Hello, I am Rizky Johan Saputra. I am an aspire to be a AI/CS engineer with a strong focus primarily on the fields of artificial intelligence . However, I am also interested in working on hardware/software co-design and real-time systems due to its applicability alongside AI. In particular, my interest revolves around machine learning, memory systems, computer vision, system programming and deep learning. My work sits at the integration of theory-driven design and production-grade implementation.
I recently completed my B.S. in Computer Science and Engineering at Seoul National University, where my work spanned low-level systems, vision-based systems, compiler-style pipelines, and applied multimodal AI. I did my undergraduate thesis on The Application of Facial Emotion Recognition (FER) in the Detection and Measurement of Burnout and Prolonged Stress Levels. My ultimate goal is to create a tech based startup firm related to either machine learning, computer vision or robotics systems into various fields such as AI, medicine or farming.
Core Research and Engineering Focus
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Memory Allocation and Runtime Systems
Design of deterministic, policy-driven, and hierarchical memory allocators (PICAS, DIMCA, DRMAT). -
Real-Time Systems and Constraints
Explicit handling of predictability, fragmentation, priority routing and performance bounds. -
Compiler-Style Architectures
Pass-based pipelines, semantic validation, intermediate representations, and backend parity (e.g., SyntraLine++). -
Applied Multimodal AI and Vision Based Systems
End-to-end pipelines combining computer vision, temporal modeling, validation metrics, and explainability (FER-based burnout detection).
My Interpretation About Computer Systems
- Explicit invariants and clearly defined failure modes
- Mathematical or algorithmic justification before optimization
- Predictability over heuristics in system-critical paths
- Strong documentation and reproducibility as first-class concerns
Technical Toolkit
I work across the full stack of system building from low-level memory and runtime design to applied machine learning pipelines and deployment. Recently, I have also been developing high-level systems and focus on front-end systems by doing web development and learning AWS APIs to comprehend the functionality of modern systems. Below is a practical summary of the tools and environments that I utilized.
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Programming Languages:
Python, C/C++, Rust, Java, C#, SQL, Assembly, Verilog, Javascript, TypeScript, HTML, CSS, Ruby, OCaml and Shell/Bash. -
Machine Learning & Vision:
PyTorch and TensorFlow/Keras for training and experimentation, Hugging Face Transformersvfor NLP workflows, OpenCV for computer vision pipelines, and scikit-learn for classical ML. Whereas for analysis and reporting, NumPy, Pandas, Matplotlib (and occasional Seaborn) are utilized. -
Systems, Backend and Deployment:
Linux/macOS development, Git-based workflows, Docker for reproducible environments, FastAPI/Flask for lightweight services and APIs, Nginx/Apache basics for hosting, and CI with GitHub Actions. Comfortable working remotely over SSH and in terminal-first setups (tmux, Vim). -
Hardware and Embedded Exposure:
Familiar with FPGA/HDL workflows (Verilog/VHDL/SystemVerilog) and prototyping with Arduino / Raspberry Pi for hardware-adjacent experimentation. -
Workflow:
VSCode, Jupyter/Colab, GitHub, Makefiles, LaTeX/Overleaf for technical writing and structured documentation.
Multilinguality
Aside from programming, I am also multilingual in various languages. Below shows all the languages I am capable of speaking, with english as my preferred daily language.- English — Native/Fluent
- Indonesian — Native/Fluent
- Korean — Advanced/Intermediate
- Japanese — Fluent/Advanced
- Chinese — Intermediate/Basic
- German — Basic
- Base: Seoul and Jakarta
- Focus: Memory Systems, Computer Vision, Machine Learning, Real-Time Systems
- Work style: Applicability, Discussion Based, Research Grade, Detailed Analysis, Debugging
- Open to: Internship/Work Opportunities, Collaboration, Research Discussion, Team Project
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- Building: Customized Memory Allocators (DRMAT/PICAS/DIMCA) and Multimodal Scent Based AI System
- Exploring: Advanced AI systems, Vision Based Analysis, and Hardware/Software Co-Design
- Interested in: Internships / Works, Research Discussions and Collaborations
Prefer work that’s measurable: correctness, latency, memory, and reproducibility.