Arjun Gupta Email & Phone Number

Teaching robots how to be useful

Santa Clara, CA, US

- One of the youngest technical leads. Led camera blindness detection and environmental sensing - negotiated features and requirements with stakeholders, planned and assigned work deliverables to the team, worked across teams to meet functional use-cases, held accountable for the final delivery of production blindness detection and environmental sensing.

Santa Clara, CA, US

- Built automation infrastructure to streamline DNN model development, KPI computation, and deployment- Built a system for maintaining traceability from training jobs to the code and commands used to generate them in order to improve repeatability of experiments- Designed a method to determine whether an upcoming region of road would be consistently.

- Developed several methods for monitoring the outputs of neural networks for direct use in pedestrian detection monitoring. The resulting paper was accepted to IEEE Intelligent Transportation Systems Conference 2020. - Collaborated with other researchers to develop a system for building a Dynamic Scene Graph (DSG) of a building automatically, the first.

Santa Clara, CA, US

- Developed a sensitivity analysis pipeline to assess neural network weaknesses- Helped improve path perception neural networks through the addition of augmentations and some minor architecture changes.

• Collaborated with two postdoctorates to develop a pedestrian trajectory prediction model that integrates information from the environment
• Integrated a model architecture from a top paper in the field
• Refactored codebase for clarity and efficiency

• Fixed, refactored, and wrote test cases for probability distribution functions for sensor detections
• Researched and developed a novel algorithm for road barrier detection and tracking using only radar data
• Collaborated with other interns to develop an end-to-end indoor beacon localization system and demonstrated a parking maneuver on the real vehicle without GPS

• Fall 2017 and Winter 2017-2018
• Developed Deep Reinforcement Learning platform for learning autonomous behaviors. Platform can run arbitrary simulations to get data and train Deep Neural Network models. Focus on usability. All aspects of the neural.
• Developed new class to embed python interpreter in C++ script to call Python Keras libraries from the learned behavior written in C++.
• Resulting platform allows for a new process of behavior creation using Deep Reinforcement Learning to make more complicated behaviors that can learn intelligent interactions between robots.
• Code publicly available at: https://github.com/argupta98/moos-ivp-pLearn
• Wrote a conference paper on the integration of Reinforcement Learning with MOOS-IvP, and will be presenting the paper at OCEANS conference in October 2018.Summer 2017

• Collaborated with other researchers using Git workflow to write an interpreter which converted App Inventor Block Code to code runnable on Arduino101. Built key functions to run the interpreter and call device methods..

Oxon Hill, Maryland, US

• Designed and built a remote control car that could autonomously navigate and drive using Radio-Frequency(RF) beacons for indoor localization. Implemented a waypoint following algorithm on Arduino to control servos.

Master'S Degree, Artificial Intelligence

Bachelor’S Degree, Computer Science