James is a fifth-year PhD student at Stanford University working with Matei Zaharia and Pat Hanrahan. His research interests are in hardware acceleration for data applications. His research has appeared at conferences like ASPLOS and VLDB.
May 28, 2021 11:05 AM PT
Description: We present a supervised anomaly detection approach that is scalable and interpretable. It works with tabular data and searches over all decision rules for the anomaly class involving one or two features. It creates a classifier out of all rules meeting user-specified precision and recall constraints, classifying a test example as an anomaly if any of the rules fire. Overlapping decision rules can be pruned to reduce model complexity, leaving a small number of simple rules that a user can easily understand. Our system operates on Pandas DataFrames and has a high-performance C++ backend with experimental GPU and FPGA acceleration available. It is available open-source at https://github.com/jjthomas/rule_engine