Congshu Zou
I am a results-driven computer science professional specializing in machine learning, with hands-on experience in building and fine-tuning models using frameworks like PyTorch, Scikit-learn, and Pandas. I have developed automated pipelines, optimized large language models, and integrated advanced features into graph neural networks. My strong programming skills in Python, C++, and SQL, coupled with leadership, collaborative and analytical abilities, enable me to contribute cross-functional projects effectively. With experience in both academic research and professional environments, I am committed to delivering innovative, data-driven solutions and impactful results.
Education
M.C.S. in Computer Science Specializing in Machine Learning
Concordia University | Mila, Montreal, QC, Canada | Sept 2023 – May 2025
B.S. in Mathematics and Statistics and Computer Science
Concordia University, Montreal, QC, Canada | Sept 2020 - May 2023
B.S. in Chemistry
Fudan University, Shanghai, China | Sept 2008 - June 2012
Projects
Model Merging and Visualization
- Train models across multiple architectures to evaluate performance and interpretability.
- Experiment with various model-merging techniques to optimize task-specific outcomes.
- Visualize intermediate layers to enhance interpretability, contributing to more transparent and explainable AI models.
Aligning Language Model with User Search Intent in Query Reformulation
- Reformulated search queries using large language models (LLMs) to better align with user intent via an AI feedback pipeline.
- Developed an automated pipeline to rewrite and refine queries with LLMs, enhancing relevance and accuracy.
- Summarized test results and generated project reports.
Machine Learning for Medical and General Image Classification
- Developed deep learning models using ResNet-50 architecture to classify medical images as well as animal faces datasets, showcasing the power of transfer learning and pre-trained models.
- Optimized model performance and interpretability by applying techniques such as transfer learning and t-SNE visualization.
- Implemented feature extraction with pre-trained CNN models and enhanced classification accuracy using machine learning algorithms such as KNN and SVM.
Enhancing Graph Neural Networks with Chirality-Aware Layers
- Integrated chirality-aware layers into Graph Neural Network (GNN) models to capture 3D molecular structures.
- Evaluated model performance on various benchmarks to assess the impact of chirality-aware layers.
Guess a Number Game
- Developed a browser-based number-guessing game using HTML, CSS, and JavaScript.
- Implemented responsive and mobile-friendly design with numeric keyboard support and restart functionality.
Work Experience
Smardt Chiller Group
Mechanical Consultant Contractor | Jan 2023–June 2024
Mechanical Engineering Team Lead | Jan 2019–Dec 2022
Mechanical Designer | Feb 2014–Dec 2018
- Led a mechanical engineering team in collaboration with global sales, purchasing, and manufacturing departments to improve internal productivity and deliver high-quality products that met customer requirements.
- Developed automated Python scripts for generating standardized catalogues, reducing manual work time by 75%.
- Programmed tools to calculate product weight and dimensions for sales integration, enabling accurate estimation for internal teams and external clients.
- Analysed historical data and developed a project tracking system using Python and analytics tools, improving team performance monitoring and increasing efficiency by 30%.
FNZ
Junior Software Developer | Nov 2023–Dec 2023
- Gained hands-on experience with SQL, executing database queries for data extraction and manipulation.
- Used Jira for task tracking and organization, supporting project management best practices.