I enjoy making things. Here are a selection of projects that I have worked on over the years.
TripDataset Machine Learning Project This project is a complete implementation of machine learning pipelines applied to the TripDataset, focusing on data preprocessing, classification, and regression tasks, including: 🧹 Data preprocessing and cleaning (handling missing values, outlier detection, normalization, and feature engineering) 🤖 Model training for classification and regression (various ML algorithms for categorical and continuous prediction tasks) 📊 Performance evaluation and metrics (accuracy, F1-score, RMSE, and other evaluation techniques) 🔍 Exploratory data analysis and visualization (insightful plots for feature relationships, distribution, and model performance)
ViT-torch: Vision Transformer on CIFAR-10 (PyTorch) This project is a complete implementation of Vision Transformer (ViT) applied to small-scale datasets (especially CIFAR-10), including: 🎯 Model implementations with various configurations (native ViT, ResNet+ViT hybrid, different patch/heads/blocks setups, Stochastic Depth/DropPath, etc.) 🌹 Training and evaluation scripts (with learning rate schedulers: Warmup/Linear/Cosine/Constant-Cosine/Warmup-Constant-Cosine) 🧩 Data augmentation (RandomCrop+Paste, MixUp, CutMix, RandAugment, and batch random augmentation) 📈 Visualization and analysis (attention maps, attention distance, gradient rollout, feature maps, positional embedding similarity)
🎯 Voice Activity Detection (VAD), or voice endpoint detection, identifies time segments in an audio signal containing speech. This is a critical preprocessing step for automatic speech recognition (ASR) and voice wake-up systems. This project lays the groundwork for my upcoming ASR project 🤭. 📈 Workflow Overview: The VAD pipeline processes a speech signal as follows:Preprocessing, Framing, Windowing, Feature Extraction, Binary Classification, Time-Domain Restoration 🍻 Project Highlights: I conducted extensive experiments comparing frame division methods (frame length and shift) and model performances, with rich visualizations. For details, see the report in ‘vad/latex/’. If you’re interested in voice technologies, let’s connect! 🔗 For more details, please visit my blog VAD
Establishment and solution of mathematical optimization model This project is a lab of the course “Linear Optimization and Convex Optimization”. It discusses a classic optimization problem, the Water filling problem. Please refer to the project description file for details. In this project, I transformed the original problem into a classic optimization problem according to the mathematical derivation in the description file, and implemented two optimization algorithms, the gradient descent method and the Newton method, and proposed a Binary-search algorithm for the original problem. At the same time, I built two linear search modes and did a lot of comparative experiments. Please refer to the report file for details. In this project, I also compared my algorithm with Monkey-search as required. As the saying goes, 1xxxxx monkeys can’t write Shakespeare’s works. I am currently working on model optimization and convergence analysis. If you are interested in this, please come and communicate with me!
