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About

3 min read Updated:
Alex Towell

About Me

I’m Alex Towell, a research engineer and computer scientist with a passion for building elegant solutions to complex problems. Currently pursuing my PhD in Computer Science at SIUE, I hold dual master’s degrees in Computer Science and Mathematics/Statistics from the same institution. As a cancer survivor, I bring a unique perspective on resilience and determination to my work, approaching challenges with both theoretical depth and practical engineering expertise.

Research Philosophy

I believe in the power of open source to accelerate scientific progress. My work spans the intersection of machine learning, statistical computing, and software engineering, with a focus on creating tools and methodologies that are both theoretically sound and practically useful. Every project I undertake aims to contribute meaningfully to the broader scientific community.

Areas of Expertise

🤖 Machine Learning & AI

  • Large Language Models: Fine-tuning, prompt engineering, and applications
  • Statistical Learning: Probabilistic models, inference, and uncertainty quantification
  • Neural Architecture: Design and optimization of deep learning systems
  • Explainable AI: Making complex models interpretable and trustworthy

📊 Statistical Computing

  • Reliability Engineering: Maximum likelihood estimation for censored and masked failure data
  • Computational Statistics: Bootstrap methods, Monte Carlo simulation, and resampling
  • Survival Analysis: Weibull distributions and series systems reliability
  • Bayesian Methods: Hierarchical models and MCMC techniques

🔐 Cryptography & Security

  • Encrypted Search: Secure index designs preserving confidentiality
  • Privacy-Preserving Algorithms: Homomorphic encryption and secure multiparty computation
  • Information Security: Query obfuscation and adversarial defense strategies
  • Applied Cryptography: Implementation of cryptographic protocols and systems

💻 Software Engineering

  • Open Source Development: 50+ public repositories with active maintenance
  • Package Development: Libraries published to PyPI, npm, crates.io, vcpkg
  • API Design: RESTful services and elegant library interfaces
  • Systems Programming: C++, Rust, and performance-critical applications

Education

Ph.D. Computer Science | Southern Illinois University Edwardsville | In Progress Focus: Machine Learning and Statistical Computing

M.S. Computer Science | Southern Illinois University Edwardsville | 2015 GPA: 4.0/4.0 • Focus: Encrypted Search and Information Retrieval

M.S. Mathematics and Statistics | Southern Illinois University Edwardsville | 2023 GPA: 3.9/4.0 • Focus: Statistical Learning and Reliability Analysis

B.S. Computer Science | Southern Illinois University Edwardsville | 2011 GPA: 3.6/4.0 • Minor in Mathematics

Technical Stack

Languages & Frameworks

Expert: C/C++, Python, R, LaTeX Proficient: Rust, JavaScript/TypeScript, Julia, SQL Familiar: Go, Haskell, Lisp, Java

Technologies & Tools

Data Science: NumPy, Pandas, SciPy, PyTorch, TensorFlow, scikit-learn Development: Git, Docker, CMake, Linux, Shell Scripting Cloud & DevOps: AWS, GitHub Actions, CI/CD pipelines Databases: PostgreSQL, MongoDB, Elasticsearch, Redis

Impact & Metrics

  • 50+ Open source projects and libraries
  • 1M+ Total package downloads across registries
  • 500+ GitHub stars on various projects
  • 5 Peer-reviewed publications
  • 12 PyPI packages maintained

Current Focus

I’m currently working on several exciting projects at the intersection of machine learning and software engineering:

  • Developing novel techniques for synthetic data generation in LLM training
  • Building probabilistic data structures for approximate computing
  • Creating educational resources for advanced statistical computing
  • Contributing to open source machine learning frameworks

Philosophy on Open Source

I believe that the best software is built in the open. Every line of code I write is an opportunity to contribute to human knowledge. My projects are designed not just to solve problems, but to teach, inspire, and enable others to build upon them.

Let’s Connect

I’m always interested in collaborating on interesting problems, especially those involving:

  • Novel applications of machine learning
  • Statistical computing challenges
  • Open source tool development
  • Research at the intersection of theory and practice

Feel free to reach out through any of these channels:

“The best way to predict the future is to invent it.” - Alan Kay