Runtime Observability Team. Working on logs and metrics in Databricks Runtime.

Continued as a teaching assistant for CS170: Efficient Algorithms and Intractable Problems in the Fall 2022 semester.

Came back to the Behavioral Analytics team as a returning intern to continue work on the service that I designed in the previous internship. Reworked an SPL2 (Splunk Processing Language) interpreter in Java responsible for running security detection rules. Used data structures and algorithms, especially tree traversals, to build evaluators based on the SPL2 query and process customer security events according to the query on the fly. The new interpreter had a 40% improvement in memory usage and a 270% improvement in events throughput, in addition to increasing the readability of detection rules.

As a teaching assistant for a computer algorithms class, created coding contests, debugged written homework assignments, hosted office hours, took part in reviewing and grading midterm and final examinations. After this appointment, based on student feedback, I was selected to receive the 2022 Outstanding Graduate Student Instructor Award.

Utilized Java, Spring Boot and Apache Pulsar to design, implement, and deploy a cloud-native distributed system of Kubernetes pods capable to start pipelines of simple filtering detection rules, ingest customers’ security logs into the pipelines and produce resulting security anomalies. Later, the service became the backbone of the team’s architecture responsible for processing security events. I collaborated on the design with many engineers, including senior and principal engineers.