Computer Science · EPFL · Software & AI
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
Building things that matter. AI systems, quant platforms, full-stack products and agentic tools. I move fast, stay rigorous, and care about impact.
From web apps to ML research, spanning finance, AI, and engineering.
See my projectsAt Junior Entreprise EPFL — over 300K€ in consulting revenue in 2023/24.
See experienceSiraEdge (FinTech), a fashion brand, and a seasonal delivery service.
Explore venturesLatest projects I'm working on
Portfolio optimization platform with 7 models, walk-forward backtesting & advanced risk management.
LightGBM, LSTM & Transformer models for order book price prediction & professional backtesting.
Natural language & semantic querying over 3D point clouds — interactive interface for spatial data exploration at DHLab EPFL.
Fully automated AI trading system with multi-agent architecture, live execution & decision approval workflow.
A collection of my work and experiments
Ambient AI that lives on your desktop. Transparent Electron overlay (Alt+Space), screen-aware context capture, and Ambient Memory — a silent cron that extracts structured facts from every conversation and injects them into future sessions. Kimi K2.6 + Supabase + Cloudflare Workers.
A real-world asset lending protocol built natively on XRPL devnet — not a mockup, a full end-to-end pipeline. Tokenize assets (XLS-33 MPTokens), lend into vaults, borrow with on-chain receipts (XLS-66). Every action generates a real XRPL transaction hash.
Next-gen RAG pipeline for the EPFL Racing Team — layout-aware document parsing (LlamaParse), vectorless PageIndex for exact citations, motorsport-tuned reranking, and a transparent reasoning chain. Built as ML Engineer at the EPFL AI Team Industry Track.
Claude Code for architectural BIM — a Revit plugin that reads the active model, takes natural language instructions, plans and executes structural actions (walls, columns, beams), and self-corrects on failure. Full perception → planning → action → verification loop applied to CAD.
Android app for student project teams — Jira-level task management with AI auto-assignment, meeting transcription via Google Cloud Speech-to-Text, LLM brainstorming that converts ideas into tasks, and offline-first notes. 1,498 commits. Kotlin + Firebase.
Bachelor thesis at EPFL DHLab — a natural language and semantic querying system over 3D point clouds, with an interactive viewer for spatial exploration and object retrieval.
A fully automated AI-driven quantitative trading system built on a multi-agent architecture — from signal generation to live execution, with a decision approval dashboard and real-time portfolio monitoring.
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.
Work Experience & Internships
Lausanne, EPFL
Lausanne, EPFL
Remote
Madrid
Lausanne
Rabat, Morocco
Academic Background & Achievements
2022 — Present
ML Engineer — Industry Track
Feb 2026 — Present
Vice President
Feb 2026 — Present
2023 — Present
2019 — 2022
Technical, Consulting & Financial Competencies
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-460
Design and implementation of data-intensive systems: relational databases, query optimization, indexing structures (B-trees, hash indexes), transaction management, ACID properties, concurrency control, and distributed data systems. Hands-on with SQL, query planning, and storage engines.
COM-302
Fundamental concepts of digital communication systems: source and channel coding, modulation schemes (QAM, OFDM), capacity limits (Shannon theorem), error-correcting codes (LDPC, turbo codes), detection theory, and modern receiver design for wireless and wired channels.
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
