Discover the Talks at PyCon Colombia 2026 ✨
Browse every accepted session—titles, tracks, levels, and speakers—before you plan your days in Medellín.
Machine Learning Applied to Genetic Sequences
DNA contains massive amounts of biological information, but how can artificial intelligence help us understand it? In this talk, we will explore how Python and Machine Learning can be used to analyze genetic sequences in a practical and beginner-friendly way. Using public biological datasets, we will demonstrate how DNA sequences can be transformed into data suitable for machine learning models, covering concepts such as feature extraction, sequence representation, and basic classification techniques. We will also review popular Python tools used in bioinformatics, including Biopython, pandas, and scikit-learn, while discussing real-world challenges when working with biological data, such as high dimensionality, noise, and interpretability limitations. By the end of the talk, attendees will have a clear understanding of how to start building genetic analysis projects using accessible tools from the Python ecosystem, even without prior bioinformatics experience.
Structured Learning: AI-Powered Platform That Transforms Academic Papers into Interactive Learning Experiences
Structured Learning is a platform that turns a research paper into a complete learning module—chapter-by-chapter explanations, incremental executable code, RAG chat, FSRS spaced-repetition flashcards, equation derivations, and a knowledge graph in Neo4j. This talk covers the product, the engineering of an agentic workflow pipeline that takes a GitHub issue to a merged PR with isolated worktrees, auto-patching after failed review, and GitHub as the agents' API, and how it runs on AWS with LocalStack for dev-prod parity. Agents don't replace engineers—they replace the glue between engineers and the boring 80% of the SDLC—and that's where compound returns live.
Leverage your Python skill using the Python interpreter
In this talk, I'll challenge the audience's mindset about Python. Python is not an interpreter, and in fact, there are multiple Python interpreters—each with its own architecture and purpose. I'll walk through Python's core internals and show how programming languages interact beneath the surface. We'll explore how to write better Python by understanding the garbage collector, what you can build using the AST, how to read and leverage the disassembler, and the practical implications of Python's transition from its old LL(1) parser to the current PEG parser. We'll also dive into lesser-known features of Python interpreters, what a PEP really is and how it shapes the language, and conclude with a deep look at Python without the GIL—what changes, what breaks, and how the core team removed it. Throughout the talk, I'll share personal stories, including battles caused by identical ASTs and the moment I believed I had discovered a way to speed up the Python interpreter itself.
hls4ml: From Python Models to Hardware Acceleration
Bridge the gap between Python machine learning and hardware implementation using hls4ml. In this workshop, you'll learn how to take ML models trained in Python (TensorFlow, PyTorch, scikit-learn) and deploy them to FPGAs using the hls4ml library. We'll cover model quantization, hardware-aware training, the HLS synthesis workflow, performance profiling, and practical considerations for deploying ML at the edge. No prior FPGA experience required.
Jerónimo López Gómez
Researcher @ Universidad de Antioquia
Natalia Echeverri Durán
Researcher @ Universidad de Antioquia
Building a Transformer with Rust
Transformers are often perceived as incomprehensible giants. This talk aims to prove the opposite: they are not black boxes but elegant mechanisms that can be understood and mastered from their fundamentals. We present Molinete AI, a GPT-2-style model built strictly from scratch in Rust. No deep learning frameworks—just tensors, math, and full control. Inspired by Feste from Tag1 Consulting (trained on Shakespeare), this project poses a different challenge: training the network on Miguel de Cervantes's work to generate text in the style of the Golden Age. Throughout the session we'll break the model down piece by piece. With the support of a Manim animated presentation (over 4,000 lines of code), we'll make visible how information flows inside the network. We'll start from tokenization (BPE) and building basic operations, then dive into the core of the model: embeddings, causal mask, and Multi-Head Self-Attention. Finally, we'll explore the learning process, watching how gradients flow through the network during training. More than a demo, this talk aims to provide a clear, operational view of Transformers, connecting theory with a real from-scratch implementation.
Python and Machine Learning for Sustainable Thermochemical Optimization
Chemical engineering still relies heavily on costly, slow experimental trials to evaluate operating conditions in thermochemical processes. This talk proposes a practical approach based on Python and machine learning to accelerate that process: building predictive models from physicochemical data that estimate key outcomes without testing every scenario in the lab. A complete flow oriented toward real applications will be shown, from data to decisions, with the goal of reducing analysis time, lowering experimental costs, and supporting process optimization with environmental impact.
