Data Scientist

• Jan 2022 - Present

Working on the Aquila Edge Kiosk: - Improved the response time of the Analytics REST Api from ~10s to 600ms per request using postgresql for storing data, Redis for caching, Asyncio for concurrent processing and FastApi for serving. - Implemented a dashboard to view the analytics using Plotly and Dash. - Created a filtering approach for time series data based on speckle filtering in computer vision, to improve the quality of the sensor signals coming from the rig. - Deployed the project using Docker on the Edge device. Technologies used: Python, Redis, FastApi, Postgresql, Asyncio, Scikit-learn, Numpy, Plotly, Dash

Graduate Research Student

• Jun 2020 - Apr 2022

Working in BimiLab under the supervision of Dr Jaerock Kwon on Internal Simulation Networks for Autonomous Vehicles :- Collecting data from Carla Simulator and preprocessing it- Using the data to train a neural network system that predicts what the vehicle will see next using the current image and motor information from the vehicle- Applying the system to create a lane keeping system Used SciPy stack, Tensorflow 2.0 and Pytorch in a later iteration.

Graduate Student Instructor

• Sep 2019 - Apr 2022

Worked as a Teaching Assistant on multiple graduate and undergraduate courses, my duties include:- Providing office hours for students- Grading the assignments and examinations- Proctoring examinations

Graduate Research Assistant

• Sep 2019 - Jun 2020

Worked in ISELab with Dr Marouane Kessentini on the optimization of Software Refactoring in the decision and objective spaces through extracting the developer's preferences:- Implemented a Clustering Algorithm using Expectation Maximization Algorithm for Gaussian Mixture Models- Contributed in writing a paper published in Transaction in Software Engineering

Internship Trainee

• Jun 2018 - Aug 2018

Detecting Suspicious Actions using Deep Learning: - Collected Data using a Raspberry Pi Camera system - Preprocessed Data using OpenCV and Scikit Image - Trained and infered a 3D convolutional network using Keras and Tensorflow Detecting Suspicious Actions using Deep Learning: - Collected Data using a Raspberry Pi Camera system - Preprocessed Data using OpenCV and Scikit Image - Trained and infered a 3D convolutional network using Keras and Tensorflow

Summer Intern

• Jun 2017 - Jul 2017