Nikhil T R Email & Phone Number

Bengaluru, KA, IN

Bengaluru, Karnataka, India

Bengaluru, Karnataka, India

- Managed and mentored a team of two software engineers, overseeing task assignments, roadmap planning, and professional development to drive project success and team growth. - Developed an innovative solution for updating DBC parsers, enabling automatic detection of updates between versions of DBC files with optional user inputs. This tackled a complex.

Bangalore Urban, Karnataka, India

- Converted the complex process of parsing Controller Area Network (CAN) data using DBC files into anaccessible feature on the Bytebeam Console. This automation reduced customer onboarding time from one day to instant setup, freeing up 80% of the integration team’s bandwidth.- Incorporated binary search techniques into the CAN parsing algorithm, reducing.

Bangalore Urban, Karnataka, India

- Created algorithms to track sessions on IoT time-series data and parse CAN data in real-time. These algorithms accounted for out-of-order data and supported scaling through sharding, enabling efficient real-time analytics.- Leveraged ClickHouse, a high-performance columnar database, for efficient storage and retrieval of time-series data. Effectively.

Bengaluru, Karnataka, India

- Instrumented Prometheus metrics for existing Flask services, capturing critical performance metrics to monitor system health effectively.- Built comprehensive dashboards in Grafana to visualize system performance. Also set up alerts for system critical metrics.- Became familiar with tools and technologies such as Presto/Trino for distributed SQL.

Kamand

Teaching Assistant for the following courses:1) IC252 Data Science Lab Sessions.2) CS310 Introduction to Communicating Distributed Processes (OS and Networking course)3) CS302 Paradigms of Programming

Bangalore

Explored transfer learning methods on air pollution forecasting using LSTM models to improve prediction in areas with little data. Tasks involved identifying clusters of data on map and training models on small sized clusters using transfer learning from models trained on nearby larger clusters. Tech: Python, Keras.