I got a Master's degree in Computer Science from the Graduate Program in Computer Science (PPGI) at the University of Brasília (UnB) (2026) and a Bachelor’s degree in Biomedicine from the Catholic University of Brasília (UCB) (2020), with a second technical qualification in Systems Analysis and Development from Faculdade Anhanguera (2023). Currently, a PhD student conducting research focused on Bioinformatics, algorithm optimization, and machine learning. Has experience in developing computational solutions using Python and C++. Works on the development and implementation of scalable systems with integration of vector databases for RAG (Retrieval-Augmented Generation), as well as process automation through NLP and Deep Learning. Holds relevant international certifications in Artificial Intelligence, Databases (SQL), and Computer Science.

Education

Experience

• Multiple Sequence Alignment Research in bioinformatics, computer networks, and cloud computing, focusing on high performance, analysis, optimization and artificial intelligence.

• Full Stack Development Participated in the development of web applications using front-end and back-end technologies, as well as collaborating on database modeling.

Projects

Intelligent chatbot using RAG (Retrieval-Augmented Generation) with CSV knowledge base. Supports multiple LLM providers (Groq, OpenRouter, Ollama), HuggingFace embeddings, and Streamlit web interface for automated customer support.

Parallel A-Star search for Multiple Sequence Alignment with pa-star-rv visualization tool. Features TrioAlign 3D dynamic programming, HeuristicHPair with h3all computation, multi-threaded initialization and heuristic calculation, and memory-optimized linearized 3D matrices.

Interactive CLI for interacting with multiple LLMs (ChatGPT, etc.). Features: multiple observers (Console, File, Log), evaluation strategies (WordCount, SemanticSimilarity, TextSimilarity), and command-based architecture for asking questions and comparing responses.

Implementation of the Needleman-Wunsch algorithm for global sequence alignment. Features matrix initialization, score calculation with match/mismatch/gap penalties, traceback for alignment retrieval, and matrix visualization.

Multiple Sequence Alignment (MSA) using Integer Programming with Google OR-Tools and CBC solver. Implements Sum-of-Pairs scoring optimization with match (+2), mismatch (-1), and gap (-3) penalties, ensuring order preservation and valid alignments.

Interactive 3D visualization tool for analyzing PA-Star execution logs. Features Tkinter GUI, Matplotlib 3D plots with color mapping, multi-format export (PNG, JPEG, PDF, SVG), automatic metrics calculation, and multi-thread performance comparator (1-8 threads).

Skills

Languages

Python C++ Ruby JavaScript SQL

Technologies

Linux Windows macOS Git Node.js Docker