Skixkk
Quantitative Researcher & Full Stack Engineer exploring the intersection of Chemistry, Scientific Computing, AI and Software Engineering.
Background
My academic background is rooted in chemistry, while my professional and technical development has expanded into quantitative research, machine learning, scientific computing and full-stack software engineering.
I use programming and mathematical modeling as tools for solving problems across scientific research, financial data analysis, artificial intelligence and engineering. My current focus is moving toward computational chemical engineering and scientific software development.
- Chemistry and chemical science foundation
- Quantitative research and systematic data analysis
- Machine learning and artificial intelligence
- Full-stack and cross-platform software engineering
- Scientific computing and numerical modeling
- Open-source scientific software development
Research Direction
My long-term research direction is Open Chemical Scientific Computing, combining chemical science, chemical engineering, numerical methods, artificial intelligence and software engineering.
- Chemical thermodynamics and equations of state
- Phase equilibrium and flash calculation
- Chemical kinetics and reaction engineering
- Process simulation and process systems engineering
- Numerical analysis, ODE / DAE and nonlinear solvers
- Optimization and optimal process operation
- AI-assisted chemical engineering and surrogate modeling
- Scientific software architecture and open-source development
Technical Background
Scientific Computing
- Python, NumPy, SciPy, Pandas and Matplotlib
- Numerical analysis and scientific data processing
- Ordinary and differential-algebraic equations
- Numerical optimization and mathematical modeling
- GPU computing and PyTorch-based scientific workloads
Artificial Intelligence
- Machine learning and deep learning
- Natural language processing
- Computer vision and vision-language models
- Time-series modeling and prediction
- AI-based industrial monitoring and anomaly detection
Software Engineering
- TypeScript, JavaScript, Python, Java and C++
- Vue, Nuxt, React, Vite and Tailwind CSS
- Node.js, FastAPI, Django and Spring Boot
- Electron and cross-platform application development
- PostgreSQL, Redis, Docker and CI/CD
Selected GitHub Repositories
My repositories cover quantitative research, artificial intelligence, scientific computing, developer tooling and engineering infrastructure.
Quantitative Research
- comoon — Financial information and quantitative research platform for event-driven data, market information and research workflows.
AI and Industrial Monitoring
- mage_monitor — AI and computer-vision experiments for industrial visual monitoring and anomaly detection.
- qwen_vl_monitor — Vision-language model experiments for structured industrial monitoring and anomaly analysis.
Scientific Computing and Infrastructure
- check-cuDNN-with-PyTorch — CUDA, cuDNN and PyTorch environment verification utilities.
Developer Tools
- DocAutoFlow — Document automation workflow based on Pandoc and LibreOffice.
- markpandocview — Markdown and Pandoc-based document preview and conversion tooling.
Open Chemical Scientific Computing
I am studying the architecture and implementation of open-source scientific software such as chemical thermodynamics libraries, process simulators, reaction-kinetics frameworks and computational fluid-dynamics systems.
- Understand chemical thermodynamics and property calculations
- Implement equations of state and phase-equilibrium calculations
- Build flash and unit-operation simulation components
- Study nonlinear equations, ODE / DAE systems and numerical solvers
- Construct process simulation and optimization workflows
- Introduce machine learning and physics-informed surrogate models
- Develop open-source scientific computing infrastructure
Open-source Development
I participate in open-source engineering through source-code research, issue analysis, bug fixing, feature implementation, documentation improvement and pull-request collaboration.
- Source-code analysis and architecture research
- Bug reproduction and debugging
- Feature implementation and refactoring
- Documentation and technical writing
- GitHub Issues and Pull Requests
- GitHub Actions and CI/CD workflows
- Open-source engineering practices and upstream collaboration
Research Roadmap
1. Chemical Foundations
Strengthen chemical engineering fundamentals including thermodynamics, transport phenomena, reaction engineering, separation processes and process safety.
2. Scientific Computing
Develop deeper capabilities in numerical analysis, linear algebra, nonlinear systems, ODE / DAE solvers, sparse computation and high-performance scientific computing.
3. Process Simulation
Progress from individual thermodynamic and unit-operation models toward equation-oriented process simulation and dynamic process modeling.
4. Optimization and AI
Combine process models with mathematical optimization, machine learning, surrogate models, parameter estimation and intelligent process control.
5. Scientific Software
Build reusable open-source computational infrastructure that connects physical models, numerical algorithms, scientific data and modern software engineering.
Long-term Direction
The long-term objective is to work at the intersection of computational chemical engineering, scientific computing, artificial intelligence and open-source software.
The intended development path is:
Chemistry → Mathematical Modeling → Scientific Computing → Process Simulation → Optimization → AI → Digital Twin → Open Scientific Software
Contact and Projects
The source code, experiments and ongoing research projects are available through GitHub.