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Hello, I’m Esi Afariwa Otoo, a Mechanical Engineering MASc candidate at McMaster University specializing in applying deep learning to energy systems. I designed and trained a physics-informed neural network (PINN) controller that achieved zero DC-bus violations across four drive cycles and temperatures, and I’m comfortable across the full ML lifecycle from dataset curation to deployment in closed-loop systems. I’m seeking ML Engineer, Applied Scientist, or Data Scientist roles where strong mathematical foundations meet real-world domain data in production ML. I thrive in cross-disciplinary teams and enjoy turning sensor data from CAN buses and test benches into reliable, impactful models.…Hello, I’m Esi Afariwa Otoo, a Mechanical Engineering MASc candidate at McMaster University specializing in applying deep learning to energy systems. I designed and trained a physics-informed neural network (PINN) controller that achieved zero DC-bus violations across four drive cycles and temperatures, and I’m comfortable across the full ML lifecycle from dataset curation to deployment in closed-loop systems. I’m seeking ML Engineer, Applied Scientist, or Data Scientist roles where strong mathematical foundations meet real-world domain data in production ML. I thrive in cross-disciplinary teams and enjoy turning sensor data from CAN buses and test benches into reliable, impactful models.

Esi Afariwa Otoo

Data Scientist, AI Engineer, AI Developer, +2

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Hello, I’m Esi Afariwa Otoo, a Mechanical Engineering MASc candidate at McMaster University specializing in applying deep learning to energy systems. I designed and trained a physics-informed neural network (PINN) controller that achieved zero DC-bus violations across four drive cycles and temperatures, and I’m comfortable across the full ML lifecycle from dataset curation to deployment in closed-loop systems. I’m seeking ML Engineer, Applied Scientist, or Data Scientist roles where strong mathematical foundations meet real-world domain data in production ML. I thrive in cross-disciplinary teams and enjoy turning sensor data from CAN buses and test benches into reliable, impactful models.…Hello, I’m Esi Afariwa Otoo, a Mechanical Engineering MASc candidate at McMaster University specializing in applying deep learning to energy systems. I designed and trained a physics-informed neural network (PINN) controller that achieved zero DC-bus violations across four drive cycles and temperatures, and I’m comfortable across the full ML lifecycle from dataset curation to deployment in closed-loop systems. I’m seeking ML Engineer, Applied Scientist, or Data Scientist roles where strong mathematical foundations meet real-world domain data in production ML. I thrive in cross-disciplinary teams and enjoy turning sensor data from CAN buses and test benches into reliable, impactful models.

Available to hire

Hello, I’m Esi Afariwa Otoo, a Mechanical Engineering MASc candidate at McMaster University specializing in applying deep learning to energy systems. I designed and trained a physics-informed neural network (PINN) controller that achieved zero DC-bus violations across four drive cycles and temperatures, and I’m comfortable across the full ML lifecycle from dataset curation to deployment in closed-loop systems.

I’m seeking ML Engineer, Applied Scientist, or Data Scientist roles where strong mathematical foundations meet real-world domain data in production ML. I thrive in cross-disciplinary teams and enjoy turning sensor data from CAN buses and test benches into reliable, impactful models.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

Instrumentation & Data Quality Teaching Assistant at McMaster University – Dept. of Mechanical Engineering

January 1, 2025 - Present

Taught measurement-system design, signal processing, and sensor calibration to 100+ students; standardized acceptance checks and SOPs reduced setup errors by 25%. Diagnosed sensor drift, bias, and noise across multiple sensor types, building intuition for data quality and distribution shift.

Research Assistant – Machine Learning & Physics-Informed Modelling at McMaster Automotive Resource Centre (MARC)

September 1, 2024 - Present

Designed and trained a PINN surrogate for real-time energy management of a battery-ultracapacitor hybrid system; achieved RMSE 0.0442, R2 0.87 on held-out WLTP data with zero DC-bus constraint violations across UDDS, HWFET, US06, and WLTP cycles and temperature range -20°C to +40°C. Implemented and benchmarked iTransformer for Li-ion battery voltage estimation; built an end-to-end ML data pipeline from instrumented test bench to CAN bus sensor data on AVL LYNX hardware; deployed in closed-loop control. Combined ML with dynamic programming to deliver a deployable controller achieving 60% reduction in Li-ion aging at peak temperature; developed physics priors via electro-thermal (Thevenin ECM) models in Simulink to train/validate the PINN.

Mechanical Engineer Intern – Infrastructure Monitoring at Ghana Water Company

May 1, 2023 - December 1, 2023

Implemented condition-based monitoring across 10+ infrastructure sites; contributed to a 15% reduction in unplanned downtime. Conducted functional verification and signal loop checks on 10+ electromechanical assemblies, reinforcing systems-thinking behind reliable ML deployment.