MSc Candidate · Otto von Guericke University Magdeburg
Process simulation meets electrochemical engineering. I bridge experimental lab work and data-driven modelling to design better industrial processes and energy systems — from hydrogen production to battery diagnostics.
Chemical engineering is fundamentally about understanding how matter transforms — and I've spent the last few years building that understanding across two continents and two very different engineering cultures. My MSc at Otto von Guericke University in Magdeburg has pushed me into reaction kinetics, advanced heat and mass transfer, and electrochemical process engineering.
Before Germany, I spent a year at the Vikram Sarabhai Space Centre in India — ISRO's propulsion research hub — where I designed experiments, ran MATLAB/Simulink simulations, and learned what it means to validate engineering decisions with real data under real constraints.
My current work sits at the intersection of process engineering and energy systems: modelling hydrogen production routes, characterising electrochemical systems in the lab, and applying machine learning to battery degradation problems. I'm drawn to roles where the physics is hard and the stakes are high.
Designed a machine learning classification pipeline using the NASA battery dataset to sort retired lithium-ion cells by health state. Engineered features from voltage, current and capacity data; evaluated model performance using confusion matrices and identified capacity-related features as dominant degradation indicators.
Developed a computational process model for hydrogen production based on reaction engineering principles. Simulated system performance under varying operating conditions to identify optimal parameters and quantify efficiency trade-offs across different production routes.
Conducted controlled laboratory experiments on electrochemical systems, evaluating process variables and system efficiency under varying conditions. Systematically mapped the relationship between operating parameters and performance metrics.
Built an automated Python pipeline to process and analyse battery performance and degradation data. Developed workflows using pandas and matplotlib for reliable evaluation of charge–discharge cycles, performance trends and anomaly reporting.
Designed, built, and tested a complete battery-powered electric drive system achieving a real-world range of ~18 km. Performed efficiency analysis, load profiling and system evaluation — a full engineering cycle from concept to validated prototype.
Conducted systematic charge–discharge experiments to characterise lithium-ion cell behaviour, analysing performance trends, capacity fade, and internal resistance evolution. Results fed directly into the ML classification pipeline for second-life sorting applications.
Key modules
Foundation
I'm actively looking for process engineering or battery technical engineering internships starting August 2026. If you're working on industrial process development, energy storage, hydrogen systems, or applied ML in chemical engineering — I'd like to hear from you.