About
I’m a highly motivated AI/ML engineer with a multidisciplinary background in machine learning, data science, and mathematical modelling. I hold a PhD in Mathematics and build neural network surrogate models and Bayesian inference pipelines for lithium‑ion battery systems. I enjoy shipping clean, production‑ready code and collaborating in cross‑functional teams.
Core Stack
Python, R, SQL, PyTorch, NumPy/SciPy, GPyTorch, pandas, scikit‑learn, Tableau, Power BI, Excel, JavaScript, HTML/CSS, MATLAB, Maple, Fortran, C, PyBaMM, PyBOP.
Also: PSO, MCMC/Metropolis, Bayesian UQ, Gaussian Processes, physics‑informed ML.
Domains
Battery modelling (ECM & physics‑based), parameter estimation, uncertainty quantification, energy systems analytics, data visualisation, teaching & public engagement.
AI/ML Batteries Bayesian Data VizExperience
Data Analyst Intern · Victoria Solution (2025)
London, UK
- Advanced analysis with Python/R/SQL; built interactive dashboards in Tableau & Power BI.
- Sourced, cleaned and structured large datasets; presented insights to stakeholders.
- Collaborated with industry mentors and data teams; delivered findings in feedback meetings.
Research Assistant · University of Portsmouth (2024–2025)
- PSO + Bayesian inference with neural surrogates for parameter estimation (custom Python).
- Applied PSO to inverse problems; MLE & MCMC (Metropolis) for UQ.
- Built Li‑ion models with PyBaMM/PyBOP; integrated experiments and numerics in Git‑driven workflows.
PhD Researcher · Battery Modelling & Surrogates (2021–2024)
- Developed scalable neural surrogate models for Li‑ion batteries enabling ultra‑fast Bayesian inference.
- Physics‑informed & supervised learning pipelines; Gaussian Processes with GPyTorch.
- Reduced‑order blended‑electrode model (MATLAB finite volume) for control‑oriented simulation.
- Collaborations with The Faraday Institution; national conferences and publications.
Selected Projects
BatteryGuard AI (Demo)
AI‑powered health check for second‑life Li‑ion batteries. Includes model parameter inference via PSO + neural surrogates and an educational microsite.
Open demo section GitHubEV Population Dashboard (Tableau)
Interactive dashboard exploring EV population in Washington State — trends by brand, geography and registration year based on open data.
View TableauBatteryGuard AI — Interactive Mini Demo
For GitHub Pages, backend calls are disabled. This simulated demo previews the UI flow only.
Upload CSV (simulated)
What it does
- Fits a Thévenin ECM to current/voltage data.
- Uses a trained NN surrogate + PSO search.
- Reports R0, R1, C1 and notes on fit quality.
Publications (selected)
- Leveraging machine learning in porous media, Journal of Materials Chemistry A, 2024.
- Multiple papers on solar desalination and energy systems in Solar Energy, IJEST, and Environmental Science and Pollution Research, 2022.
- In preparation (2025): Bayesian inference for Thévenin ECM; Reduced‑order physics‑based model with blended electrodes.
Teaching
- Lecturer (Quantitative Methods, Logistics, Mathematics) — International College Portsmouth (2023–present)
- Teaching Assistant (Python Programming; Maths for Finance; Derivative Pricing) — University of Portsmouth (2022–2023)
- Maths Tutor — University of Portsmouth (2022–2024); Exam Invigilator (2021–2024)
Awards & Memberships
- AFHEA (2025); IEEE Member; LMS Associate Member; IMechE Affiliate; RSS Member.
- IGNITION Responsible AI Hackathon — Finalist (3rd place runner‑up), AWS credits awarded.
- Global PhD Scholarship (University of Portsmouth); Association of Inventors Member.
PhD, Mathematics — University of Portsmouth (2021–2025)
Dissertation: Neural Network Surrogates for ultra‑fast Inference in Li‑ion battery models.
MSc, Mechanical Engineering — Sharif University of Technology (2019–2021)
Top 2% on entry; GPA 18.53/20. Dissertation on solar desalination systems.
BSc, Mechanical Engineering — Babol Noshirvani Univ. of Technology (2014–2018)
Top 8% (3rd Top Student). Dissertation on bio‑inspired underwater robots.
Contact
Email Benyamin.ebrahimpour@port.ac.uk
Location Southsea, Portsmouth, UK
Links LinkedIn · Google Scholar · GitHub