Scott Sikorski

University of Virginia

GPA: 3.83

Coursework: Reinforcement Learning, Learning for Interactive Robots, Software Engineering for Machine Learning, Software Analysis, Human-Robot Interaction, Machine Learning

2x All-ACC Academic Honor Roll

2024 Mobile App Development Teaching Assistant

Embedded Software Engineer Intern

Rapid development and deployment of full stack embedded software with new radar technology for autonomous vehicles. Integrate Human-in-the-Loop services to embedded application. Dockerize full main application with MongoDB for portable Linux usage.

Embedded Software Engineer Intern

Developed and maintained high level software across shared C/C++ codebase for Forerunners. Built 12 new sport profiles and integrated 3 new autonomous features for existing ski activities. Built unit tests with GoogleTest for 10 transition animations and 25 watch faces to verify UI. Built 5 new widgets in graphics library for new AMOLED display. Contributed to release cycle of new product.

Research Assistant

Built and optimized Quiet Direct Simulation using CUDA for NVIDIA GPU for fast, low-noise fluid simulation. 2000% improvement for 3d scenario over C++ CPU achieving below 1ns per particle per timestep.

Reinforcement Learning for Task and Motion Planning in Mobile Home Robots

Double Deep Q-Network learning approach to complete complex sequential tasks in home environments. The model was trained in unique environments on a variety of tasks that include motion and interactable tasks. Custom state one-hot encoding was used to represent the robot's state space in relation to the environment to accommodate a real-time learning environment. Testing was conducted in the simulated environments to demonstrate the model's ability to learn the optimal policy for minimal actions and no failures.

Total Running

Features: weekly run log with Garmin API, calculator, feed of friends, goals, personalized metrics Used Firebase for backend NoSQL database and user authentication Deployed self-trained running metric AI models on personal Pi server using Flask and REST APIs

Lifelong Learning for Task and Motion Planning in Mobile Home Robots

Dual policy task and motion planning approach to catastrophic forgetting, the forgetting of task completion when placed in a new environment. One policy would guide the motion using a RRT. The other policy is trained using a limited memory replay buffer to simulate the memory constraints of a robot. This policy would ensure that the loss of previous tasks did not increase when learning new tasks using Gradient Episodic Memory. This model suffered from poor exploration, which was addressed in the reinforcement learning version of this.

Machine Independent Optimized C Compiler

For a subset of the C programming language, I built a compiler that scans, parses, builds intermediate representations, and optimizes the code. Scanner, parser, and abstract syntax tree (AST) are built using C++. 3 address code and all optimizations are implemented using Python3.

• Scanner that finds syntax errors
• Parser that builds an abstract syntax tree (AST) Error recovery is implented using Wirth's algorithm so that after an error, the program will recover to find additional errors that are displayed to user.
• From AST, 3 address code is generated, which is machine independent, not control flow bound low level code
• Using 3 address code, our basic blocks and control flow graph are generated to perform optimizations and thus data flow analysis
• Optimizations: Local Value Numbering, Dead Code elimination, Partial redunancy elimination

Autonomous 7-DOF Robot

Robot autonomously performs grasping and placing of objects in a world environment. World environment is built using sensors as state space is discretized over pi/16.0 radians. Rapidly exploring random trees (RRTs) are used to sample the state space and find shortest path. This is done by randomly sampling in robot link and joint limits then finding the nearest constructed node. A new node is added pi/16.0 radians in the direction of the sampled node and creates an edge between the nearest and new nodes. The back reference pointer to this node is then tracked to find the shortest path