Lei Cao: Toward an End-to-end Anomaly Discovery Paradigm

ABSTRACT:

Anomaly detection is critical in enterprises, with applications ranging from preventing financial fraud, and defending network intrusions, to detecting imminent device failures.

Although previously developed research offers a plethora of stand-alone methods for detecting particular types of anomalies, there is no end-to-end solution for data scientists to effectively discover anomalies over large volumes of varied data. To build such a system, several critical challenges have to be solved: How to determine which among many alternative anomaly detection algorithms is the best for a given task and to find the proper parameter settings? How to leverage a small amount of end-user feedback to improve the anomaly extraction process? How to best present the anomaly detection results such that users do not have to evaluate the potentially large number of anomaly candidates one by one?

This talk will present our ADP solution that solves all above problems. ADP supports all stages of anomaly discovery by seamlessly integrating anomaly-related services within one integrated platform. It enables the tuning free anomaly detection and achieves the power of sense making afforded by anomaly summarization and explanation services, while allowing the users to easily steer the discovery process with human ingenuity.

Bio:

Dr. Lei Cao is a Postdoc Associate at MIT CSAIL, working with Prof. Samuel Madden and Prof. Michael Stonebraker. Before that he worked at IBM T.J. Watson Research Center as a Research Staff Member. He received his Ph.D. in Computer Science from Worcester Polytechnic Institute, supervised by Prof. Elke Rundensteiner. His recent research is focused on developing end-to-end tools for data scientists to effectively make sense of data.

Zoom Linj: https://uci.zoom.us/j/808173003