ZHANG Rushan
张如山

Hi! I am ZHANG Rushan, a student from the Hong Kong University of Science and Technology. Welcome to my homepage!


Education

  • The Hong Kong University of Science and Technology (HKUST) — 2020 Fall – 2024 Spring
    • BEng in Aerospace Engineering and BSc in Computer Science
    • Top 2% in the class (No. 1 out of 41 students as of 2023 Fall)
    • CGA 3.979/4.300
    • Aerospace Engineering: Thermodynamics [A+]; Fluid Mechanics [A+]; Aerodynamics [A]; Aircraft Performance and Stability [A+]; Computational Fluid Dynamics [A+]
    • Computer Science: Programming with C++ [A+]; Object-Oriented Programming and Data Structures [A+]; Software Engineering [A+]; Design and Analysis of Algorithms; Deep Learning in Medical Image Analysis; Machine Learning [A-]
    • Awards:
      • HKIE Aviation Scholarship (Nominated) — Fall 2023
      • School of Engineering Dean’s List — Fall 2020-2021, Spring 2020-2021, Fall 2021-2022, Spring 2021-2022, Spring 2022-2023
      • HKSAR Government Scholarship Fund – Reaching Out Award — Fall 2022-2023
      • The BDR Scholarship – Academic Performance — 2022-2023
      • University’s Scholarship Scheme for Continuing Undergraduate Students — 2021-2022, 2023-2024
  • The Georgia Institute of Technology (Georgia Tech) — 2022 Fall
    • Exchange Student
    • CGA 4.000/4.000
    • Aerospace Engineering: Aerothermodynamics [A]; Jet and Rocket Propulsion [A]; Computational Fluid Dynamics [A]
    • Computer Science: Computer Vision [A]; Systems and Networks [A]; Introduction to Database Systems [A]

Publication

  • DoNet: Deep De-overlapping Network for Microscopy Instance Segmentation
    • Hao Jiang*, Rushan Zhang*, Yanning Zhou, Yumeng Wang, Hao Chen.
    • Co-first author
    • Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023
    • Paper available here
    • Codes available here

Experience

  • Iterative surrogate model optimization for transient fluid structure interaction (Summer research, Prof. Dr. Robert Katzschmann@ETH Zurich) — 2023 Summer – Present
    • Designing, implementing and evaluating surrogate models for fluid structure interaction, conducting optimization with the resultant surrogate models
    • Inspiration:
      • Modeling transient fluid flow as a Markov process to enable single-step prediction of flow evolution
      • Introducing shape representation from the field of computer vision to enable monolithic modeling of fluid-structure interaction
      • Introducing active learning techniques to reduce the number of samples required for optimization
    • Role: Project leader
    • Aiming for publication at ICML 2024 as the first author
  • Deep learning for medical image analysis (Undergraduate research, Prof. Chen Hao@HKUST) — 2022 Spring – 2024 Spring
    • Designing, implementing and evaluating a novel de-overlapping strategy for semi-transparent cervical cell segmentation
    • Inspiration:
      • Using extra information from the overlapping area and the non-overlapping area to guide the segmentation of the whole cell
    • Role: Co-first author
    • Publication: CVPR2023: DoNet: Deep De-overlapping Network for Microscopy Instance Segmentation
  • Designing a high-performance airfoil by advanced CFD and machine-learning methods (Final year design project for aerospace engineering, Prof. Fu Lin@HKUST) — 2023 Fall – 2024 Spring
    • Designing, implementing and evaluating machine-learning-based methods for airfoil shape optimization, and comparing results with numerical adjoint methods and experimental results
    • Inspiration:
      • Machine learning methods excel in identifying coarse global optima, while numerical adjoint methods excel in refining local optima
      • Combining these two methods could potentially yield improved results
    • Role: Project proposer and leader
  • Adversarial or reinforcement learning-based closed-loop training strategy for PINN-based fluid simulators that generalize (Final year project for computer science, Prof. Dit-Yan YEUNG@HKUST)  — 2023 Fall – 2024 Spring
    • Designing, implementing and evaluating a closed-loop training strategy for PINN-based fluid simulator with adversarial or reinforcement learning, to achieve enhanced generalizability
    • Inspiration:
      • Closed-loop strategy can more efficiently explore the solution space
      • Training strategies like RL and AL can be used to form a closed-loop process
    • Role: Project proposer and leader

Extracurricular Activities

  • Aerial robot development team member, HKUST ENTERPRIZE Robo Master Team — 2020 Fall – 2022 Spring
    • Collaborating in a team for mechanical design and manufacturing of robots
  • Student helper, Prof. Yang Jinglei@HKUST — 2021 Summer
    • Collaborating with the back-end group, in charge of the Vue-based front-end programing
    • Implementing the hazard warning function with pop-up windows
  • Hackathon, hackUST 2022 — 2022 Spring
    • Collaborating in a team for the development of a webapp called School Application Helper, helping students to manage school application requirements and timelines
    • Github repository; Video demo
  • Research assistant, Prof. Ki Ling CHEUNG@HKUST — 2022 Summer – 2022 Winter
    • MySQL database maintenance and data washing with Dask
    • Providing technical support for researchers from different technical backgrounds
  • Peer mentor, COMP & CPEG Mentor-Mentee Scheme — 2023 Fall
    • Providing mentoring to second-year students admitted to the computer science and engineering department
  • Exchange buddy, Exchange Buddy Program — 2023 Spring, 2023 Fall
    • Helping exchange students navigate the HKUST campus

Skills

  • C/C++ programming
  • Python programming
    • PyTorch(master), NumPy(master), Matplotlib(master), TensorFlow(intermediate), Django(intermediate), Scrapy(intermediate), Dask(intermediate), Pandas(elementary)
  • CAD
    • SolidWorks(master)
  • Documentation
    • Markdown(master), LaTeX(master)
  • Webapp Development
    • Vue.js(intermediate), React.js(elementary), MySQL(intermediate), Google Firebase(elementary)
  • Soft Skills
    • Collaboration and leadership, self-learning, self-motivation, time management