A student-owned association committed to excellence in data science, quantitative finance, and applied AI — building rigor into work that actually ships.
A student-led collective for data science, quant finance, and applied AI.
enode is a student-owned association at ESADE. We run workshops, build projects, and bring practitioners into the room — across three disciplinary tracks.
We're young: a year and change old. The work is real, the standard is high, and the bar to ship is non-negotiable. Press on.
Three departments.
Recent artifacts.
Calendar.
If the standard sounds right — write to us.
We're not selecting for "already a quant" or "already a researcher." We're selecting for engagement, proactiveness, and people who consistently show up.
about enode.
A student-owned association at ESADE. Founded in 2024. Three departments — Research / AI, Quant, and AI4Law. A small board, a smaller standard, and a list of things shipped.
What enode is.
enode is a student-owned technical association at ESADE — operating across the Sant Cugat campus (Research, Quant) and Pedralbes (AI4Law).
We are an academy (workshops & learning sessions), a lab (applied projects & research notebooks), and a talks programme (external speakers, masterclasses, alumni) through localhost.
The association is run by a small board, with a head per department. Everything else — speakers, workshops, projects — is run by members. Press on, show your work, ship the artifact.
Who runs it.
ongoing election process.
ongoing election process.
ongoing election process.
ongoing election process.
ongoing election process.
ongoing election process.
Operating structure.
President
Executive Board
├── Head of Quant
├── Head of Research / AI
├── Head of AI4Law (Pedralbes)
├── Head of Marketing & Community
└── Head of Operations
Quant Department
├── Quant Workshop Lead
├── Quant Projects Lead
└── Quant Associates
Research / AI Department
├── AI Foundations Lead
├── Applied ML Lead
└── Research Associates
AI4Law Department
├── Content / Doctrine Lead
└── AI4Law Associates
Marketing & Community
├── Content Lead — Tech
├── Content Lead — Legal
└── Marketing Associates
Operations
└── Events Coordinator
What we've shipped.
- 2024 Q4Founding cohort
First members onboarded. Initial workshop calendar and Quant department launch.
- 2025 Q1NL → DB query · Barcelonactua
Built a natural-language querying tool for an NGO's operational database. Engagement now paused.
- 2025 Q2Agentic lead-production demo · Supersonik
Prototype for a startup. Useful, then archived; consulting de-prioritised in favour of fundamentals.
- 2025 Q3GPT-2 & Llama replications
Member group rebuilt both architectures end-to-end. The kind of work we want to foster.
- 2025 Q4AWS data platform · v0.1
ETL pipeline, Terraform-managed, paper-trading on Alpaca. Iterating quarterly.
- 2026 Q1AI4Law department launch
Pedralbes campus, separate stream, joint cross-campus seminars with Research and Quant.
- 2026 Q2Backtesting engine v0.4 + research paper contributions
Strategy framework hardened. Select members contributing to a Spanish research group's preprint.
Three principles.
- I.Press on.
Persistence and determination alone are omnipotent.
- II.Show your work.
Mathematically describe what you implement. Implement what you describe.
- III.Ship the artifact.
A workshop, a notebook, a paper, a strategy in the paper-trading account — the artifact is the proof.
Where to find us.
ESADE Sant Cugat — Research / AI, Quant. Workshops in Aula 001 and 005.
ESADE Pedralbes — AI4Law.
Mail: enode@esade.edu · Cycle 2025—26
enode/research.
The how and why behind modern computing and data science. We learn the mathematical theory behind data, the optimization of algorithms, and the lower-level mechanics of code — then turn it into projects we can show.
Upcoming — research.
What we hope to achieve
- FOUND/01Statistical foundations
MLE derivation under common distributions, change of variables, transformation of densities.
- FOUND/02Optimization & backprop
Gradient descent variants, automatic differentiation, the math underneath training loops.
- APPL/01Applied ML
Recommender systems, classifiers, simple LLM apps, forecasting, dataset analysis from scratch.
- DEEP/01Architectures
Embeddings, transformers, mixture density networks, diffusion, graph neural networks.
- SYS/01Systems & performance (early-stage)
Memory management in large AI models. Reading C++ and Rust to write high-performance tools.
- LED/01Student-led sessions
If you've mastered a niche library or built a unique side project — you teach it.
Before listing what we have done: enode is only a year old. We are still in our founding phase. Everything you see here is being molded in real time.
LLM architectures from scratch. A group of our members replicated GPT-2 and Llama from scratch. With the pace of the field these are already outdated — but this is the kind of work we want to foster.
Consulting (paused). We previously developed a natural-language database query system for the NGO Barcelonactua and an agentic lead-production demo for the startup Supersonik. We've put consulting on the back-burner; the most valuable growth comes from sticking to the fundamentals.
Research exposure. Select members have contributed to papers with a Spanish research group. These collaborations depend on external cycles — but we believe research exposure, perhaps even our own preprints, will come naturally with our learning.
Past projects.
Ideal candidates.
We're not selecting for "already a researcher." None of us would claim to be one. We're selecting for engagement, proactiveness, and people who consistently show up, take initiative, and like learning by building.
- A.A solid grasp of statistics, calculus and linear algebra
And a desire to see how they apply to AI/ML.
- B.Python with mathematical rigor
Pandas, NumPy, notebooks. Willingness to write clean, testable code.
- C.Curiosity for low-level work (bonus)
Even basic intuition about memory, compilers, hardware, or Rust/C++ to build faster, safer tools.
enode/quant.
Learning the math, turning it into efficient code, and building the infrastructure so our work is reproducible and properly deployable. We run strategies in a paper-trading account today; the longer-term goal is a small, student-led fund.
Upcoming — quant.
Research threads.
- R/01Options fundamentals
Pricing intuition, volatility, sensitivities.
- R/02Monte Carlo methods
Simulation-based pricing and risk estimation.
- R/03Return behavior
Distributions, tails, assumptions beyond normality.
- R/04Correlation & dependence
Structure, regime shifts, diversification limits.
- R/05Black–Litterman
Baking personal expectations into classical portfolio optimization.
For developing strategies, we work in small pods. Each pod can pick a direction and run with it.
Everyone has the freedom to explore the mathematical trading topics they're genuinely curious about — momentum, pairs trading, portfolio optimization, volatility forecasting, financial dashboards — then bring the best ideas back to the group through write-ups, reviews, and clean implementations.
Strategy development.
Engineering.
- E/01ETL + data platform on AWS
Ingestion → cleaning → feature generation → storage / access → automated strategies → trading on Alpaca.
- E/02Terraform
Infrastructure as code, versioned and repeatable.
- E/03Deployment-ready structure
Proper packaging and environments so the team isn't dependent on one laptop setup.
- E/04Backtesting engine
A consistent framework for unbiased testing of any strategy.
- E/05Reproducibility mindset
Consistent datasets and repeatable runs so results can be compared and improved over time.
Two profiles.
We typically see two strong profiles. Either is welcome on its own; the best teams have both.
Quant / research. Solid math/stats foundation (calc, linear algebra, probability — more is a plus); interest in modeling (returns, risk, optimization, time series); solid Python for research and a willingness to write clean, testable code.
Engineering / dev. Stronger on databases & data modeling (SQL, schemas, performance, pipelines); Docker; cloud tooling (AWS basics); CI/CD, packaging, and keeping systems reliable for a team.
Quant workshops/from theory to labs.
These sessions were built to slow the department down in the right way: before writing trading code, members should understand the mathematics they are using, where assumptions enter, and how each model becomes an implementation.
Why we designed them.
The quant track is not meant to be a collection of disconnected finance demos. We want members to move from definitions to proofs, from proofs to models, and from models to code that can be inspected, defended, and improved.
The first block built the mathematical base: real analysis, numerical thinking, optimization, and the discipline of stating assumptions precisely. The second block moved into stochastic processes and financial applications, where the same theory becomes simulation, pricing intuition, and hands-on labs.
The point was not to cover everything. It was to give members enough structure to read future material seriously, ask better questions, and build strategies without treating the math as decoration.
All content and slides developed by our instructors are available in the public Drive. The stochastic processes in finance implementations are available in the linked GitHub repository.
Who guided the work.
Marius Oltean
Marius supported the mathematical foundations: real analysis, optimization, and the broader bridge from abstract concepts to usable quantitative reasoning. At ESADE, he coordinates mathematics courses in the BBA and BAIB programs. His background is in mathematical physics, with specialization in general relativity and numerical analysis.
Adrià Garcés Ortiz
Adrià led the stochastic workshops, final financial applications, and hands-on labs. He is a PhD student with an MSc in Physics of Complex Systems & Biophysics and a BSc in Physics from Universitat de Barcelona.
We are grateful to both professors for helping turn a young student department into a place where rigor, curiosity, and implementation can meet.
ai & the
letter of
the law.
A specialized branch dedicated to mastering applied artificial intelligence within the juridical field, and to bridging the gap between technical innovation and legal frameworks.
Cultivating a specialized branch for aspiring law professionals.
In this spirit of convergence, we recognize the immense value of integrating into the broader ESADE ecosystem — and particularly into the Pedralbes campus and its body of aspiring law professionals.
An initiative led by AI4Law proponents to ensure legal scholars are equipped not merely to adapt, but to lead a legal field being rapidly transformed by complex regulatory, ethical and data-privacy challenges.
Upcoming — ai4law.
Regulatory landscape
The EU AI Act, GDPR, sectoral compliance, and the operational shape of "high-risk" systems.
Doctrinal analysis
Reading judgments and statutes the way technical readers read papers — for evidence, scope, and limits.
Applied prototypes
Document review, contract drafting, evidentiary tooling — built with lawyers in the room.
Ethics & data privacy
Where ML decisions meet due process, consent, and the slow grammar of accountability.
Cross-campus seminars
Joint sessions with Research and Quant. Lawyers explain problems; technologists explain tradeoffs.
Career & practice
Talks with legaltech founders, in-house counsel, judges, and academics shaping the discipline.
AI4Law operates under enode's charter and editorial standard, on a separate campus. Membership is open to students of either discipline; the cross-pollination is the point.
Where Research and Quant interrogate models, AI4Law interrogates the institutions models touch — and asks what credibility looks like when the artifact is a contract, a brief, or a verdict.
One umbrella, one standard.
press on.
We are not selecting for "already a quant" or "already a researcher." We are selecting for engagement, proactiveness, and people who consistently show up. The biggest expectation is simple: you need to put in the work.
Research / AI
Stats, calculus, linear algebra. Python with rigor. Bonus for Rust/C++ or systems curiosity.
Quant
Modeling intuition (returns, risk, time series). Pandas / NumPy. Willingness to write clean, testable code.
Engineering
SQL, Docker, AWS basics, CI/CD. Keeping systems reliable for a team.
Marketing & Community
Social, content, member spotlights. Help us answer: what is enode, and why stay?
Operations
Make things actually happen — rooms, calendars, forms, speaker logistics, internal organization.
AI4Law (Pedralbes)
Law students who want to lead, not adapt to, an AI-transformed legal field.
Write to us.
Tell us what you've been reading, building, or stuck on. A paragraph is enough. We read everything.
Submitting opens your mail client with the message pre-filled to enode@esade.edu.