CV

Adjunct Professor

Departamento de Ingeniería para la Innovación, Universidad Iberoamericana

Teaching at the Actuary Department courses like Machine Learning, Data Intelligence, Selected Topics on Information Systems, Python Programming

• Designed the syllabus for the Machine Learning and Data Intelligence courses.

Teaching Assistant

Universidad Nacional Autónoma de México

• DGTIC
• Helped and NVIDIA embassador in the Fundamentals of Accelerated Computing with CUDA Python NVIDIA Deep Learning Institute workshop.

AI and Contact Center Specialist

Hello CIT

• Management and implementation of AI solutions to automate speech and text applications for Spanish-based processes directly into Huawei’s AICC service.
• Agent development and deployment for automation of tasks related to public tenders processes and analysis.
• Custom CRM design enhanced with AI solutions for internal use.

PhD

Universidad Iberoamericana

Computer Science

• Theory of Relativity
• Study the impact of multimodal information to predict the sentiment polarity of tweets regarding a) COVID-19 and b) sport events, taking place in Mexico.
• We use a series of Large Language Models to represent text and image data to fuse them using different fusion rules and a Support Vector Machine to predict the final sentiment label of each element.

M. C. Sc.

Centro de Investigación en Computación, I.P.N.

Computer Science

• We gathered financial related tweets regarding selected companies listed in the Mexican Stock Exchange in order to create a learning model to predict the direction of the stock price movement.

Bachelor

Escuela Superior de Física y Matemáticas, I.P.N.

Finance

Honorable Mention

Universidad Iberoamericana

As a result of the PhD thesis defense.

Department Prize for Outstanding Teaching Performance.

Universidad Iberoamericana

Awarded for outstanding teaching performance.

Enhanced phishing detection using multimodal data

Knowledge-Based Systems

hishing remains one of the most persistent cybersecurity threats, increasingly exploiting not only technical vulnerabilities but also human cognitive biases. Existing detection systems often rely on single-modality features and black-box models, which restrict both generalization and interpretability. This study presents an explainable multimodal framework that combines textual and technical cues, including message content, URL structure, and Principles of Persuasion, to capture both objective and subjective aspects of phishing.

Análisis de emociones en torno a las comunidades de activistas en el contexto de las movilizaciones del \#8M2023 en México

Revista Iberoamericana de Comunicación

En este artículo se aborda el estudio de las emociones expresadas en los hasthtags producidos en torno a las movilizaciones feministas ocurridas el 8 de marzo de 2023 en México. Para realizar el análisis de datos correspondiente al #8M2023 en la red social X (antes Twitter) se utilizó la API de X para recolectar una muestra de 1301 publicaciones de 866 usuarios únicos. Se analizaron con base en la propuesta de Jasper (2012). El análisis permitió identificar nodos, comunidades y reivindicaciones vinculadas a emociones y la conexión generacional presente en las consignas.

Machine learning framework for country image analysis

Journal of Computational Social Science

In this work, we compare the performance of a machine learning framework based on a support vector machine (SVM) with fastText embeddings, and a Deep Learning framework consisting on fine-tuning Large Language Models (LLMs) like Bidirectional Encoder Representations from Transformers (BERT), DistilBERT, and Twitter roBERTa Base, to automate the classification of text data to analyze the country image of Mexico in selected data sources, which is described using 18 different classes, based in International Relations theory.