Working on a team of 3 to modernize internal systems for sales, customer calls, and product uploads. Responsibilities include SQL DB Automation and Management, selenium site testing, chrome extensions and external tools, system patching, api development, disaster recovery strategy, and data analytics. Primarily focused on opportunities for automation to reduce average call-time, down-time, and onboarding.

The VR-Teleop Lab is currently working on a research project in partnership with BostonDynamics. This project is being led by PhD Stefanie Tellex and is focused on the early development of long-distance low-latency teleoperations with Robotics, VR (Virtual Reality), Computer Vision, AI (Artificial Intelligence), and Unity (programming language).

• Started working as the only undergrad student and progressed to supervise the newest team members.

• Optimizing the rendering performance of pointcloud data from Spot using Unity.

• Using AI to improve the quality of video: reduce the effects packet loss on video streaming.

• Investigating the level of effort and impact on upgrading to a new version of ROS (Robot Operating System).

The Breakthrough Lab is Brown University’s accelerator program for students developing high-impact ventures. Foreverfan is a Brown Breakthrough Lab (B-Lab) sponsored startup. Foreverfan aims to help new musicians analyze and bolster their growth with data analytics and social tools.

• Joined the seed stage of Foreverfan with 5 other fellow Senior students.

• Responsible for front-end and back-end development.

• Drive brainstorming sessions with the founders (CEO and CTO) to discuss business models, product direction and roadmap.

Alarm.com is a publicly traded company ($843M yearly revenue) that offers an intelligent cloud based (SaaS) operating system which enables service providers and their subscribers to manage and control their residential and commercial properties with highly advanced IoT technology solutions.

• Worked on a team of four tasked with converting an in-house AI (Artificial Intelligence) model, used to detect movements, into a binary executable that could be interpreted by a convolutional neural networks (CNN).

• Leveraged Deep Learning concepts, TensorFlow and Caffe frameworks to understand the in-house AI model and ensure that it can work with different deep learning frameworks.

• Developed a new executable to work with embedded middleware on a stream video recorder (SVR).