Biviana Marcela Suárez Sierra

About

Biviana Marcela Suárez Sierra is a statistician and data science researcher, with experience in statistical modeling, machine learning, and computational analysis of complex data. She is currently a university professor and leads interdisciplinary research projects that integrate statistics, programming, and data analysis to address problems in health, energy, the environment, and digital humanities. Her recent work has focused on developing methodologies for analyzing large textual corpora through natural language processing (NLP) techniques, text mining, and statistical learning. As a linked researcher on the project Art, science and technology in the public sphere: the contribution of Digital Humanities to the analysis of discourses in the public sphere in Colombia, she has led teams made up of students and researchers from diverse disciplines to study how discourses about science, culture, and society circulate in Colombian media. She has more than a decade of experience in research and data analysis, both in the academic sector and in public and private entities. Her main interest is building bridges between computational tools and real problems, promoting collaboration spaces among data scientists, programmers, linguists, health professionals, and social science specialists. At PyCon Colombia 2026 she will participate as director and moderator of a workshop on natural language processing for corpus analysis in Digital Humanities, sharing experiences on how Python can become a meeting tool between technology and the humanities.

Workshop

Artificial IntelligenceData Science

NLP in Practice: From Corpus Linguistics to RAG with Python

FORMAT: WorkshopLEVEL: IntermediateLANGUAGE: Spanish

Natural language processing today offers a mature set of tools for analyzing textual corpora systematically and reproducibly, but the path between having the documents and obtaining results is not always clear. This workshop covers that path from start to finish. In two hours, participants will build an understanding of the NLP ecosystem: its history, logic, and methods. The session opens with a timeline from the earliest rule-based models to transformers, followed by a map of techniques organized by problem type (classification, entity extraction, semantic search, generation) so each participant can identify which method they need for a specific textual problem. The second part covers two implementations with Python. First, topic modeling with BERTopic, reviewing the internal pipeline of embeddings, UMAP, and HDBSCAN. Second, a conversational assistant with RAG: corpus indexing, semantic retrieval, and connection with a language model to answer queries about the documents. Upon completion, each participant will have a functional notebook with both pipelines and a clear map of the ecosystem to guide their own textual analysis projects.

Andrés Felipe Puerta Velez

Asistente de investigación y estudiante de maestría en matemáticas aplicadas. @ Universidad EAFIT

Biviana Marcela Suárez Sierra

Profesora vinculada al área de Computación y analística @ Universidad EAFIT

Dora Cecilia Alzate Gallo

Estudiante de la Maestría en Estudios Humanísticos @ EAFIT

Karen Melissa Gomez Montoya

Ingeniera matemática - Asistente en investigación @ Universidad EAFIT

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