I am an undergraduate in Statistics with strong interests in Artificial Intelligence, Time-Series Modeling, and Quantitative Finance.
My work focuses on learning robust structure from noisy, non-stationary systems, with current research spanning wind power forecasting, sparse Mixture-of-Experts architectures, and data-driven market analysis.
I am particularly interested in building models that are not only accurate, but also efficient, transferable, and grounded in real-world constraints.
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AI for Energy Systems
Edge-native wind power forecasting, spatiotemporal learning, robust modeling under noise and deployment constraints -
Quantitative Finance
Statistical signal extraction, market regime modeling, high-noise financial time series, alpha-oriented research -
Machine Learning
Sparse Mixture-of-Experts (MoE), representation learning, scientific machine learning, time-series forecasting
- Ultra-LSNT
An efficient sparse MoE framework for edge-native wind power forecasting, designed for noisy environments and resource-limited deployment settings.
- 🏃 Half-marathon runner | PR: 1h 48m
- 📖 Interested in philosophy, human nature, and long-term thinking
- ✍️ Exploring writing as a parallel form of structured expression
- Email: ljylikezmn999@gmail.com
- GitHub: B1ue13E