About Me

Long before I wrote my first program, I was captivated by mathematics—patterns in numbers, the elegance of proofs, and the sense that rigorous structure could unlock entire worlds of possibility. That early curiosity naturally evolved into an interest in computation: if mathematics provided the language of abstraction, computing became the instrument to animate those abstractions at scale.

I formalized that intuition while studying at the Institute of Mathematics and Statistics (IME‑USP), where the boundary between pure theory and practical systems felt deliberately porous. Courses in algorithms, data structures, compilers, and numerical methods reinforced a conviction that performance is not just an optimization phase—it is a design mindset. I became fascinated with the question: how do we transform a mathematically clean model into a system that remains efficient, predictable, and reliable under real workloads?

I hold graduate credentials from the University of São Paulo (USP) and completed an academic term at the University of Toronto focused on parallel computing, distributed systems, and advanced compiler design—strengthening a binational perspective on systems performance research and engineering rigor.

That question led me deep into parallel and distributed computing. I explore scheduling, load balancing, concurrency control, memory hierarchies, and heterogeneous acceleration (especially GPUs) to push throughput and scalability without sacrificing determinism or correctness. I am particularly interested in how theoretical limits (Amdahl, Gustafson, roofline models) map onto practical engineering trade‑offs in modern architectures.

Professionally and academically, I focus on building and evaluating systems that behave robustly under contention, failure, and growth. I value reproducibility: benchmarks should be defendable, artifacts should be inspectable, and performance claims should survive scrutiny. This emphasis on measurable truth ties back to the precision I admired in mathematics—it keeps engineering honest.

Beyond the core of systems performance, I enjoy exploring compilers and program analysis as enabling technologies: better intermediate representations, static analysis, and transformation pipelines widen the space of feasible optimizations. The interplay between symbolic reasoning and low‑level mechanics remains a constant source of motivation.

Today, my work sits at the intersection of theory, tooling, and execution: taking principled models, embedding them in real systems, measuring their behavior, and iterating. It is an ongoing narrative—rooted in early mathematical curiosity—driven by a desire to make complex computation more transparent, efficient, and trustworthy.

The University of Toronto is a leading global public research university. My academic term there broadened my perspective on parallel computing, distributed systems, and advanced compiler design—complementing and extending the systems foundations developed at IME‑USP.

• Official site: utoronto.ca
• Computer Science Department: web.cs.toronto.edu

The University of São Paulo (USP) is a leading public research university in Latin America. I studied at the Institute of Mathematics and Statistics (IME‑USP), a renowned center for mathematics, statistics, and computer science.

• Official site: usp.br
• Institute of Mathematics and Statistics (IME‑USP): ime.usp.br

IME‑USP is a renowned institute at USP spanning mathematics, statistics, and computer science. It provided the theoretical and practical foundations of my CS formation.

• Official site: ime.usp.br

The Sociedade Brasileira de Computação (SBC) is Brazil’s leading professional association for computer science and computing education. It promotes research, education, and technology dissemination through conferences, journals, and community initiatives across the country.

• Official site: sbc.org.br