Recommendation & Search Algorithms · Machine Learning · Applied Mathematics
I am a Mathematics undergraduate at The University of Hong Kong, pursuing a second major in Computer Science and a minor in Finance. I build retrieval and ranking pipelines, reproducible machine-learning experiments, and numerical methods for inverse problems, with a particular interest in recommendation, search, and advertising systems.
- Multi-channel candidate retrieval, learning to rank, negative sampling, and temporal evaluation
- Applied machine learning with explicit validation, ablation, and reproducibility contracts
- Numerical methods for inverse problems, reduced-order modeling, and regularization
- Python and C++ systems that keep modeling logic separate from data, evaluation, and interfaces
Python · PyTorch · LightGBM · DuckDB
An offline retrieval-and-ranking system built on 31.8 million H&M transaction events.
- Combined nine heuristic, collaborative, learned, text, and image retrieval channels in a quota-aware K=500 candidate interface.
- Trained a LambdaRank model on 100,000 customer-week queries under four rolling temporal cutoffs; an untouched future week reached MAP@12 of 0.03448, a 3.94% relative gap from development.
- Ran bounded-memory inference for 1,371,980 customers across 138 shards and submitted final predictions scoring 0.03117 public / 0.03116 private MAP@12 on Kaggle.
C++17 · Information Retrieval · CMake · LLM APIs
A retrieval-augmented document question-answering system with a deterministic C++ search layer.
- Implemented tokenization, a custom ownership-aware binary-search-tree multimap, and an inverted index.
- Applied AND constraints within expanded term groups and unions across groups before grounded synthesis.
- Injected the model client so retrieval tests remain deterministic and credential-free; live generation reads credentials only from environment variables.
Python · scikit-learn · pandas · Matplotlib
A leakage-resistant study of personality, stage-fright, and behavioral-outcome prediction.
- Kept preprocessing and model selection inside development pipelines before one-time evaluation on an untouched 20% holdout.
- Evaluated 381 model-and-feature-subset configurations with a predeclared parsimony rule.
- Selected a three-feature Gradient Boosting model with 0.9651 holdout ROC-AUC, retaining a compact behavioral feature set.
Python · XGBoost · scikit-learn · pandas
A collaborative regression study comparing tree ensembles, regularized models, and principal-component regression on airfare data.
- The final XGBoost model reached RMSE ₹2,319.47 and R² 0.9896 on a 60,031-row held-out same-period test set.
- Examined model-estimated booking-time effects with partial dependence while keeping predictive associations distinct from causal claims.
- My recorded contributions cover code for Experiments 1 and 2 and the interpretation and discussion for Experiment 2.
As an HKU Summer Research Fellow, I study reduced-order reconstruction of source geometry from noisy final-time diffusion observations.
- Implemented 100 × 100 finite-difference simulations, Tikhonov regularization, and Monte Carlo noise and sensor experiments.
- Constructed covariance-designed Proper Orthogonal Decomposition bases and reduced reconstruction to a 20–40 dimensional solution space.
- Audited five numerical experiments against theoretical claims while preparing a manuscript-style research write-up.
I have also modeled urban rail route design under geometric and construction constraints, combining analytical linear-programming cases with Particle Swarm Optimization for multi-intersection layouts. This work was recognized in the S.-T. Yau High School Science Award.
Languages
Python · C++ · SQL · MATLAB
Machine Learning & Data
PyTorch · LightGBM · XGBoost · scikit-learn · pandas · NumPy · SciPy · DuckDB
Recommendation & Search
Candidate Retrieval · Collaborative Filtering · Two-Tower Models · LightGCN · LambdaRank · Negative Sampling · Temporal Validation · MAP/NDCG/Recall
Engineering & Research
Git · CMake · pytest · Jupyter · LaTeX · Numerical Optimization · Experiment Design
- HKU Summer Research Fellow in numerical inverse problems and reduced-order modeling
- GPA 3.98/4.30; Dean's List, HKU Undergraduate Entrance Scholarship, and Lee Shau Kee Scholarship
- Mathematics coursework in probability, stochastic processes, optimization, numerical analysis, and scientific computing
- Email: pingsenchow@outlook.com
- GitHub: github.com/Stanley-Chow
I am open to internship and research opportunities in recommendation, search, ranking, advertising algorithms, machine learning engineering, and applied data science.