Academic Background

A complete list of all academic courses I have completed in university

My academic journey started at Chalmers University of Technology, where I pursued a Bachelor's degree in Engineering Physics studying topics such as mathematics, physics, statistics, and experimental physics. After completing my Bachelor's, I pursued a Master's program in Data Science & AI at the same institution focusing on machine learning and deep learning.

Master Program - Chalmers University of Technology (2021-2023)

Advanced studies in machine learning, artificial intelligence, and data science with applications in various domains.

Master Thesis - Chalmers University of Technology

The topic of my Master Thesis was on researching estimation methods for the model performance of deep learning models in relation to the size of its training dataset. Additionally, I studied different methods of continuous re-training techniques to improve deployed deep learning models.

Artificial Neural Networks

Fundamentals of neural networks, backpropagation, and deep learning architectures.
Stochastic Optimization Algorithms

Optimization methods for machine learning, including stochastic gradient descent and its variants.
Data Structures and Algorithms

Advanced data structures and algorithm design and analysis for efficient computation.
Simulation of Complex Systems

Computational methods for modeling and simulating complex systems across various domains.
Algorithms for Machine Learning and Inference

Algorithmic foundations of machine learning, including probabilistic graphical models and inference methods.
Computational Biology

Computational methods for analyzing biological data and modeling biological systems.
Dynamical Systems

Analysis and modeling of dynamical systems with applications in science and engineering.
Statistical Learning for Big Data

Statistical methods and computational techniques for analyzing large-scale datasets.
Advanced Machine Learning with Neural Networks

Advanced topics in deep learning, including modern architectures and training techniques.
High Performance Computing

Parallel computing, GPU programming, and optimization for scientific computing.
Image Processing

Digital image processing techniques and computer vision algorithms.
Technology and Society

Ethical, social, and economic implications of technology and AI.
Machine Learning for Natural Language Processing

Application of machine learning techniques to natural language understanding and generation.

Bachelor Program - Chalmers University of Technology (2018-2021)

Engineering Physics with focus on theoretical and applied physics, mathematics, and computational methods.

Bachelor Thesis - Chalmers University of Technology

The topic of my Bachelor Thesis was on object detection using convolutional deep learning model architectures using YoloV3. The dataset we used was synthetically generated using a Python library to simulate Mie Scattering.

Linear Algebra and Geometry

Fundamentals of vector spaces, linear transformations, eigenvalues, and geometric applications.
Introductory Mathematical Analysis

Limits, continuity, differentiation, and integration of functions of a single variable.
Computer Programming

Introduction to programming concepts using Python, focusing on problem-solving and algorithm development.
Mechanics 1

Classical mechanics including Newton's laws, work and energy, momentum, and rotational motion.
Tools of Engineering Physics

Essential tools and techniques used in engineering physics, including numerical methods and data analysis.
Multivariable Analysis

Calculus of multiple variables, partial derivatives, multiple integrals, and vector calculus.
Real Analysis

Rigorous treatment of real numbers, sequences, series, and continuity.
Linear Algebra and Numerical Analysis

Numerical methods for solving linear systems, eigenvalue problems, and matrix factorizations.
Mechanics 2

Advanced topics in classical mechanics including Lagrangian and Hamiltonian formulations.
Complex Mathematical Analysis

Theory of functions of a complex variable, complex integration, and residue calculus.
Vector Fields and Classical Physics

Application of vector calculus to classical physics problems, including fluid dynamics and electromagnetism.
Electrical Circuits and Systems

Analysis of electrical circuits, network theorems, and system responses.
Theory of Electromagnetic Fields

Maxwell's equations, electromagnetic waves, and their applications.
Fourier Analysis

Fourier series, Fourier transforms, and their applications in physics and engineering.
Optics

Geometrical and physical optics, interference, diffraction, and polarization.
Mathematical Statistics

Probability theory, random variables, statistical inference, and hypothesis testing.
Strength of Materials

Analysis of stress and strain in solid materials under various loading conditions.
Environmental Physics

Physical principles underlying environmental systems and climate science.
Structural Analysis using the Finite Element Method

Numerical methods for structural analysis using finite element techniques.
Quantum Physics

Introduction to quantum mechanics, wave functions, and quantum states.
Experimental Physics 1 - Measuring Technique

Laboratory techniques and measurement methods in experimental physics.
Thermodynamics and Statistical Mechanics

Laws of thermodynamics, kinetic theory, and statistical mechanics.
Partial Differential Equations

Analytical and numerical methods for solving partial differential equations.
Applied Quantum Physics

Applications of quantum mechanics in modern physics and technology.
Solid State Physics

Crystal structures, electronic properties, and phenomena in solids.
Experimental Physics 2 - Basis

Advanced experimental techniques and data analysis in physics.
High Frequency Electromagnetic Waves

Propagation and applications of high-frequency electromagnetic waves.
Subatomic Physics

Physics of elementary particles and fundamental interactions.