Hello! 👋

I'm Joshua McConkie

Robotics & Autonomy | Applied Mathematics Student

Brigham Young University · Expected graduation: April 2028

Seeking Summer 2027 internships in robotics, autonomy, perception, and software engineering.

About Me

I’m an Applied and Computational Mathematics student at Brigham Young University, graduating in April 2028. I enjoy the math behind robotics, and I like building things to see how those ideas work in practice. I’m seeking Summer 2027 internships in robotics, autonomy, perception, and software engineering.

My experience includes multi-agent planning and optimization research at BYU IDeA Labs, scientific simulation, and a camera-based feedback control system. I’m the Perception Lead for BYU’s RobotBoat Team and Vice President of the BYU SIAM Chapter.

I co-designed Mimic’s robotics architecture and skill-transition graph, and directed integration of perception with simulated robot execution. My teams earned first place at the 2026 NVIDIA × Nebius × Antioch × Toloka Physical AI Hackathon and second place at BYU’s HireReady Hackathon.

Perception Lead

BYU RobotBoat Team

Vice President

BYU SIAM Chapter

1st Place · 2026

Physical AI Hackathon

NVIDIA × Nebius × Antioch × Toloka

2nd Place · 2026

BYU HireReady Hackathon — Robot Learning

Mimic · 24-hour team competition

Mathematical Modeling Optimization Simulation Numerical Computing Data Analysis Multi-Agent Systems Feedback Control Robotics Architecture Perception Pipeline Design MuJoCo Python C/C++ Julia MATLAB OpenCV Embedded Systems Linux Git

Projects

Mimic’s simulated Franka Panda carrying a red cube above the tabletop in MuJoCo

Mimic — Learning from Human Demonstration

2nd Place · BYU HireReady Hackathon

Co-created a system that recognizes manipulation phases in human video, tracks object motion separately, and retargets pick-and-place tasks to a simulated Franka Panda in MuJoCo.

Co-designed the architecture and modular skill-transition graph, linking classifier labels to robot actions and validating the two highest-probability predictions against legal transitions.
Specified frame-aligned data and coordinate-mapping contracts, and selected proximity-based waypoint handoff while retaining measured final-arrival checks.
Directed implementation and integration through AI coding agents. The recorded simulation demonstrates grasp, transport, and release; robot motion uses conventional IK and control.

Robotics ArchitectureSkill GraphsCoordinate RetargetingMuJoCo

Physical AI Sprint team on stage holding the first-place award beneath the event and sponsor banner

Physical AI — Robot Integration & Evaluation

1st Place · Physical AI Hackathon · 2026

Our team won the five-hour NVIDIA × Nebius × Antioch × Toloka Physical AI Hackathon with a vision-language-action evaluation and retraining workflow. My contribution centered on the robot-side integration path and control harness for an SO-101 arm.

Built and tested a control harness intended to connect model actions to LeRobot, with action validation, command clamping, and explicit control intents.
Investigated platform, device-mapping, and serial-connection limitations. The final demo ran the frontend and backend; the physical robot connection remained incomplete.

PythonLeRobot IntegrationAction ValidationRobot Control Harness

Ball-on-plate system tracking and balancing a ball

Ball-on-Plate Balancing System

Built a closed-loop control testbed with camera-based ball tracking, real-time state estimation, and PID control. Developed ESP32 firmware for two-servo actuation and IMU feedback over I2C, along with supporting custom PCB hardware.

PythonCOpenCVState EstimationPID ControlESP32

Performance comparison for receding-horizon multi-agent coverage planning

Multi-Agent Coverage Planner

Created a configurable simulator for receding-horizon multi-agent coverage planning. The planner achieved 97–99% of full-horizon coverage while reducing runtime by 2.5–10× across benchmark scenarios.

PythonPath PlanningMulti-Agent SystemsOptimizationData Analysis

Circuit Analysis Solver

Programmed a C++ modified-nodal-analysis solver for resistor networks and independent sources, representing circuits as linear systems to calculate node voltages and component currents.

C++Linear AlgebraCircuit Analysis

Experience

Research Assistant — Multi-Agent Planning & Optimization

BYU IDeA Labs

Mar 2026 - Present

Developed a multi-agent grid path planner that achieved 97–99% of full-horizon coverage with a 2.5–10× runtime reduction
Proved a 50% approximation guarantee for a robust round-robin resource planner against the optimal solution
Identified failure conditions for adjacency-constrained path-planning guarantees

Research Assistant — Quantum Photonics Simulation

BYU Electrical Engineering Department

Nov 2025 - Jan 2026

Optimized a Julia research simulation codebase, achieving an approximately 60× speedup through allocation reduction and hot-loop refactoring
Contributed to the optimization and simulation of a hybrid Gaussian and non-Gaussian quantum-optics state engine

View project repository

Research Assistant — Econometrics

BYU Economics Department

Apr 2025 - Sep 2025

Built statistical-modeling and large-scale data-preparation workflows in Python and Stata for causal-inference research
Supported reproducible quantitative analysis of relationships between language learning and career outcomes

Education

B.S. in Applied and Computational Mathematics

Brigham Young University

Expected Apr 2028

Major GPA: 4.00/4.00; ACT: 35/36
BYU Merit Scholarship; Bain Case Competition second-round participant (2025)
AI Association team lead and Competitive Programming Association member
Coursework in analysis, differential equations, computational linear algebra, signals and systems, circuits, computer systems, data structures, and econometrics