Tsinghua University

Graduate-level ML: kernel methods, ensemble learning, deep architectures, optimisation and the probabilistic foundations behind them.

Replication, partitioning, consistency models and distributed transactions - how databases stay correct and fast across many machines.

Large-scale data processing with distributed compute frameworks, storage formats and the systems trade-offs of working at scale.

Advanced algorithm design and the combinatorial mathematics behind complexity, counting and optimisation problems.

From classical language models to transformer-based architectures for understanding and generating text.

The rendering pipeline, transformations, shading and rasterisation - the maths and GPU work behind real-time 3D.

How search engines index, rank and retrieve documents: inverted indexes, ranking models and relevance evaluation.

Modelling sequential data: stationarity, forecasting, and signal models - the basis for my BioTemporal-HAR research.