Oseremen Ojiefoh

Every year millions of pets go missing in the United States, yet the process for reuniting them with owners is fragmented across paper flyers, Facebook groups, and disconnected shelter databases. DogTag is a mobile platform designed to close that gap.

• Designed core product flow: photo-based pet reporting, automated geofenced alerts to nearby shelters and volunteers, and a secure in-app messaging layer between owners and rescuers.
• Scraped and normalized public shelter databases to seed a live lost/found index, significantly reducing duplicate manual entry for users.
• Conducted structured customer discovery interviews with 45 pet owners and 20 shelters/rescue volunteers, using findings to prioritize MVP features and de-scope lower-value flows.
• Reached the finalist round of the 2024 Skandalaris Venture Competition, pitching the business model, market size, and technical architecture to a panel of investors and faculty judges.

A common frustration in math and science education is the gap between a student's written work and meaningful, immediate feedback. Index addresses this by turning a phone or tablet into a real-time tutor that reads handwritten problem sets.

• Built the initial MVP during the Skandalaris Hackathon (Fall 2025), validating the core workflow from handwritten capture to AI-generated feedback.
• Competed in Ideabounce's Open Mic category (Fall 2025), networked with judges, and secured a MacBook donation to support ongoing development.
• Originally enrolled in the Skandalaris Venture Competition, then intentionally stepped back to refocus on customer discovery and problem selection before scaling engineering effort.
• After the donated MacBook proved too old for practical native iOS development, we purchased a newer used MacBook to unblock the product roadmap.
• Took INdex through Olin's entrepreneurship ecosystem to refine customer discovery, product positioning, and go-to-market framing.
• Network and support: ongoing guidance from Cyril Loum (Director of Venture Development, Skandalaris), connection with Will Blanchard (CEO, former Senior PM / VC Associate, WashU alum) via McKelvey CNX, and an upcoming conversation with Gabby Cazeau (Partner, Harlem Capital) focused on enterprise software and applied AI.
• Current progress direction: restarting intentionally from scratch, defining critical hypotheses first, then running short customer-discovery test cycles to shape differentiated MVP features for underserved student segments.
• Active learning hypothesis set includes: whether students want hint-first AI walkthroughs, whether handwriting remains preferred for step-based STEM work, and whether real-time nudge UX is preferred over existing note-taking and AI study tools.
• Near-term validation plan: run quick Figma click-demos and controlled user tests, collect survey outcomes across target demographics, and iterate toward a minimal-but-rich MVP before native iOS build-out.
• Building an OCR + LLM pipeline that ingests a photo of handwritten student work, identifies the problem type, traces the student's steps, and returns targeted step-by-step guidance rather than just the answer.
• Designing an immersive digital-notebook UI modeled on GoodNotes and Notability, prioritizing low-latency ink rendering and intuitive annotation gestures.
• Evaluating cross-platform frameworks (Flutter, React Native, Swift/SwiftUI) for performance trade-offs in ink latency, camera access, and on-device ML inference.
• Prototyping the feedback engine with GPT-4o vision for early validation before fine-tuning a smaller model for cost and latency at scale.

Built a fully playable browser-based video game controlled entirely through hand gestures, with no physical controller, during a 36-hour hackathon.

• Used MediaPipe Hands to detect and track 21 hand landmarks in real time via webcam, mapping index-finger tip coordinates to in-game movement inputs at ~30 fps.
• Integrated OpenCV for frame capture, preprocessing, and visual feedback overlay, ensuring robust performance under varying lighting conditions.
• Collaborated in a 4-person team, owning the gesture-to-input mapping layer while teammates handled game logic and UI rendering.
• Presented the live prototype to judges; awarded 1st place in the Emerging Track at Hack WashU 2024.

Designed and implemented a closed-loop autonomous driving system on a Raspberry Pi-based vehicle platform from the ground up.

• Implemented and hand-tuned PID controllers for motor speed regulation, iteratively adjusting proportional, integral, and derivative gains to minimize overshoot and steady-state error under varying surface friction.
• Fused data from a photoresistor (ambient light), an ultrasonic sensor (obstacle detection/ranging), and an onboard camera to enable reactive lane-keeping and collision avoidance.
• Validated actual vs. commanded wheel speed using FFT analysis of encoder pulse signals, diagnosing resonance artifacts and confirming control bandwidth.
• Implemented real-time object detection and color tracking using OpenCV, enabling the vehicle to autonomously follow a designated target at a constant standoff distance.

Designed and built a self-managing raised-bed garden that monitors soil conditions and waters itself without human intervention, powered by solar energy.

• Wired capacitive soil-moisture sensors to Arduino analog inputs and wrote firmware to trigger a solenoid-controlled water valve when moisture fell below configurable thresholds, preventing both under- and over-watering.
• Integrated a solar panel + LiPo charge management circuit to keep the system off-grid indefinitely, including low-power sleep modes between sensor polling cycles to extend battery life.
• Added a DHT22 temperature/humidity sensor and logged readings via serial to a local data store for seasonal analysis of plant health.
• Designed the wiring harness and enclosure for outdoor weather exposure, applying conformal coating to moisture-sensitive boards.

Contributed to a NASA-affiliated research project applying computer vision to classify landscape types in ground-level directional photographs for use in climate and environmental monitoring models.

• Fine-tuned a ResNet-18 CNN (PyTorch) on a labeled dataset of sky, land, and water imagery captured across diverse geographic environments, achieving 92–95% test accuracy across the three-class problem.
• Built and managed a Zooniverse crowdsourcing pipeline to scale data labeling beyond what the core team could annotate, coordinating volunteer contributors and resolving label conflicts via majority-vote aggregation.
• Contributed written sections, quantitative figures, and LaTeX formatting to the research paper "Uniting Machine Learning and Citizen Science for Automatic Land Cover Classification."
• Presented findings at the AGU Fall 2023 Meeting (American Geophysical Union) — one of the largest Earth science conferences globally.
• Returned as a team mentor the following cohort, leading project coordination, Discord community management, and supporting new interns through the research workflow.

Independent Stave Company (the world's largest barrel manufacturer) manually counts wooden staves on pallets for inventory and quality control — a slow, error-prone process. I was brought on to automate it with computer vision.

• Evaluated and benchmarked two neural network architectures — CountGD (a grounding-based counting model) and Annolid (an instance segmentation tracker) — for accuracy and inference latency on high-resolution pallet imagery.
• Built the full data pipeline: raw image ingestion, perspective correction, OpenCV-based preprocessing (contrast enhancement, denoising), and structured annotation using LabelMe for model training.
• Trained and validated models on an internal dataset of stave pallets, iterating on bounding-box vs. point-based annotation strategies to improve recall on partially occluded staves.
• Delivered a working inference pipeline projected to eliminate hundreds of thousands of dollars in annual manual counting labor.

• Epidemiological modeling: Calibrated SIRD and SIRDVB compartmental models in MATLAB using fmincon to fit real COVID-19 case data, then used the calibrated parameters to project the impact of different vaccination rollout strategies and intervention timing on peak case counts.
• Image classification: Built and compared MNIST digit classifiers (logistic regression baseline vs. fully connected neural network), evaluating performance with confusion matrices and per-class precision/recall.
• Unsupervised learning: Applied K-means clustering to county-level COVID trajectory data to identify regions with similar epidemic curves, assessing cluster stability via silhouette scores.
• Signal processing: Designed and analyzed passive RC filter circuits, simulated frequency response, and developed a composite Butterworth low-pass + notch filter pipeline for de-noising biomedical signals with 60 Hz power-line interference.

Explored the integration of AI-powered note-taking into a HoloLens 2 mixed-reality application targeting medical and dental clinical workflows.

• Investigated HoloLens 2 SDK (MRTK3) and spatial mapping APIs to understand feasibility of anchoring UI elements to physical clinical environments.
• Prototyped a voice-to-text and contextual annotation layer using Azure Cognitive Services, allowing clinicians to dictate and tag observations hands-free during procedures.
• Researched LLM integration for auto-summarizing session notes into structured clinical documentation formats.

Enrolled in the McKelvey School of Engineering. Coursework spans circuit design, signals & systems, embedded systems, and data science, alongside pre-med requirements in biology and chemistry.

Key courses: ESE 205 (Systems Lab — robotics & controls), ESE 326 (Signals, Systems & Data Science), Engineering Science, Biology, Chemistry.

Activities: Taylor STARS, Developer's Student Club (DSC), National Society of Black Engineers (NSBE), Black Men's Coalition, Minority Association of Premedical Students (MAPS), Intramural Soccer, Intramural Basketball.

GPA: 3.941 UW / 5.007 W  ·  Top 25%  ·  SAT Superscore: 1510  ·  AP Scholar with Distinction

AP courses: AP Computer Science A, AP Calculus BC, AP Physics, AP Chemistry, AP English, AP U.S. History.

Activities: Business Professionals of America (BPA) — Community Service Chair, Java Programming & Financial Portfolio Management competitor; Black Student Alliance; Student Ambassador; Texas Boys State Delegate (2023).

GPA: 4.000 UW / 4.975 W  ·  Class Rank: 26 / 624

Notable coursework: CAD (Autodesk Inventor — earned Certified User credential), Engineering Science, Computer Science II (version control, web design). Transferred to Rick Reedy for junior year.

Completed the competitive NASA SEES internship as a researcher, then returned as a returning mentor to support the next cohort.

• As intern: led the land-cover ML pipeline, co-authored research paper, and presented at AGU Fall 2023.
• As mentor: coordinated team assignments, organized the program Discord server and shared Google Drive, facilitated weekly sync meetings, and guided new interns through dataset preparation and model training workflows.
• Supported junior researchers in preparing their own conference presentations and written reports.

• Served as Community Service Chair, organizing volunteer events and tracking member service hours.
• Competed in Java Programming at the state level.
• Competed in Financial Portfolio Management — placed 5th nationally and reached the top-10 presentation finalist round at the BPA National Leadership Conference.

Selected as a delegate to the American Legion's Texas Boys State program — a week-long civic leadership simulation where students form mock governments and run for elected office.

• Served concurrently as Precinct Chairman, County Chairman, District Co-Chairman, and State Party Delegate.
• Ran a campaign for Commissioner of the General Land Office on the Nationalist Party ticket, delivering speeches and building coalition support among delegates from across Texas.

Co-organized a team, coordinated scheduling and attendance, and led team to finish as the 2nd seed in the division.

Held active roles across three student organizations simultaneously: Community Service Chair (BSA), general member (Multicultural Club), and official Student Ambassador representing the school to prospective students and families during campus visits.

Participated in a community volunteer day at Bonton Farms, an urban-agriculture nonprofit in South Dallas that uses farming to address food insecurity and provide job training in an underserved neighborhood.

• Assisted with produce harvest, sorting, and packaging for community distribution.
• Supported livestock care duties alongside full-time farm staff.