Julian Dolby has been a Research Staff Member at IBM's Thomas J. Watson Research Center since 2000. He works on a range of topics, including static program analysis, software testing, the semantic web (AI) and programming technology support for machine learning.
- Initial exploration has revealed challenges with common machine learn practice that necessitate techniques like the following: always make every tensor dimension be a different size, to minimize matrix manipulation bugs; another tactic is copious comments detailing the layout of tensors. They made some initial efforts on using program analysis to obviate such burdens and make code more reliable; they have started building such support using WALA: https://github.com/wala/ML
- His testing work has been focused on several areas: Web applications in the Apollo project, finding concurrency bugs using both dynamic execution and model checking, and finding security issues in Android apps (https://dl.acm.org/citation.cfm?id=2771803)
- His semantic Web work has been on scalable inference with the SHER project; recently, he has focused on representing RDF data efficiently in an RDBMS. A summary of much of their Semantic Web work in keynote he gave at the Semantic Big Data workshop at SIGMOD 2017 can be found here: https://www.youtube.com/watch?v=YTSgXrGnxjg&t=2h1m44s
He was educated at the University of Wisconsin-Madison as an undergraduate, and at the University of Illinois at Urbana-Champaign as a graduate student where he worked with Professor Andrew Chien on programming systems for massively-parallel machines.