Meshach Hopkins

Electrical & Computer Engineer | Robotics, Computer Vision, & Digital Twins
Philadelphia, Pennsylvania  |  mhopkins@andrew.cmu.edu  |  (410) 501-7680

Technical Focus

Computer Vision, Nonlinear Optimization, Power Systems, Robotics, State Estimation, and Digital Twin Systems

Education

Master of Science in Electrical and Computer Engineering
Carnegie Mellon University
ECE QPA: 3.66 | Robotics QPA: 3.83
Coursework: Applied Stochastic Processes; Optimal Control and Reinforcement Learning; Adaptive Control and Reinforcement Learning; Circuit Simulation and Optimization
2019–2024
Bachelor of Science in Computer Engineering
University of Maryland, Baltimore County
Cumulative GPA: 3.51 | Final two years: 3.75
2015–2019

Technical Skills

Programming: Python, PyTorch, Cython, C, C++, MATLAB, JavaScript, HTML, CSS, React, Verilog, Assembly
Engineering & Robotics: ROS, COMSOL Multiphysics, Simulink, MATPOWER, Fusion 360, Blender
Systems & Web: PostgreSQL, NGINX, Linux/Ubuntu, headless server administration, full-stack development

Research Experience

Johns Hopkins Applied Physics Laboratory
May 2019 – May 2025
Graduate Summer Intern (2 years) & On-Call Team Member
  • Designed and implemented autonomous trajectory-following and landmark-processing algorithms for the Boston Dynamics Spot robot.
  • Applied a vision-based, neuro-inspired ring-attractor network state estimator to motion planning on a mobile robotic platform.
  • Developed a custom ROS simulation environment for a swarm robotics competition and integrated SLAM and path-planning algorithms in simulation and on physical TurtleBots.
  • Taught undergraduate interns foundational ROS and robotics concepts while supporting rapid prototyping and system integration.
Power Systems Lab & Digital Twins Research Group, Carnegie Mellon University
Aug. 2021 – Aug. 2024
Graduate Researcher
  • Researched energy-storage and microgrid digital twins for security, resource management, and emergency-response applications.
  • Reformulated optimal power flow for stochastic microgrid networks and applied deep learning to battery-degradation estimation and renewable-generation forecasting.
  • Used MATLAB, MATPOWER, Simulink, and COMSOL for modeling, experimentation, validation, and comparative analysis.
ECLIPSE Research Cluster, University of Maryland, Baltimore County
May 2016 – Nov. 2017
Undergraduate Research Volunteer / Assistant
  • Supported hardware-security research using side-channel analysis and statistical signal processing to classify microprocessor instruction execution.
  • Configured FPGAs, wrote serial I/O code and Assembly test programs, and modified open-source graphical interfaces.
Interdisciplinary Program in High Performance Computing, UMBC
June 2015 – Aug. 2015
Summer Undergraduate Research Intern
  • Analyzed associations between gene expression and Alzheimer’s disease incidence using principal component analysis and sufficient dimension reduction in a parallel-computing framework.
  • Presented findings at UMBC’s annual Summer Undergraduate Research Fest.

Selected Publications & Presentations

IEEE Power & Energy Society General Meeting
July 2024
Multi-Period Three-Phase AC Optimal Power Flow Using Differential Dynamic Programming
Selected for oral presentation based on submitted abstract.
IEEE AI/ML for Multi-Domain Operations Applications
2023
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for More Robust, Efficient, and Adaptable Artificial Intelligence
Acknowledged contributor.
Joint Mathematics Meetings
Jan. 2016
Statistical Analysis of a Case-Control Alzheimer’s Disease: A Retrospective Approach with Sufficient Dimension Reduction
Selected for oral presentation and submitted paper.