Tech executive and problem solver. I specialize in turning complex research into real-world products. With a background in Physics, Math, and Computer Science, I build and scale platforms using HPC, Big Data, and AI.

• ✉️ kentanaka3@outlook.com
• 🌐 kentanaka3.github.io
• 💼 linkedin.com/in/kentanaka3
• 🧑‍💻 github.com/kentanaka3
• 📍 Guadalajara, Mexico

Research-driven leader who guides multidisciplinary teams from hypothesis to production, aligning scientific rigor with organizational priorities. My focus is simple: taking deep technical concepts and turning them into stable, high-performance tools that solve actual business problems.

• Domain (Big Data + HPC): Architecting distributed systems and parallel workflows for high-throughput research and enterprise workloads.
• Leadership (Research-to-Impact): Translating academic research into production systems, roadmaps, and stakeholder value.
• Expertise (Physics-Informed AI): Integrating scientific models with ML for trustworthy, explainable, and robust predictions.
• Strategy (Platform Architecture): Designing resilient backends, APIs, and data platforms with governance and scalability in mind.

Systems & Computing: High Performance Computing (HPC), Big Data & Distributed Systems, Parallel Algorithms, CUDA, OpenMP, OpenACC, MPI, SLURM, Linux, Docker.

Data & AI: Physics-Informed AI, Machine Learning, Deep Learning (PyTorch), Mathematical Modeling, Data Engineering, MLOps, Data Visualization.

Development: REST APIs, Backend Architecture, Python, C/C++, SQL, JavaScript, HTML5/CSS3, Git, CI/CD Pipelines.

Languages: Spanish, English, Japanese, Italian, Chinese.

Software Developer II · ORACLE de México Jul. 2020 - Aug. 2022 · Guadalajara, Mexico

• Architected and deployed a production backend system facilitating automated analytics and solutions via a scalable customer-facing API.
• Engineered automated patch recommendation workflows, significantly reducing diagnostic latency and enhancing critical system reliability.
• Spearheaded the integration of ML models (Linear Regression, CNNs) into Oracle Database diagnostics to create an intelligent, automated patch recommendation engine.

R&D Modeling & Simulation Engineer (Intern) · HP Labs Jun. 2019 - Jun. 2020 · Guadalajara, Mexico

• Pioneered advanced numerical simulations for thermal processes in 3D printing, leveraging mathematical modeling to maximize print fidelity.
• Accelerated simulation throughput by implementing parallel computing algorithms on HPC resources for complex geometry analysis.
• Partnered with material scientists to validate theoretical models against experimental benchmarks, effectively bridging the computational-to-manufacturing gap.

PhD in Applied Data Science and Artificial Intelligence Jan. 2023 - Present · University of Trieste (UniTs)

• Implemented distributed training and inference pipelines across multi-node GPU clusters, leveraging parallel computing techniques (MPI, CUDA) to handle large-scale seismic datasets efficiently.
• Engineered a custom validation framework using bipartite optimization and weighted similarity scores to ensure high-fidelity event matching and maintain strict accuracy against official reference catalogs.

Master in High Performance Computing Sep. 2023 - Aug. 2024 · International School for Advanced Studies (SISSA)

• Developed an end-to-end ML pipeline for automated, real-time earthquake detection, enabling regional monitoring systems to instantly process and characterize seismic events at scale.
• Architected HPC-optimized workflows using deep learning models (Transformers and CNNs) to ingest massive, continuous data streams, drastically increasing processing speed compared to legacy CPU systems.

Postgraduate Diploma in Quantitative Life Science Sep. 2022 - Aug. 2023 · International Centre for Theoretical Physics (ICTP)

BSc. Physics Aug. 2016 - Dec. 2021 · University of Guadalajara

• Simulated complex social systems using 1D Ising spin models to analyze the dynamics of racial segregation and social pressure. By integrating local and global exchange dynamics (Kawasaki and Schelling models), I identified how environmental "noise" and "temperature" impact system stability, equilibrium, and the transition from order to chaos.
• Quantified system behaviors through rigorous statistical analysis, extracting critical power-law exponents ($z \approx 2.48$, $\lambda \approx 1.15$, $\theta \approx 0.46$) to define domain growth and persistence. I optimized the computational simulations to resolve numerical difficulties in ground-state transitions, moving the project from theoretical abstracts to high-fidelity numerical results.