[{"title":"developer","permalink":"https://joshm21.github.io/roles/freelancer/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"Freelancer","job_title":"Developer","summary":"Develop custom full-stack applications and internal automation tools to streamline business workflows and solve complex technical challenges."},{"title":"Lozier","permalink":"https://joshm21.github.io/roles/lozier/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"Lozier","job_title":"Manufacturing Engineer","summary":"Optimized production environments through robotics integration, machine and fixture design, and process improvements to enhance both safety and efficiency."},{"title":"The Harbor","permalink":"https://joshm21.github.io/roles/haven/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"The Harbor","job_title":"Humanitarian Project Co-leader","summary":"Co-directed a community help center to serve 100+ displaced Ukrainians weekly."},{"title":"Groschopp","permalink":"https://joshm21.github.io/roles/groschopp/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"Groschopp","job_title":"Mechanical Engineer","summary":"Directed research and performance testing for industrial gearbox systems to ensure high reliability and technical compliance for motor applications."},{"title":"Iowa State University","permalink":"https://joshm21.github.io/roles/iowa-state/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"Iowa State University","job_title":"Peer Mentor Coordinator","summary":"Directed mentorship programs focused on leadership development, character growth, and academic excellence for university students."},{"title":"JFSCO","permalink":"https://joshm21.github.io/roles/jfsco/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"JFSCO","job_title":"Civil Engineer","summary":"Engineered technical solutions for rail transload facilities through precise calculations, automated design workflows, and comprehensive site drafting."},{"title":"Tutor","permalink":"https://joshm21.github.io/roles/tutor/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"Tutoring Consultant","job_title":"STEM Tutor","summary":"Empower high school and university students to master advanced mathematics and science through one-to-one and small-group tutoring."},{"title":"Dordt","permalink":"https://joshm21.github.io/roles/dordt-university-engineering-faculty/","section":"Roles","content":"","project_title":"","domains":[],"skills":[],"company":"Dordt University - Engineering Faculty","job_title":"Student Researcher","summary":"Conducted experimental research on fluidized bed biomass gasification to optimize the conversion of organic waste into sustainable energy sources."},{"title":"Robotic Fixture Design","permalink":"https://joshm21.github.io/projects/robotic-fixture-design/","section":"Projects","content":"See it in action Your browser does not support the video tag. Gallery 1 Fixture Plans Overview.png 2 Fixture Plan Detail.png 3 Plc Programming.png 4 Pneumatic Diagram.png Situation Problem: Manual clamping and welding processes were labor-intensive and physically demanding. Goal: Automate the part-holding process to improve throughput and workplace safety. Context: Operators were reporting frequent back injuries due to repetitive, awkward posturing during manual welding. Task Responsibility: Lead designer for the mechanical fixture and the control logic. Constraints: Must integrate seamlessly with existing robot arms and fit within the current cell footprint. Tech Stack: CAD software for modeling, PLC for sequencing, and pneumatic actuators. Action Design: Developed a custom pneumatically actuated clamping fixture using CAD to ensure precision alignment. Programming: Wrote PLC logic to synchronize sensors, actuators, and robot arm movements for a seamless handshaking sequence. Prototyping: Collaborated closely with the tool and die shop to fabricate the hardware. Iterating: Conducted stress testing and refined the clamping pressure and sensor positioning to eliminate part slippage. Result Efficiency: Halved labor costs by transitioning from manual to automated operation. Safety: Significantly improved ergonomics, resulting in a measurable reduction in reported back injuries. ","project_title":"Robotic Fixture Design","domains":["Mechanical Engineering"],"skills":["Robotics","Machine Design","PLC Programming","CAD Modeling"],"company":"Lozier","job_title":"","summary":"Designed and implemented a robotic welding cell with automated pneumatic clamps, halving labor costs and reducing workplace injuries."},{"title":"School Behavior App","permalink":"https://joshm21.github.io/projects/school-behavior-app/","section":"Projects","content":" Situation A school district needed a centralized way to track behavioral data for hundreds of students across multiple classrooms. Existing paper-based or disjointed systems made it difficult to identify long-term trends, manage reward \u0026ldquo;point\u0026rdquo; economies, or keep parents updated in real-time without significant manual effort from teachers.\nTask I was tasked with building a low-friction, high-impact web application that would:\nEmpower Teachers: Quick entry for positive/negative behavioral incidents. Engage Students: A portal to view \u0026ldquo;merit points\u0026rdquo; and redeem them for school rewards. Inform Parents: Automated, easy-to-read progress reports. Equip Admins: High-level analytics to monitor trends per teacher or student over time. Action I leveraged Google Apps Script as a serverless backend to minimize deployment friction and technical overhead for the district:\nRole-Based Web App: Developed a custom frontend that dynamically serves different views based on the logged-in user (Teacher, Student, Parent, or Admin). Gamified Reward Logic: Built a ledger system within Google Sheets that calculates point balances in real-time, allowing students to \u0026ldquo;spend\u0026rdquo; points on a rewards catalog. Automated Parent Reporting: Engineered a trigger-based mail merge system that sends weekly reports to parents. I implemented conditional formatting logic in the email templates (e.g., green for positive trends, red for frequent incidents) for instant legibility. Administrative Dashboards: Created filtered aggregate views for admins to track behavioral KPIs across the entire district without needing a dedicated database administrator. Result Proven Longevity: The application has remained in active production use since 2019, successfully handling years of data and user rotations with minimal maintenance. Zero-Friction Deployment: The app was deployed within the existing school Google Workspace, requiring no employee training, new software installations, or server maintenance. High Adoption: Successfully scaled to handle hundreds of students and parents simultaneously. Improved Outcomes: Admins could identify behavioral patterns weeks earlier than the previous manual system, allowing for faster intervention and more consistent positive reinforcement. ","project_title":"School Behavior App","domains":["Software Developer"],"skills":["Web Apps","Rapid Prototyping"],"company":"Freelancer","job_title":"","summary":"A full-stack web app serving teachers, students, parents, and admins to manage behavioral data and reward systems across an entire school district."},{"title":"Georgian Morphological Segmentation \u0026 Visualization","permalink":"https://joshm21.github.io/projects/georgian-verb-segmentation-visualization/","section":"Projects","content":" Situation Georgian verbs are famously complex, featuring a multi-slot morphological system where a single word form can encode subject, object, and version markers. Manually analyzing these forms is time-consuming, and standard text-based dictionaries fail to illustrate how individual morphemes shift across different grammatical categories.\nTask I was responsible for building a technical bridge between raw linguistic data and human-readable analysis:\nML Segmentation: Develop a model to automatically split verb forms into constituent parts (e.g., ga-v-aket-eb-di). Interactive Visualization: Build a web-based dashboard to align and compare these segments visually for researchers and students. Action I approached this as a two-stage engineering problem:\n1. The Segmentation Model (Back-End) I implemented a Conditional Random Field (CRF) model using sklearn-crfsuite to treat morphological segmentation as a sequence labeling task.\nState Logic: To handle Georgian orthography, I used a three-state logic—I (Inside), S (Single), and N (Null)—to account for the \u0026ldquo;empty slots\u0026rdquo; common in Kartvelian verb structures. Accuracy: The model was trained on a dataset of over 13,000 forms, learning to identify root variations and prefix/suffix changes across different screeves. 2. The Morphological Dashboard (Front-End) I built a reactive interface using Vue.js to display the model’s output in a meaningful way.\nSynchronized Alignment: Using CSS Grid, I engineered a master table that vertically aligns every grammatical slot, ensuring that roots and preverbs stay perfectly stacked across an entire paradigm. Interactive Highlighting: I added a \u0026ldquo;hover-sync\u0026rdquo; feature where interacting with a specific morpheme (like a versionizer) highlights its counterparts throughout the chart, making subtle linguistic shifts instantly visible. Result High-Precision Analysis: The model achieved ~98% word-level accuracy on complex verb forms. Visual Clarity: The dashboard successfully isolates stem developments and thematic changes that are traditionally difficult to trace in flat text. Scalable Infrastructure: This system allows for the rapid processing of massive verb databases, turning raw strings into structured, educational datasets. Related Work Live Interactive Demo NLP Portfolio ","project_title":"Georgian NLP Segmentation \u0026 Visualization","domains":["Research","Software Developer"],"skills":["Natural Language Processing","Machine Learning","Web Apps"],"company":"Freelancer","job_title":"","summary":"A specialized NLP toolset that uses Conditional Random Fields (CRF) to segment complex verb forms and a Vue.js interface to visualize morphological shifts."},{"title":"Automated Invoicing Pipeline","permalink":"https://joshm21.github.io/projects/automated-invoicing-pipeline/","section":"Projects","content":" Gallery 1 the Complex Vendor Form.png 2 the Returned Vendor Data.png 3 Results.png Situation The client’s quoting process was stalled by a massive manual bottleneck involving:\nManual \u0026ldquo;Swivel-Chair\u0026rdquo; Entry: Copying 20+ data points from a Google Sheet into a difficult 3rd-party vendor site. Complex Logic: Vendor forms included hundreds of nested options, leading to frequent errors. Slow Turnaround: Employees had to manually log in, submit, and copy results back to calculate final pricing. Task Replace the manual copy-paste workflow with an automated system to:\nCentralize Operations: Allow employees to stay within a single Google Sheet. Eliminate Latency: Reduce quote turnaround from days to hours. Ensure Accuracy: Automate data mapping to prevent costly human errors. Action I engineered an end-to-end automation suite using Google Apps Script:\nMulti-User Environment: Configured Named Filter Views and Protected Ranges to allow multiple sales employees to work simultaneously. This ensured each user only saw and edited their assigned leads, preventing data overwrites. Spreadsheet UI: Built a configuration interface with strict Data Validations for sales employees to select vendor options accurately. Headless Submission: Developed a script to programmatically authenticate and auto-complete the complex vendor form. Real-time Updates: Utilized onEdit triggers to automatically refresh vendor pricing as soon as quote parameters were adjusted. Auto-Invoicing: Created a post-processing trigger that populates a PDF invoice template, saves it to Drive, and emails the customer automatically. Result Speed: Slashed processing time from days to hours. Responsiveness: Sales team could quickly adjust and re-quote customers. Backlog Recovery: Successfully cleared a multi-month backlog of old requests. Reliability: Achieved 100% data integrity between vendor costs and customer invoices. ","project_title":"Automated Invoicing Pipeline","domains":["Automation"],"skills":["Scripting","APIs","Integrations"],"company":"Freelancer","job_title":"","summary":"Streamlined financial operations by developing an automation suite that eliminated manual data entry between spreadsheets and vendor portals."},{"title":"Paintline Optimization","permalink":"https://joshm21.github.io/projects/paintline-optimization/","section":"Projects","content":" Situation Problem: Inefficient powder utilization and frequent, unoptimized color changes were driving up material costs and reducing throughput. Goal: Increase powder transfer efficiency and streamline the scheduling of parts on the moving track. Context: The system utilized spray guns to coat charged parts on metal hangers across a continuous conveyor line. Task Responsibility: Lead data collection, nozzle calibration, and the development of a digital scheduling solution. Constraints: Must work within part batch deadlines and provide an accessible interface for floor managers. Tech Stack: VBA (Excel-based tool), ultrasonic thickness gauges, and flow control sensors. Action Data Analysis: Audited paint thickness across various part geometries and colors to identify areas of over-application. Process Tuning: Optimized spray nozzle outputs to ensure uniform coverage, reducing wasted powder and \u0026ldquo;orange peel\u0026rdquo; defects. Software Development: Built a custom VBA tool that allowed managers to input part batches and constraints. Optimization: Programmed the tool to minimize high-cost color changes and part-size transitions while meeting strict delivery timeframes. Result Financial Impact: Netting $20,000+ in annual savings through reduced powder waste and improved labor efficiency. Consistency: Achieved high-uniformity coating standards, reducing rework by stabilizing paint thickness. ","project_title":"Paintline Optimization","domains":["Mechanical Engineering","Software Developer"],"skills":["Scripting","Optimization","Testing","Data Analysis"],"company":"Lozier","job_title":"","summary":"Optimized powder utilization and developed a custom scheduling tool for a paint line, netting over $20k in annual savings."},{"title":"Drafting Automations","permalink":"https://joshm21.github.io/projects/drafting-automations/","section":"Projects","content":" Gallery 1 Wrote Code to Automatically Mark Clear Points.png 2 Wrote Code to Automatically Draw Where Underground Utilities Crossed Rail Alignment.png Situation Problem: Manual drafting workflows for large-scale civil projects were prone to human error and consumed excessive billable hours. Goal: Create a robust set of \u0026ldquo;one-click\u0026rdquo; tools to handle complex geometric exports and bulk text edits within the AutoCAD environment. Context: Specialized tasks like railway turnout extraction and coordinate data exporting required custom logic not found in the standard Civil3D toolkit. Task Responsibility: Design, code, and document a collection of productivity scripts to be used by the wider drafting team. Constraints: Tools had to be compatible with legacy AutoCAD versions and support external data formats (CSV/TXT) for seamless integration with field surveys. Tech Stack: AutoLISP for core CAD manipulation and VBA/Instructions for data management. Action Data Automation: Developed scripts to automatically draw and export point data, eliminating manual entry and ensuring coordinate accuracy between the model and field files. Geometric Specialized Tools: Built a custom Turnout Data Export utility to handle specialized rail geometry calculations and reporting. Bulk Processing: Created an Incremental Text Replacement tool to automate sequential labeling and systematic text updates across entire drawing sets. Documentation: Authored comprehensive Work Instructions for each utility to ensure consistent application and reduce the learning curve for new users. Result Speed: Reduced data extraction and export times by approximately 80% compared to manual methods. Accuracy: Eliminated transcription errors by automating the flow of data from the drawing space to external utility reports. Scalability: The tools allowed the team to handle significantly larger datasets without increasing the drafting headcount. ","project_title":"Drafting Automations","domains":["Automation"],"skills":["Scripting","CAD Modeling"],"company":"JFSCO","job_title":"","summary":"Streamlined financial operations by developing an automation suite that eliminated manual data entry between spreadsheets and vendor portals."},{"title":"Biomass Gasification Research","permalink":"https://joshm21.github.io/projects/biomass-gasification-research/","section":"Projects","content":" Situation The Problem: Biomass and municipal solid waste (MSW) are underutilized energy sources due to high tar production and low conversion efficiency. The Goal: Optimize a 75 KWth bubbling fluidized bed (BFB) gasifier to handle diverse feedstocks (industrial/agricultural waste). Environment: Research conducted in the on-site bay using a system funded by a $60,000 NSF EPSCoR grant. Task Design: Integrate a biogas burner into the existing gasification infrastructure. Analyze: Evaluate how feedstock variation and reactor hydrodynamics impact cold-gas efficiency. Maintain: Ensure project continuity through rigorous data documentation for faculty and future researchers. Action Hardware Engineering: * Designed and installed improvements to a biogas burner as part of a collaborative engineering team. Experimental Testing: * Conducted parametric experiments focusing on temperature, bed composition, and fluidization hydrodynamics while analyzing gas chromatography. Data-Driven Optimization: * Identified correlations between feed system configurations and tar production levels. Knowledge Management: * Drafted technical reports and findings for the Engineering Department Chair. Result Efficiency Gains: Identified specific operating parameters that maximized cold-gas efficiency while minimizing tar. System Capability: Successfully upgraded the research platform with a functional burner for synthesis gas combustion. Academic Impact: Created a foundational dataset and documentation library used by subsequent student research teams. ","project_title":"Biomass Gasification Research","domains":["Mechanical Engineering","Research"],"skills":["Machine Design","Testing","Technical Writing","PLC Programming"],"company":"Dordt University - Engineering Faculty","job_title":"","summary":"Engineered solutions to increase cold-gas efficiency and reduce tar formation in a 75 KWth bubbling fluidized bed reactor using agricultural waste."},{"title":"Contact","permalink":"https://joshm21.github.io/contact/","section":"","content":"","project_title":"","domains":[],"skills":[],"company":"","job_title":"","summary":""},{"title":"Search","permalink":"https://joshm21.github.io/search/","section":"","content":"","project_title":"","domains":[],"skills":[],"company":"","job_title":"","summary":""}]