In 2015, I graduated from WPI with a Bachelors in Mechanical Engineering with minors in Aerospace and Electrical Engineering. My undergrad thesis was to design and test a flapping-wing Micro-Aerial Vehicles.
I'm interested in multi-robot systems, swarm robotics, computer vision, machine learning (and its niche fields), and optimal control. My research is broadly in multi-robot systems: I'm working on making a collective, decentralized decision-making algorithm in a swarm of robots that is robust to sensor noise and potential defecting agents while constrained by memory and communication capabilities. I've also worked in autonomous vehicles through Udacity's self-driving car nanodegree program where I've worked on Computer Vision, Deep Learning, Sensor Fusion projects.
Additionally, I've worked on machine learning projects in my time such as speed prediction in a video stream, semantic segmentation , text generation, adversarial image generation, & Kaggle challenges as well.
My long-term interests are to work in tying together reinforcement learning with the control of multi-robot systems.
Udacity's Self-Driving Car Nanodegree
Ongoing.Completed Term One (Computer Vision & Deep Learning) and Term Two (Sensor Fusion, Localization, & Control).
Worked on multiple projects with a goal of implementation on Udacity's Carla (and possibly an RC-car). Implemented lane detection using color palette selection, Canny Edge detection, Region-of-interest determination, and Hough feature transform or color-edge thresholding, perspective transformation, line-fitting using sliding-window algorithm of a color-histogram and then second-order polynomial fitting, traffic sign classification based on custom neural networks (inspired by Lenet, Inception) on augmented German Traffic Sign Dataset,
Behavioral Cloning using NVIDIA's end-to-end network, vehicle detection and tracking on a dashboard camera using generated features and SVMs (improved by then implementing ensembles and YOLOv2 for real-time tracking).
Created a control scheme to win the Scaling chain of integrators domain using ROS & Python. The problem required a point robot to optimally reach randomly-generated goals and avoid obstacles in any dimension-space.
The solution used a traveling-salesman optimization to order goals, potential fields attracting to goals & repelling from obstacles, random walk to avoid unfortunately generated polytopes and logic to indicate unsolvable scenarios. A futher implementation was done by creating a bisimulation of the higher-order system with a
lower-order system (which while less accurate will make finding a solution simpler.)
Deep Reinforcement Learning implementation for a racing game.
Implemented Deep-Deterministic Policy Gradient to control a racing agent in an Outrun simulator with rewards given based on normalized velocity and penalties given to collisions and off-road travel. The issue with this simulator is stochasticity
(the agent can chose between different tracks at different points in the race) which meant significantly more training needed to be done, and the lack of proper sensors on the car (for positioning, proximity, wheel spin, orientation etc.) which meant that rewards couldn't be tuned as finely. This algorithm will be reimplemented
on another simulator such as Torcs.
Hardware implementations of collective motion and flocking in a Sparki swarm.
Worked on a setup using Sparki robots identified using Aruco tags and an overhead camera system. Implemented collective transport of
of a bulky object, warehouse sorting (sorting of boxes into respective goal regions), and flocking. Flocking was implemented by creating a virtual distance sensor (using the overhead system) that would
give local distances instead of a global distance. Each agent has feels a force (based on the Lennard-Jones potential relative to neighboring positions) and aims to maintain a similar velocity vector with other robots
to a goal location.
Haptic Feedback implemenation for the da Vinci surgical system.
Creation of a modular directional haptic feedback device that can augment the performance of a surgeon using the da Vinci research kit by providing sensory feedback in addition
to visual feedback. The haptic feedback worked by using asymmetric sinusoidal ascillations producing an apparent shearing sensation implemented using resonating actuators using PSM joint commands in a ROS simulation environment. The goal
was to make tasks like suturing much easier. An unanticipated side-effect of the setup was while directionality could originally be sensed, after a few minutes of constant vibration, the brain tunes out the sense of direction.
Compressible Fluid Mechanics
Design and implementation of a Transonic wind tunnel.
Creation of a transonic wind tunnel to test air speeds betweem M=0.3 to M=0.9 and involved sizing calculations for the throat, Solidworks modeling of the system, with proper tolerancing before machining could occur.
Artificial Intelligence Grader - Fall 2018 Instructor: Professor Dmitry Korkin
Introduction to Communication & Networks Graduate Tutor - Spring 2018 Instructor: Professor Alexander Wyglinski
Analysis of Probabilistic Signals and Systems Graduate Tutor - Fall 2017 Instructor: Professor James Matthews
Principles of Communication Systems Senior Tutor - Fall 2017 Instructor: Professor Alexander Wyglinski
Optimal Control Grader - Spring 2017 Instructor: Professor Raghvendra Cowlagi
ICRA 2016 FMR Scaling Chain of Integrators Winning team
WPI International Scholarship & Dean's List
Charles O. Thompson Scholar
Tau Beta Pi (Engineering Honor Society)
Edexcel Challenge Trophy & Best Academic Results 2011