MS Robotics @ Northeastern University | Robotics Software Engineer
Building scalable, reliable systems at the intersection of robotics, machine learning, and real-world autonomy.
I am a robotics graduate student at Northeastern University with over seven years of prior industry experience as a software and machine learning engineer. My background includes designing and operating production-scale distributed systems, real-time data pipelines, and ML platforms for anomaly detection and forecasting.
I'm working at the intersection of AI and robotics, where intelligent systems must operate safely and reliably in the physical world. My interests include motion planning, control theory, simulation-based learning, and the observability infrastructure required to deploy and scale autonomous systems in production environments.
My work spans large-scale software systems, machine learning pipelines, and robotics systems. In industry, I built and operated production ML infrastructure for anomaly detection, forecasting, and observability at scale. In robotics, I focus on motion planning, simulation-driven learning, and real-time control, with an emphasis on reliability and system behavior under edge cases.
Evaluated nonholonomic motion planners for Ackermann-steered vehicles, focusing on performance trade-offs between feasibility, runtime, and planning success.
Created Python-based robot navigation simulator with RRT path planning and adaptive P-controller achieving 90%+ goal-reaching success in obstacle-dense environments through integrated LIDAR sensing, multi-gain control architecture, and real-time collision avoidance.
Building an end-to-end pipeline using the CARLA simulator to surface rare and safety-critical driving scenarios, focusing on identifying long-tail events and improving agent behavior through reinforcement and imitation learning.
Implemented a complete kinematic and control framework for a UR5 6-DOF manipulator, replacing an unreliable iterative inverse kinematics solver with a differential velocity-level controller.
In prior roles, I built and operated production-scale distributed systems and ML pipelines handling high-volume telemetry and real-time decision-making. While the code is proprietary, the architectural patterns and operational lessons directly inform my robotics and autonomy work.
Understanding the principles of API retries, including exponential backoff, jitter, fallback mechanisms.
Read More →Uncover the fundamentals of virtualization, including hypervisors, containerization, and their role in modern cloud infrastructure.
Read More →Understanding data durability and the critical role of Write-Ahead Logging (WAL) in ensuring data integrity.
Read More →This is video talks about how to build an image classifier using the handwritten digits dataset
Watch on YouTube →This video describes what AutoML is and how it can be used to automate machine learning tasks.
Watch on YouTube →This video explains how to scale features in machine learning datasets to improve model performance.
Watch on YouTube →