🔬Research Interest
🧑🧒🧒 I’m open to collaborate on...
😁 About Me
Hello! I’m Alan Crespo Murillo, currently in my seventh semester of Engineering in Data Intelligence and Cybersecurity at Universidad Panamericana in Mexico City. Although I’m Mexican, I spent formative years living in Costa Rica, where I developed a passion for poetry and urbanism. Back in Mexico, my studies sparked a deep interest in artificial intelligence and its real-world applications. I’m also a dedicated cryptocurrency enthusiast and I like to explore new themes and projects. I’m always eager to collaborate on projects that explore topics outside my current area of expertise.
I joined GTC, because I want to explore new areas of investigation. I would like to meet new people and collaborate on developing projects that can last for years. For me, GTC is a great environment for meeting very talented people and learning from other fields.
🐾 My AI+X Journey
(2025.7) Machine Learning for Financing Forecast
My plan is to take then:
Machine Learning in Quantitative Finance - J.P. Morgan Project
Co-designing Quantum Computing Architecture - IBM Project
Quantum Machine Learning - Google Quantum AI Project
🎬 Content & Media Highlights
If you’d like to know more about me or just have a conversation, feel free to reach out at [email protected]
To learn more about my startup, visit www.coinlab.com.mx or contact us at [email protected]
We’re open to collaborating with potential investors, visionary partners or content creators.
Our long-term goal is to evolve Coinlab from an investment and software development platform into a Web3 consultancy, helping businesses integrate blockchain solutions into their models.
🏆 Achievements & Honors
Entrepreneurship
CoparTank Finalist – May 2025
Finalist at CoparTank, a national startup competition organized by Coparmex—the Employers' Confederation of the Mexican Republic—where my Coinlab team presented an AI-powered cryptocurrency investment platform designed to democratize market access for novice users.
My research papers:
Optimizing Urban Layouts Using Reinforcement Learning (Q-Learning Agent and Deep Q-Network Agents)
Introduces two RL agents to design urban grids under the n-minute city model, validated in synthetic and real-world scenarios.
24th Mexican International Conference on Artificial Intelligence (MICAI2025), Mexico City, Mexico.
Status: Submitted and under review.
Human-Friendly Explanation Model Based on the Aristotelian Practical Syllogism for Reinforcement Learning Agents in Urban Intelligent Design
Proposes an explanation model aligning Q-learning decisions with Aristotle’s practical reasoning to enhance interpretability in urban planning.
24th Mexican International Conference on Artificial Intelligence (MICAI2025), Mexico City, Mexico.
Status: Submitted and under review.
Reinforcement Learning in Urbanism: Building the Cities of the Future with AI
Presents RL agents that optimize service locations to ensure rapid infrastructure access in simulated environments.
Congreso Mexicano de Inteligencia Artificial, Mexico City, Mexico.
Status: Accepted.
Exploring AI Ethics and Urban Planning: A Deep Reinforcement Learning Approach to Intelligent Urbanism
Explores ethical dimensions of AI in urban design through a DQN agent for equitable street layout generation.
Media Ecology Association Proceedings, Mexico City, Mexico.
Status: Accepted.
🎈 Fun Facts
I love playing chess—fun fact: I actually met my girlfriend in our university chess club.
I spent part of my adolescence in Costa Rica, where I first fell in love with poetry and natural environments.
I’m a literature enthusiast, especially works about nature. My favorite poet is Eugenio Montejo and my favorite novel is Kokoro by Natsume Sōseki.