Marcos Sanson

About Me

My academic and professional journey has taken me across the United States and internationally, including two separate study abroad experiences in Spain.

In addition to my studies as a computer science student at Grand Valley State University, I also work part-time as an undergraduate researcher in artificial intelligence (AI), machine learning (ML), and evolutionary computation (EC) research.

My current research focuses on designing and optimizing computational models, evolutionary algorithms, and machine learning techniques to solve complex problems.

Education

Grand Valley State University - Allendale, Michigan, United States
Bachelor of Science in Computer Science, Minors in Mathematics and Cybersecurity (expected May 2026)
GPA: 3.893/4.000
Honors:

• Barry Goldwater Scholar
• Ronald E. McNair Post-Baccalaureate Achievement Program Scholar
• P. Douglas Kindschi Undergraduate Research Fellowship in the Sciences (Winter 2024, Winter 2025)
• Dean's List (Fall 2022 - Winter 2024)

• Algorithms Engineering
• Data Structures and Algorithms
• Computer Organization
• Introduction to Software Engineering
• System-Level Programming & Utilities
• Discrete Structures
• Linear Algebra
• Introduction to Cybersecurity
• Intercultural Communication & Service Learning (Spain Study Abroad - Summer 2023)

Universidad Carlos III de Madrid - Madrid, Spain
Study Abroad Program (September 2024 - December 2024)
Honors: Recognized with Notable (Very Good) academic distinction
Relevant coursework:

• Machine Learning in Healthcare (10/10 - Excellent)
• Creativity and Innovation (9.7/10 - Excellent)
• Web Analytics (8.5/10 - Very Good)
• Computer Networks

Projects

• 1. Identifying Evolvability-Enhancing Mutations via Computational Models
  October 2024 - Present (expected: April 2025)
  • Overview: Traditional biological evolution experiments take months, years, or even decades to complete, but computational models allow us to study thousands of generations per minute. With this capability, I am researching how certain mutations improve evolvability using NK landscapes, a mathematical model of fitness landscapes.
  • Key Activities:
    • Designing computational experiments to analyze fitness data
    • Testing various evolvability metrics to quantify adaptability
    • Running large-scale simulations on Grand Valley State University’s high-performance computing cluster
    • Comparing fitness landscapes with and without specific mutations to identify trends in evolvability
  • Impact: Identifying patterns that reveal how specific mutations enhance a population’s ability to adapt over time, contributing to a deeper understanding of evolvability in biological and artificial systems
  • Mentor: Dr. Austin Ferguson, Assistant Professor, College of Computing, Grand Valley State University
  • GitHub Repository: https://github.com/FergusonAJ/quantifying_evolvability
• 2. Automatic Atrium Segmentation via Machine Learning
  September 2024 - December 2024
  • Overview: Traditional manual segmentation of atrial structures (regions of the heart) in medical imaging is time-consuming and varies between observers. Using machine learning, I developed a model for automatic 3D segmentation of the atrium to improve accuracy and efficiency in cardiac image analysis.
  • Key Activities:
    • Preprocessed 3D medical imaging data to ensure high-quality inputs
    • Built data pipelines to automate and streamline processing
    • Optimized mesh alignment using neural networks to improve segmentation accuracy
  • Impact: Improved the speed and precision of atrial segmentation, reducing manual effort and enhancing diagnosis and treatment planning in cardiology
  • Mentors:
    • Dr. Gonzalo Ricardo Ríos Muñoz, Assistant Professor, Bioengineering Department, Universidad Carlos III de Madrid (UC3M)
    • Dr. Pablo Martínez Olmos, Associate Professor, Signal Theory and Communications Department, UC3M
    • Dr. Antonio Artés Rodríguez, Professor, Signal Theory and Communications Department, UC3M
  • GitHub Repository: https://github.com/officialconfuzius/cardiologyml
• 3. Exploiting Phylogenetic Analysis to Improve Evolutionary Search Algorithms
  September 2023 - July 2024
  • Overview: I collaborated with Dr. Alexander Lalejini to enhance evolutionary search algorithms using phylogenetic analysis. We improved optimized problem-solving by integrating evolutionary biology insights into computational methods.
  • Key Activities:
    • Designed and executed experiments on a high-performance computing cluster
    • Developed novel subsampling methods for optimization problems
    • Analyzed experimental data to assess algorithm performance improvements
    • Co-authored a peer-reviewed paper and presented our work at the 2024 Genetic and Evolutionary Computation Conference
  • Impact: Our work demonstrated that incorporating phylogenetic analysis into evolutionary search algorithms can significantly enhance their efficiency and effectiveness in solving complex optimization problems.
  • Mentor: Dr. Alexander Lalejini, Assistant Professor, College of Computing, Grand Valley State University
  • Publication: Runtime phylogenetic analysis enables extreme subsampling for test-based problems
  • GitHub Repository: https://github.com/amlalejini/phylogeny-informed-subsampling
• 4. Procedurally-generated 2D Role-playing Game
  January 2024 - April 2024
  • Overview: In a software engineering course, I collaborated with a team to develop a 2D role-playing game featuring procedurally generated content. Our goal was to enhance replayability and player engagement by ensuring unique gameplay experiences with each session.
  • Key Activities:
    • Designed core game mechanics, including character progression and combat systems
    • Implemented procedural content generation algorithms to create dynamic environments and quests
    • Conducted playtesting sessions to gather feedback and refine gameplay elements
  • Impact: Delivered a functional game prototype that demonstrated the potential of procedural generation to enhance replayability in role-playing games.
  • GitHub Repository: https://github.com/Marcos-Sanson/Procedural-RPG
• 5. Meteorological Software Application
  May 2023 - November 2023
  • Overview: During my internship at Parque Náutico de Castrelo in Spain, I developed a Python-based application to automate the creation of weather databases and real-time data visualization. This project aimed to improve environmental monitoring and support projects like tree planting and erosion management.
  • Key Activities:
    • Developed data pipelines to automate the processing of multilingual datasets
    • Integrated data visualization tools to monitor environmental conditions
    • Collaborated with a cross-cultural team to ensure the application's effectiveness
  • Impact: Improved weather data processing speeds by over 200,000%, significantly increasing the efficiency of environmental and meteorological monitoring.
  • GitHub Repository: https://github.com/Marcos-Sanson/MeteoGalicia-Application
• 6. Integrated Machine Learning for Robot Control
  October 2022 - September 2023
  • Overview: I collaborated with Dr. Jared Moore at Grand Valley State University to integrate machine learning and evolutionary algorithms for controlling a digital quadruped robot. Our goal was to enhance the robot's gait and control efficiency.
  • Key Activities:
    • Designed experiments and analyzed data to optimize algorithms
    • Pretrained neural networks using supervised learning before applying neuroevolution techniques
    • Refined hyperparameters to improve algorithmic performance
    • Developed tools for experiment execution and data analysis, ensuring reproducibility and integrity of results using frameworks like PyTorch
  • Impact: Improved the efficiency and adaptability of quadruped robot control systems, contributing to advancements in evolutionary robotics.
  • Mentor: Dr. Jared Moore, Associate Dean of Undergraduate Studies & Outreach, Grand Valley State University
• 7. Spot Micro Agile Robot Dog
  October 2021 - May 2022
  • Overview: Working with another student, I developed a fully operational robot inspired by Boston Dynamics' Spot agile mobile robot. Our goal was to create a cost-effective, functional quadruped robot for both disaster relief and research purposes.
  • Key Activities:
    • Developed Python-based software enabling remote control via Bluetooth
    • Integrated hardware components, including a Raspberry Pi, 3D-printed parts, servo motors, ultrasonic sensors, and an LCD display
    • Conducted extensive testing and debugging to ensure functionality across various environments
    • Published project documentation and contributed open-source code to the robotics community
  • Impact: Created a functional quadruped robot that serves as a platform for further research and development in robotics, and is still active to the present day.
  • Project Website: https://sites.google.com/view/senior-tech-project