AI/ML Engineer · Researcher · Toledo, Ohio
Making machine learning work on signals that don’t behave
I build ML systems for physical processes — furnaces, hip scans, roads, microscopes — where the measurement is as hard as the model. I’m an ML engineer at the Northwest Ohio Innovation Consortium, partway through a master’s in computer science engineering at the University of Toledo.
- 3 Published papers Springer Nature · IEEE · UPenn-validated
- 1 Granted patent Flower Plucking Robotic Arm, IN 573184
- 6 Featured projects industrial · medical · transport
- 3.92 GPA University of Toledo
About
I work on the machine learning that has to survive contact with hardware.
I build machine learning systems for physical processes — furnaces, hip scans, roads, microscopes — where the measurement is as hard as the model. The through-line is hybrid fuzzy–deep learning: a fuzzy layer for the ambiguity, a deep model for the structure underneath it.
Selected work
Six projects I built against real data
Each one states its limits as plainly as its results.
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Northwest Ohio Innovation Consortium
AI digital twin for a glass fiber furnace
Thermocouples submerged in molten glass drift, degrade and fail, leaving operators blind on the variable that controls fiber diameter, breakage and energy cost. A virtual sensor keeps reading when the physical one stops.
4.37°FRMSE, middle of melt at 100 TPD
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MS thesis research, University of Toledo
Hip osteoporosis risk from CT imaging and clinical data
Osteoporosis usually goes undiagnosed until a fracture happens. This work inherited a prior pipeline, audited it end to end, and found that its headline result did not survive honest evaluation.
0.976DiceU-Net bone segmentation
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University of Toledo, Dept. of EECS
Fuzzy deep neural network for brain tumor classification
Tumour margins are soft and blurred, which is exactly where hard clustering fails. Fuzzy C-Means segments the ambiguous boundary and a CNN classifies what it finds.
95.8%overall accuracy
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University of Toledo
Fuzzy deep LSTM for cancer cell death analysis
Studying how cancer cells die normally requires fluorescent labelling, which is invasive and can alter the biology being observed. This asks whether death patterns can be recognised from raw, unlabelled microscopy video alone.
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Ohio Department of Transportation
Traffic demand forecasting
With only seven years of counts, time-series forecasting is the wrong frame. Normalising the origin-destination matrix by population turns the task into learning a spatial assignment operator that can be re-expanded against any year.
8.92%MAPE across 919 count stations
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Independent work, then Ohio Department of Transportation
Vehicle speed estimation from video
Built first independently — roadside video capture, detection, tracking and speed derived from tracked movement, with real-time inference on a Raspberry Pi at the roadside. Then integrated into ODOT’s existing statewide detection network.
Speed accuracy was never documented
Tech stack
The tools I reach for
Fuzzy–deep learning at the centre; the rest is what it takes to get a model in front of someone who can act on it.
learning
- Python
- TensorFlow
- PyTorch
- Keras
- Scikit-Learn
- OpenCV
- FastAPI
- React
- pandas
- NumPy
- Docker
- Git
- Vite
- PostgreSQL
- Streamlit
Contact
Get in touch
I’m looking for full-time ML engineering and research roles from Summer 2027, and for internships before then. The fastest way to reach me is email.