A record of professional experience, technical explorations, and continuous education. Each project represents problems solved and value delivered—for clients, teams, and organizations.
Lead AI turning generative AI into real impact. Building prototypes and MVPs, translating AI to concrete business value for clients, and helping build an excellent AI team in the agri-food sector.
Led AI initiatives for protein analysis and bioinformatics. Built and deployed machine learning systems for mutation effect prediction, achieving production-grade accuracy on complex biological data. Direct client engagement and technical leadership.
Voluntary presentation on AI and data science topics. Sharing knowledge and perspectives on practical applications of machine learning in business contexts.
Featured in discussion on AI startups and the practical realities of building AI products. Sharing perspectives on what works—and what doesn't—in applied machine learning.
Full-stack development and technical problem-solving. Early experience bridging technical implementation with client requirements.
Exploring RFDiffusion and how diffusion models are revolutionizing de novo protein design. Technical analysis with projections for the field.
Technical breakdown of using neural networks for antibiotic discovery—from molecular representations to production insights.
Detailed notes from Google's foundational NLP paper. Key insights and practical takeaways.
Analysis of large-scale adversarial representation learning and its implications for unsupervised learning.
Practical techniques for encoding and processing protein sequences in ML pipelines.
Implementation guide for secure connections in Rust web applications. Production-ready patterns.
Building a rapid-response data API during the pandemic. Real-time data aggregation and visualization.
A personal favorite—because data scientists need to eat too.
Advanced methodologies in data science, machine learning systems, and practical AI applications in industry contexts. Graduated cum laude.
Foundation in computational biology, genomics, and bioinformatics algorithms. Where data science met life sciences.
Project finance management and mastering project complexity. Skills for leading technical teams and large-scale initiatives.
First experience applying machine learning to real protein analysis challenges. Foundation for current work.
Agricultural biotechnology research. Experience with large-scale genomics data in production contexts.