CV

Postdoctoral Research Associate

Imperial College London

• Design and implement modular computational and machine-learning research frameworks in Python and PyTorch for data-intensive biological and medical research.
• Develop reusable scientific-computing infrastructure supporting numerical modelling, machine learning, large-scale analysis, and reproducible workflows across neuroscience and breast-cancer projects.
• Translate mathematically grounded models into scalable implementations with automated testing, documentation, and CI/CD.
• Collaborate with computational scientists, experimental researchers, and engineers to turn scientific questions into robust models and software systems.

PhD Researcher

University Hospital Bonn

• Designed PyTorch pipelines using U-Net and Fast R-CNN architectures for biological feature detection, segmentation, and quantitative microscopy analysis.
• Developed Python image-analysis methods for tracing neuronal processes, detecting fluorescent puncta, and characterising neuronal morphology.
• Built reusable packages and end-to-end workflows integrating data ingestion, numerical methods, simulation, image processing, optimisation, and automated evaluation.

PhD Researcher

University of Mainz Medical Center

• Developed quantitative models of molecular transport, receptor trafficking, and synaptic processes using differential equations, numerical simulation, optimisation, and data-driven modelling.
• Connected mathematical models with experimental measurements to investigate neuronal signalling and synaptic plasticity.
• Maintained reproducible computational workflows and documentation for interdisciplinary research teams.

Junior Research Assistant

National Centre for Biological Sciences / Institute for Stem Cell Science and Regenerative Medicine

• Developed distributed Python pipelines for large-scale parameter optimisation on HPC infrastructure.
• Optimised biochemical reaction-network models containing hundreds of reactions and thousands of parameters.
• Built REST APIs, data-processing infrastructure, and scientific web applications for biological datasets.

PhD, magna cum laude

Rhenish Friedrich Wilhelm University of Bonn

Neuroscience, computational and machine-learning focus

M.Tech.

Indian Institute of Technology Kanpur

Biological Sciences and Bioengineering

B.E., with distinction

Institute of Engineering and Technology, Indore

Computer Engineering

Recurrent Network Models of Systems Consolidation and Representational Drift

Manuscript submitted

• Developed a multi-region recurrent rate-based neural network of hippocampal and cortical populations to investigate memory consolidation and representational drift.
• Modelled Hebbian plasticity, recurrent and feed-forward connectivity, intrinsic excitability, and activity-dependent intrinsic plasticity.
• Designed in-silico perturbations and analysed ensemble recruitment, connectivity, population activity, and population-vector correlations.
• Demonstrated how transient intrinsic plasticity can stabilise cortical memories despite ongoing hippocampal representational drift.

Computational Modelling of Learning-Evoked Oscillatory Dynamics

Developing a model of how learning-evoked slow oscillations may organise population activity and offline memory reactivation in the human medial temporal lobe.