Rubén
Science begins with curiosity

Hi, I'm Rubén Cañadas

My name is Rubén, and to be honest, I don't really know what I am.

I'm not a physicist.

I'm not a mathematician.

I'm not an AI engineer.

I'm not a software engineer.

Or maybe I'm a bit of all of them.

I like everything, I learn from everything, and I refuse to put myself in a box.

On a good day, we can talk about pseudo-Riemannian manifolds or tensor algebra.
On another, about how quantizing the Poisson bracket gives rise to commutators in quantum mechanics.
And if that feels too theoretical, we can jump straight into improving a multi-head attention mechanism or fine-tuning a Transformer with QLoRA.

Curiosity is the common thread.

That's why I've decided not to define myself on my own website.
I'm not a title. I'm not a label.

I'm just someone who's endlessly curious.

I could tell you about my career path, my degrees, my experience — all the usual stuff.
It would be perfectly fine.

But everyone does that.

That's what LinkedIn is for.

But anyway, here's what I've been focused on lately:

I'm bringing physics, mathematics, and artificial intelligence together in the world of computational biology.

The goal is not just to simulate molecules or predict structures.

It is to understand, in depth, how biology encodes information.

How physical laws, mathematical structure, and chemical constraints shape molecules and proteins.

How meaning emerges from form.

How function is written into matter.

Hidden inside molecular structures is an intrinsic language — a code shaped by evolution and physics.

By learning to read that code, we can build models that don't just predict outcomes, but understand.

Systems that capture structure, dynamics, and intent.

Systems that reason about biology, rather than approximate it.

That is the direction I'm exploring.

Currently Learning

Things I'm Exploring Right Now

Topics and technologies I'm currently diving deep into

CUDA Kernels with Triton

Writing custom CUDA kernels using Triton to optimize inference, finetuning, and training pipelines. Achieving significant speedups in deep learning workloads.

CUDATritonPyTorchOptimization

Algebraic Topology for Molecular Docking

Exploring how topological data analysis and algebraic topology can help find optimal binding spaces for molecular docking simulations.

TDAPersistent HomologyDrug Discovery

Preference Alignment (DPO, RLHF, RLAIF)

Studying and implementing preference alignment techniques like Direct Preference Optimization (DPO), RLHF, and RLAIF to better align language models with human preferences.

DPORLHFRLAIFLLM Alignment
Personal Projects

Personal Projects

Side projects I build to solve real problems in computational biology.

MolFun
Open Source

MolFun

Python 3.10+PyTorch 2.xCUDA 12+Triton

Open-source framework for fine-tuning and adapting pre-trained protein structure prediction models like OpenFold. Features four fine-tuning strategies, modular architecture, and comprehensive experiment tracking.

4 Fine-Tuning Strategies

Head Only (~50K params), LoRA (~600K), Partial (~5M), Full (~93M)

Experiment Tracking

Native WandB, Comet, MLflow, Langfuse & HuggingFace integrations

Modular Architecture

Swappable components via registry: attention, blocks, structure modules

Complete Pipeline

Data loading, MSA handling, featurization, model export

CodeMol
Private

CodeMol

Python3D RenderingWebSocketAI Agents

Desktop protein visualization app combining 3D molecular rendering with a terminal-style command console. Features 200+ Python tools, AI agent integration, and collaborative real-time sessions.

Console-First

Text commands instead of GUI menus for power-user workflows

200+ Tools

Across 27+ groups for visualization, analysis, and manipulation

AI Agent Integration

Natural language interaction via LLM agents with specialist routing

Trajectory Analysis

MD playback, RMSD tracking, clustering, multi-structure comparison

Agent Skills
Open Source

Agent Skills

PythonFastAPINext.jsTypeScriptDockerMCP

Curated collection of production-tested skills for Claude Code that encode best practices for full-stack development, testing, security, and architecture.

Backend Development

FastAPI + DDD, SOLID principles, Pydantic validation, pytest testing

Web Development

Next.js/React with feature-first organization and ports & adapters

Cybersecurity

OWASP top 10, container hardening, prompt injection defense

Web Testing

Vitest + Testing Library, MSW mocking, accessibility testing

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