About Me

About Me

Hi, I’m Fred - an enthusiast of technology, mathematics, and computing with a passion for solving complex problems that lie at their intersection. My journey began with a deep interest in mathematical modeling and theoretical physics, which led me to complete my BSc at UCL, where I specialized in computational approaches and mathematical analysis. During my studies, I developed a particular fascination with using computational methods to analyze nonlinear, noisy, and complex systems, culminating in a research project on Cellular Automata simulations for modeling forest fires.

My professional experience spans both startup and enterprise environments. At NAAMA Studios, a research-focused laser technology startup of 40+ people, I worked as a Systems Engineer developing automated reporting systems and optimizing laser system efficiency to help achieve the company’s goal of improving efficacy in dermatological operations like tattoo removal. Later, at Playtech, a corporation of 6,000+ employees, I worked on high-performance sports event-based trading systems serving millions of users. These experiences gave me invaluable insight into how businesses of all sizes leverage data and technology to make strategic decisions, and how data science and machine learning are becoming increasingly crucial in our modern society.

I’m currently pursuing an MSc in Computational Statistics and Machine Learning at UCL, where I’m diving deeper into the mathematical foundations of AI while looking to find a place to combine my love of research and theory with creating and optimizing software. I’m particularly passionate about both exploring theoretical advances in machine learning and grounding these ideas in practical applications that solve real-world problems. This drive has led me to participate in numerous hackathons, including placing as a finalist in the Anthropic London Hackathon 2023, where I developed an AI-powered code correction tool for teaching new developers.

Looking ahead, I’m excited to work at the frontier of technology, with particular interests in machine learning, software systems, and computational approaches to complex problems. My background in physics, combined with my engineering experience across different business environments and ongoing ML studies, gives me a unique perspective on tackling challenging problems - always with an eye toward making meaningful contributions while ensuring practical applicability.

Career Journey & Impact

Software Development at Playtech (Jan 2023 - Aug 2024)

As a Backend Content Developer, I made significant contributions to the sports events driven trading systems:

  • Implemented an asynchronous bet-building service using Python, achieving an 80% increase in return rates.
  • Developed microservices serving real-time data to millions of users for major betting companies.
  • Led documentation initiatives across three teams, reducing new developer onboarding time and search time.
  • Optimized database operations, achieving 90% reduction in processing costs.
  • Conducted technical interviews and mentored new team members.

Systems Engineering at NAAMA Studios (June 2021- Apr 2022)

As an R&D Systems Engineer, I:

  • Enhanced laser system efficiency through hardware-software optimization.
  • Developed automated KPI reporting systems using cloud infrastructure.
  • Created custom LaTeX report generation pipelines.
  • Bridged hardware and software domains in a fast-paced startup environment.

Academic Background

MSc Computational Statistics and Machine Learning, University College London (Sep 2024 - Sep 2025)

Focusing on kernel methods, approximate inference, deep learning, bayesian methods, markov chains, sampling methods, convex optimisation, reinforcement learning, and probabilistic modeling

BSc Theoretical Physics, University College London (Sep 2018 - July 2022)

Specialized in computational physics and mathematical modeling

  • Research Project: Cellular Automata Simulation for Forest Fires
  • Key Modules: Computational Physics, Mathematical Methods, Statistical Physics

Technical Expertise

Programming & Development

Proficiency Levels: Fluent (F) | Advanced (A) | Working Knowledge (WK) | Beginner (B)

Core Languages

  • Python (F): NumPy, PyTorch, TensorFlow, Django, scikit-learn, Numba, JAX
  • C/C++ (WK): Performance optimization, system programming
  • SQL (A): Oracle, SQLite, query optimization
  • JavaScript (WK): Web development, frontend integration
  • Bash (A): Automation, system administration

Specialized Technologies

  • Machine Learning Frameworks: PyTorch, TensorFlow, JAX, Ivy
  • Cloud Platforms: AWS, GCP, Azure
  • Database Systems: MongoDB, Oracle, SQLite
  • Message Brokers: Apache Kafka
  • Development Tools: Git, Docker, Kubernetes
  • CI/CD: Jenkins, GitHub Actions

Language Skills

  • English (Fluent)
  • Portuguese (Advanced)

Professional Development

  • Machine Learning Specialization (DeepLearning.AI)

I’m always open to discussing new opportunities and collaborations in machine learning, software engineering, or research roles. Feel free to connect with me on LinkedIn or check out my projects on GitHub.