarrassingly parallel. I think there's a real chance to lower that activation energy and let everyone use these sorts of systems.”
At UChicago, Jonas hopes to continue these research threads in collaboration with scientists across campus as well as at Argonne and Fermilab. After helping teach the CS 121 - Computer Science with Applications course this fall, he’s planning courses and workshops on machine learning for inverse problems and measurement that will fold into the University’s traditional strength in developing new technologies for understanding nature and the universe.
“Chicago has this large history of instrumentation ranging from Millikan measuring the charge of the electron 100 years ago, to Fermi building fission reactors, to Fermilab, to the Advanced Photon Source at Argonne,” Jonas said. “There's this whole trajectory of people who are trying to advance science by basically building things so you can just see what you're curious about. So it's a very exciting place to come in as a machine learning person.”