This repository includes code-related and technical comouter work I did individually or in group throughout my 4 year MEng in Biomedical Engineering at Imperial College (2015-2019). This includes work in C++, MATLAB, PCB design, COMSOL, SolidWorks and Finite-Element Analysis (FEA).

_I include in this repository all relevant courseworks or projects I completed. All practicals I completed throughout my degree are listed to show the variety and volume of subjects I was trained on. The respective files/scripts of those practicals are not included given the code from those was not entirely a product of my independent work but that of TAs and Course leaders.

Courses: Programming I and Programming II Throughout my Programming I and Programming II courses I learnt C and C++. I learnt objected-oriented programming (OOP), dealing with pointers and adresses as well as general programming concepts (for, while, if-else statements, object types...)

Courses: Signals & Systems, Control Systems, Modelling in biology, Brain Machine Interfaces, Computational Neuroscience, iGEM competition, Master's thesis

Learning Outcomes:
Relevant work: Practicals on modelling ion channels in single neurons, single neurons responses, groups of neurons through synapses and networks of neurons.

Learning Outcomes: Understand the way in which as of today the nervous system can be interfaced with electronic devices. Understanding the challenging faced when designing brain-machine interfaces.
Relevant work: Development of a script to predict hand movement of a monkey from Brain EEG signals using A Naive-Bayes Classifiers and a linear regressor. Report and code available.

Relevant work: Develop curve fitting alogrithms with optional choice of loss fonctions (RMSE, AIC, cross-entropy...) for fitting growth curve of biological organism from plate-reader experiments. Alternatively developed a script (from Alice Boo's original script) to help in the automation of diltion protocol for plate-reader experiments.

Relevant work: Modelling of a complex biological single cell system with a set of 15+ ODEs and development of curve fitting algorithms.

Learning outcomes:
Relevant work: Set of computer practicals on bifurcation analysis, solving systems of ODEs, network analysis and modelling of Markov Chains + Coursework

Learning Outcomes: Understanding the nature of signals
Relevant work: Practicals on numerical implementation of Fourier Series and Fourier transforms(continuous and discrete) with fake signal s as well as with real ECG data.

Learning outcomes: Understanding Laplace transform, Linear-Time invariant systems, block diagrams, Poles and stability, transfer function, proportional–integral–derivative(PID) controller and state feedback control.
Relevant work: Numerical implementation of the above-mentioned concepts

Courses: Biomedical Advanced and Computational Stress Analysis(BACSA), iGEM Competition

Learning Outcomes: Know and understand the principles of computational finite elemant analsysis(FEA), its advantages and drawbacks.
Relevant work: Testing (computational) and re-designing of a wheel bracket through FEA.

Relevant work: Simulation an electrochemical reaction from a circular electrode into an semi-infinite space to check the feasibility of building a real bio-electrochemical interface.

Courses: Biomedical Instrumentation and iGEM Competition

Learning outcomes: Know and understand the structure of basic building blocks (e.g.op-amps) as well as their limitations and non-idealities. Understanding how the latter affect system-wide performance.
Relevant work: Design and implementation of an op-amp based circuit for a biomedical device.

Aim: Designing an array of independent electrodes to be used as electrochemical cells (Purpose: allowing bio-electrical interface between electrodes and genetically modified Escherichia Coli.
Implementation and result: Designed and built a functional gold 14x14cm gold array with over 300 individual components.