Computer Science Student @ EPFL • Software & AI Engineering • Finance & Data
Bridging Technology, Data, and Strategy. Learning Fast. Delivering Impact.
A Computer Science student at EPFL with hands-on experience in software engineering, AI/ML, quantitative analysis, and project management. Passionate about designing practical, scalable solutions that connect technology with real-world needs, across industries such as finance, engineering, and consulting. Driven by curiosity and a desire to learn, build, and contribute to impactful projects.
My Journey & Impact
I am a Communication Systems student at EPFL with a strong interest in solving complex problems through technology, data, and thoughtful design. My experience spans software development, machine learning, quantitative finance, and project management allowing me to work comfortably across technical, analytical, and strategic challenges. I enjoy turning ideas into real systems, whether through building ML models, developing full-stack applications, optimizing financial strategies, or managing multidisciplinary consulting projects. My curiosity drives me to explore diverse fields, from algorithms and systems to applied AI and business innovation. I’m motivated by challenges where I can learn quickly, collaborate with talented teams, and create solutions that have a tangible impact in tech, finance, engineering, or consulting.
Technical, Consulting & Financial Competencies
Work Experience & Internships
Remote
Madrid
Rabat, Morocco
Lausanne
A collection of my work and experiments
How do you democratize access to professional-grade portfolio optimization tools? Discover SiraEdge, a comprehensive platform combining 7 advanced models with educational resources.
A machine learning system for predicting short-term mid-price moves from order book features. Features comprehensive order flow analysis, multiple ML models (LightGBM, LSTM, Transformer), and professional backtesting framework with transaction costs simulation.
An educational project teaching machine learning fundamentals through practical applications in financial market prediction. Features K-means clustering, regression models, and ensemble methods with comprehensive learning resources.
Movie discovery platform featuring a hybrid recommendation engine that fuses movie metadata, viewing histories, social activity, and popularity signals to serve cinematic suggestions that evolve with every interaction.
Multi-theme analyst workspace with a refined Surseoir-inspired UI that lets teams browse financial PDFs, capture figures, and interrogate an on-page AI copilot powered by OCR, PDF.js rendering, and a custom vision pipeline.
A habit-tracking application designed to help users build lasting habits through streak-based motivation. Features include advanced analytics, personalized challenges, collaborative streaks, and smart integrations with health apps.
Co-founding a seasonal delivery service in tourist areas. Processing 500+ deliveries while maintaining 95% customer satisfaction through streamlined logistics.
Research about mathematical and physical models for short-term FX market movements to predict fluctuations. Presented during Baccalaureate oral exam.
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A platform that helps users find partners for group projects, facilitating collaboration through shared interests and skill matching with an integrated database.
Designed an app to help users track their daily nutrition intake, set dietary goals, and maintain a healthy lifestyle with personalized recommendations.
Building a contemporary streetwear brand from the ground up. From product development to reaching 1,000+ customers, lessons in entrepreneurship and brand building.
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A collection of diverse small-scale projects, including algorithms, web development demos, and experimental applications showcasing creativity and technical skills.
View on GitHubAcademic Background & Achievements
2022 - Present
2023 - Present
2019 - 2022
Education Nationale · May 2021
Lycée Descartes · 2020-2022
Academic Coursework & Self-Study
MATH-232
Core for modeling uncertainty, risk, stochastic processes — used in pricing, risk analysis, data science. Comprehensive foundation in probability theory and statistical analysis essential for quantitative finance and machine learning. Covers key concepts from basic set theory and combinatorics to advanced topics like hypothesis testing, statistical modeling, and Bayesian inference.
CS-250
Strong foundation in designing efficient algorithms and complexity analysis — essential in quantitative / high-frequency trading. Fundamental algorithms and data structures: design paradigms (divide & conquer, dynamic programming, greedy), sorting, graph algorithms, analysis of complexity.
COM-300
Gives tools in stochastic processes which are closely related to models used in quantitative finance. Stochastic processes and models applied to communications systems (random processes, queuing, Markov models, applications in networking).
AICC-1, AICC-2
Combinaison des fondamentaux des mathématiques discrètes avec les principes cryptographiques appliqués. Couvre la théorie des nombres, l'arithmétique modulaire, les algorithmes de chiffrement et les protocoles cryptographiques avancés, formant une base solide pour concevoir des systèmes de sécurité robustes.
CS-233
Very relevant in finance: prediction, classification, anomaly detection, portfolio optimization. Basic machine learning methods: supervised/unsupervised learning, regression, classification, clustering, model evaluation.
MATH-310
Introduction to modern algebra focusing on groups, rings, and fields. Covers integer arithmetic, Bezout's theorem, dihedral groups, symmetric groups, subgroups, homomorphisms, quotient groups, classification of finite abelian groups, rings, ideals, polynomial rings, integral domains, Euclidean domains, fields, and finite fields.
CS-214
Shows you understand best practices, clean design, maintainability, and ethical software design — important in production-level financial systems. How to design and implement reliable, maintainable, efficient software using functional programming, abstraction, modularity, verification.
MATH-101, MATH-106, MATH-203(d)
Core foundations in real and multivariate analysis: limits, continuity, sequences/series, differentiation and integration of single and multivariable functions, ODEs, vector calculus (grad, div, curl), line/surface integrals, Stokes/Gauss theorems, and Fourier transforms. Provides rigorous mathematical grounding for modeling and quantitative methods.
COM-202
The techniques are related to time series analysis, filtering noise, frequency domain methods — useful in financial data analysis. Theory and application of signals and systems: Fourier analysis, filtering, discrete-time systems, sampling, transforms.
CS-202
Understanding hardware, performance, memory, concurrency — useful when implementing high-performance financial software. Operating systems and system-level programming concepts, memory management, concurrency, processes, threads, system calls.
COM-301
Security is crucial in software / fintech / banking / traditional platforms; having that foundation is a plus. Introduction to principles and practice of computer security and privacy: threats, cryptography, access control, secure system design.
PREPA-032(a), MATH-111(e)
Study of vector spaces, matrices, determinants, and fundamental geometric concepts. Linear algebra provides critical tools for machine learning, computer graphics, and optimization algorithms used in both finance and software development.
CS-328
Numerical linear algebra, optimization, automatic differentiation, error analysis, methods for visual computing and machine learning. Critical for implementing efficient algorithms in quantitative finance and data science applications.
CS-290
Software development with ethics, responsibility, professional practices, reliability, maintainability. Essential for building trustworthy systems and understanding the ethical implications of technology.
MATH-101(e), PREPA-031(a), PREPA-031(b), MATH-106(e)
Comprehensive foundation in mathematical analysis including limits, continuity, differentiability, series convergence, and integration techniques. These concepts provide the theoretical foundation for quantitative finance and algorithmic problem-solving.
CS-311
Comprehensive course covering the entire software development lifecycle from conception to production. Essential for understanding how to build scalable, market-ready technology products.
CS-213
Study of user interface design, usability, interaction models, user-centered design, evaluation techniques. Important for creating intuitive applications and platforms.
CS-270
Study of computer organization and design, including processors, memory hierarchies, and I/O systems. Understanding how hardware and software interact at a fundamental level to optimize performance and efficiency in computing systems.
CS-234
Study of how technologies interact with society, democracy, public policy, digital rights. Important for understanding the broader impact of technology on society and regulatory considerations.
PHYS-114
Classical electromagnetism: Maxwell's equations, fields, potentials, waves, boundary conditions. Provides mathematical foundation and problem-solving skills applicable to complex financial modeling.
PHYS-101
Classical mechanics covering kinematics, dynamics, conservation laws, oscillations, and wave motion. Provides strong mathematical foundation and analytical problem-solving skills essential for quantitative analysis.
Get in touch!
ismail.moudden1@gmail.com
linkedin.com/in/ismail-moudden
Lausanne, Switzerland