Rohit Murali

Rohit Murali has over five years of professional experience in robotics software engineering and research. Rohit began their career in 2016 as a Software Engineering Intern at Cyklo, a bicycle automation startup. During their time there, they built a facial recognition system with 88% accuracy using MTCNN and FaceNet models and implemented I2C communication protocol between Raspberry Pi and Microcontroller to verify OTP entered on bicycle stand. In 2017, they worked as a Software Engineer Intern at Sardar Patel Robotics and Automation Committee, where they tested and built software to actuate motion for manual and autonomous robots to navigate obstacles and detect targets. Rohit also performed real time object detection using OpenCV image processing functions.

From 2019 to 2022, Rohit worked as a Graduate Research Assistant and Research Assistant at Virginia Tech. As a Graduate Research Assistant, they predicted relative target location using data collected in real time for stationary AUV by optimizing beamforming algorithms. Rohit also simulated Adaptive filtering and Spectral Subtraction on MATLAB to filter noise added due to AUV movements. As a Research Assistant, they designed a PCB using KiCAD to actuate solenoid valves to control gas flow for a system that quantifies hazardous substances in gases. Rohit also developed and integrated temperature system to control gas flow based on temperature.

Currently, Rohit is working as a Robotics Software Engineer at Owl Autonomous Imaging.

Rohit Murali has a comprehensive educational history. Rohit attended St. Pius X High School from 2002 to 2013, after which they attended Bhartiya Vidya Bhavans Sardar Patel Institute of Technology Munshi Nagar Andheri Mumbai from 2015 to 2019, where they obtained a BE in Electrical, Electronics and Communications Engineering. Rohit then pursued a Masters of Science in Systems, Controls and Signals from Virginia Tech from 2019 to 2022. Additionally, they have obtained various certifications from Coursera and Udemy, including Browser-based Models with TensorFlow.js, Architecting with Google Kubernetes Engine: Foundations, Architecting with Google Kubernetes Engine: Production, Architecting with Google Kubernetes Engine: Workloads, Google Cloud Platform Fundamentals: Core Infrastructure, Convolutional Neural Networks, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Learn VHDL and FPGA Development, Sequential Models, Structuring Machine Learning Projects, Google Cloud Platform Big Data and Machine Learning Fundamentals, and Data Analysis with Python.