CV

Education

• B.S. in University of Waterloo, Ontario, Canada, 2020-2025

Work experience

• January to August 2024: Data Scientist
  • University of Waterloo, Mechanical and Mechatronics Engineering Department
  • I developed a novel GNN-based method for river ice classification to support automated monitoring in Montreal. I presented my research at Geoinformatics 2024, gave a 20-minute talk, and volunteered at the conference.
  • Supervisor: Professor Andrea Scott
• April to September 2023: Research Assistant
  • University of Waterloo, Applied Math Department
  • I integrated first-order PDEs, like Advection and Burgers equations, into GNNs to tackle over-smoothing and enhance performance in deep architectures. Collaborated with faculty and peers, contributing to discussions and technical reports.
  • Supervisor: Professor Hans De Sterck
• April to September 2022: Image Algorithm Engineer
  • Aqrose Technology
  • At Aqrose Technology, a company specialising in advanced image processing and AI solutions for industrial applications, I developed a new algorithm for detecting data matrix codes in images, aiming for higher accuracy and reduced processing time. This role enhanced my leadership and teamwork skills while allowing me to explore machine learning in computer vision independently.
• September to December 2021: Research Assistant
  • Chinese Academy of Science, Institute of Semiconductors
  • Conducted research in underwater image processing at CAS, reviewing literature and developing a novel dehazing method. Gained expertise in OpenCV and C++ and deepened knowledge of image processing techniques.

Skills

• Programming & Software Development
  • Python (NumPy, SciPy, PyTorch, TensorFlow)
  • C/C++ (OpenCV, Eigen)
  • MATLAB, Julia, Maple, SQL, Racket
  • Git, Linux System, Latex, Markdown, HTML
• Numerical Methods & Scientific Computing
  • Finite Element Methods (FEM), Finite Difference Methods (FDM)
  • Solvers for PDEs & BVPs (Crank-Nicolson, Advection-Diffusion models)
  • High-performance computing (HPC), mixed-precision computing
• Machine Learning & AI
  • Graph Neural Networks (GNNs), Geometric Deep Learning
  • Image Processing & Computer Vision (DCP-based dehazing, SAR image analysis)
  • Optimization techniques (SGD, Adam, Newton’s method)
• Soft Skills & Communication
  • Research presentation & academic writing (Conference talks, papers)
  • Leadership & teamwork (Led a multicultural research team)

Publications

Learnable Weight Graph Neural Network for River Ice Classification

Qu, Y.; Soleymani, A.; Sudom, D.; Scott, K.A. Learnable Weight Graph Neural Network for River Ice Classification. Proceedings 2024, 110, 30. https://doi.org/10.3390/proceedings2024110030

Enhanced Underwater Image Dehazing Using an Improved Dark Channel Prior Method

First-order PDES for Graph Neural Networks: Advection And Burgers Equation Models

Talks

Conference Proceeding talk 3 on Relevant Topic in Your Field

March 01, 2014

Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA

Talk 2 on Relevant Topic in Your Field

February 01, 2014

Talk at London School of Testing, London, UK

Tutorial 1 on Relevant Topic in Your Field

March 01, 2013

Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA

Talk 1 on Relevant Topic in Your Field

March 01, 2012

Talk at UC San Francisco, Department of Testing, San Francisco, California

Teaching

Teaching experience 2

Teaching experience 1

Service and leadership

• Currently signed in to 43 different slack teams