Data Scientist | AI Researcher | Cat Lover
Currently, I am working on Multimodal Retrieval-Augmented Generation (RAG) systems and Vision-Language Models (VLMs), with a strong focus on advancing multimodal large language models (MLLMs) and Explainable AI. My research explores how visual and textual information can be effectively integrated to build intelligent systems that are both accurate and interpretable.
In particular, I am interested in developing efficient and robust models for analyzing and understanding human perceptions from street view imagery. This includes investigating how urban environments are perceived in terms of safety, liveliness, aesthetics, accessibility, and overall quality of life. By extracting meaningful perceptual and semantic features from large-scale visual data, my work aims to support applications in urban computing, smart cities, and data-driven urban planning.
More broadly, I am motivated by the challenge of creating multimodal AI systems that can reason across different data modalities while remaining transparent, explainable, and adaptable to real-world scenarios.
Agentic-AI Engineer June 2024 - Current
Consulting Remote 🌎
LegalXplain: Apply Large Language Models (LLMs) methods to explain relevant binding references in legal documents from the Brazilian Supreme Court (STF).
Technologies: Azure, RAG, LangChain, Langgraph, OpenAI, Legal-NLP
AI Engineer June 2022 - June 2024
Getter Amplified Industry (GetterAI) Manaus - Brazil 🇧🇷
Samarco: Monitor the entire industrial operation process and generate audible or visual alerts when a risk is detected. The development of end-to-end applications focuses on the detection of safety implements and the correct analysis of potential risks in industries.
Akzonobel: Building an infrastructure to infer safety risks and protection regulations. Implementing AI end-to-end applications to assess posture and ergonomics, detect work cycles and times elapsed, and determine factory safety areas.
Technologies: AWS, Kuberflow, Docker, Kubernetes, MLOps
AI Data Scientist May 2019 - May 2022
Fundação Getúlio Vargas (FGV) Rio de Janeiro - Brazil 🇧🇷
LegalAnalytics: Exploring and predicting possible citations between Brazilian legal documents. Analysis, processing, organization, and extraction of the content of legal documents from the Brazilian Supreme Court-STF. Through NLP and LLMs, it is possible to identify and predict the most relevant sentences.
TimberFlow: Detection of deforestation and illegal transport in the Amazon. Integration of Sinaflor and Sisflora, Brazil’s two most relevant databases on timber transport. Through Markov chains and graph networks, it is possible to predict the most likely transport flow between a consumer and a producer.
UrbVis: Analysis of criminal data and correlations with urban security perception. Through convolutional networks, segmentation, and object detection, it is possible to identify, process and extract the main characteristics of street images in order to predict the perception of security in the cities of Rio de Janeiro and Sao Paulo.
Technologies: Azure, TensorFlow, Keras, PyTorch, Computer Vision, NLP
Web/Mobile Developer August 2017 - October 2018
CERNICALO S.A. Lima - Perú 🇵🇪
Web Applications: Backend development using frameworks such as Laravel (Php) and Spring (Java). Furthermore, implementation and deployment of cloud hosting services.
Mobile Applications: Android Mobile Applications Development using Android Studio with Android SDK versions from Jelly Bean (API 18) to Marshmallow (API 23).
Technologies: Android, Java, PHP, Laravel, MySQL, Apache Kafka.
Research Assistant January 2016 - July 2017
Centro de Tecnologías de Información y Comunicaciones (CTIC) Lima - Perú 🇵🇪
BeaGOns: Real-Time Fog-Edge web application to read and process sensor data. Using an edge computing architecture, we deploy Raspberry Pi 2B-based modules (Edge Layer) across Lima City to collect environmental data via sensors.
CHESS: High-Performance Beowulf Hybrid CPU-GPU Cluster with NVIDIA GeForce GTX 980 Ti. Using Apache Spark, MPI, and OpenMP, we simulate urban transport traffic with 1.2TB of temporal data from New York City and Seoul.
Technologies applied: C/C++, HPC, Fog Computing, Golang, Embedded systems
Fundação Getúlio Vargas (FGV) Rio de Janeiro - Brasil 🇧🇷
Ph.D. in Computer Science 2022 - 2026
Thesis: Exploração da percepção de segurança urbana com modelos de visão-linguagem e contrafactuais - Slides
Supervisor: Ph.D. Jorge Poco
Contributions:
Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro - Brasil 🇧🇷
M.Sc. in Computer Science 2021 - 2023
Thesis: Detecção Automatizada de Exploração de Vulnerabilidades em Fóruns de Hacking Clandestinos - Slides
Supervisor: Ph.D. Daniel Menasché
Contributions:
Universidad Católica San Pablo (UCSP) Arequipa - Perú 🇵🇪
M.Sc. in Computer Science 2018 - 2020
Thesis: Técnicas de Aprendizaje Profundo para el Análisis de la Percepción de la Seguridad Urbana - Slides
Supervisor: Ph.D. Jorge Poco
Contributions:
Universidad Nacional de Ingeniería (UNI) Lima - Perú 🇵🇪
B.Sc. in Computer Science 2012 - 2017
Thesis: - Slides
Supervisor: Ph.D. Glen Rodriguez
Final Project: Diseño e implementación del core level de una plataforma transversal basado en arquitecturas Fog Computing - Slides
Supervisor: Ph.D. Manuel Castillo
To know further about my research line, visit this link.
Category | Proficiency in approximate descending order from left to right ————————— | ————————————————————– Programming Languages | C, C++, Go, Python, R, M (Octave/MATLAB), Javascript Web Technologies | HTML, CSS/SCSS, Django, VueJS, ReactJS, Flask, BeeGo, Gorilla, Node.js, Angular, Jekyll Databases/Storage | PostgreSQL, MySQL, MongoDB Cloud | AWS, GCP Productivity Tools | LaTeX, GIT, Jupyter Software Engineering | Test-Driven Development: Selenium
Available on request.