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Associate Professor Jasleen Kaur received a 2019 Google Faculty Research Award from Google AI. Kaur’s proposal was one of only 150 to be funded out of 917 applications from 50 countries.

Google Faculty Research Awards provide unrestricted gifts to support world-class technical research in computer science, engineering, and related fields. The awards are structured as seed funding to support one graduate student for one year. The awards are very competitive; on average, around 15 percent of applications receive funding, and Kaur’s research proposal was one of only eight to be funded in the area of networking. The review process for 2019 involved 1,100 reviewers from across Google.

Many enterprises like Google, Facebook, and Amazon maintain large-scale, private networks to connect datacenter clusters. Those networks are equipped with instruments that monitor the health and performance of the network, but when anomalies and outages occur, manually diagnosing the problem can require much time and effort.

Kaur’s proposal will use unsupervised learning methods to analyze performance data collected on Google’s B4 network. Unsupervised learning is a branch of machine learning where a computer attempts to draw inferences from input data without any output data. In other words, rather than being given raw data and corresponding conclusions and determining how to classify the raw data, the computer will be asked to analyze only the raw data in search of patterns that will help in manual diagnosis.

The goal of Kaur’s project is to derive models that can help identify the causes of anomalies in monitored data. If successful, the research will enable more efficient and accurate diagnosis of the root causes of anomalies in wide-area enterprise networks like Google’s.