Technical and strategy lead for 10+ engineers on Google Search working on backend result indexing, retrieval, ranking. Support all aspects of feature development, primarily in C++, including feature design and implementation, training and quality tuning, performance optimizations to support ultra-high reliability and low latency, live experiment design and analysis, and binary release management (logging, canarying, rollouts, monitoring, on-call rotations). Collaborate cross functionally with product managers, user experience researchers, and designers to lead the end-to-end development of numerous Google Search features which are used by billions of users around the world.

People manage a small team of junior engineers, providing career guidance, performance management, and foster successful group dynamics to accomplish team goals.

Big Data Network Analyst (2015-2017): Used machine learning, statistics, and research to enhance the intuition of company decision makers and introduce data in an understandable way. This included the following responsibilities.

• Analyzed crowd-sourced Per-Call Measurement Data (PCMD) from the 4G LTE network. Developed a new RF footprinting technique to estimate and predict customer locations from PCMD.

• Studied user movement and trajectories from PCMD in a number of scenarios including at an NFL game and during a Wireless Emergency Alert (WEA) broadcast during a hurricane. Answered questions such as, "how many people were at the event?", "where did they come from", "where did they move to?".

• Explored a 5G cellular architectures enabled by Software Defined Networking (SDN) such as a virtualized Evolved Packet Core (vEPC).

• Developed a big-data collection and analytics tool to support the aforementioned activities including close-loop network control, automation, and visualization.

Research Intern/Collaborator (2012-2015)

Characterized the repeatability and predictability of experimentally obtained received signal power on cellular networks in the presence of human mobility. Studied the applicability of these insights to basestation handoff, user localization, and data scheduling algorithms. Demonstrated that the repeatable patterns of received signal power along a user’s commute can be used to localize the user and improve handoff procedures and scheduling algorithms. This work continued as part of the Ph.D. Thesis.

Analyzed Medium Access Control (MAC) layer performance for mobile ad hoc networks (MANETs) equipped with next generation beamforming technologies. Derived the MAC layer performance upperbound and demonstrated the feasibility of a random access MAC layer protocol to achieve the performance bound, albeit at the cost of added delay. Considered implications on latency, topology control, and power constraints for our random access MAC protocol. This work continued as part of the Ph.D. Thesis.