Research Experiences

Research Assistant(with Prof. Lee Cooper)

Sep 2023 - Present
Northwestern University
  • Utilize Multi-Instance learning techniques in digital pathology for prostate cancer molecular subtype classification, potentially improving accuracy and robustness of the model’s predictions.
  • Introduce supervised End-to-end model for the tile-level features to improve the performance of the MIL model in the PAM50 dataset.
  • Investigate the impact of pre-trained foundational models to the performance when combined with MIL for molecular subtype classification.
  • Incorporate multi-scale representations of digital path imaging data to enhance the model’s ability to predict molecular subtypes in PAM50 dataset.
  • Drafted a paper including an in-depth comparison of the proposed method with current state-of-the-art techniques, demonstrating its effectiveness through detailed ablation studies.
  • Implement attention gates and residual connections within the U-Net architecture to better capture intricate tumor boundaries and improve segmentation accuracy, especially for small or complex regions.
  • Implement weakly supervised methods for analyzing pathology images to predict two-year survival of patients diagnosed with brain tumors.

Research Assistant(with Prof. Bernard Martin)

Jun 2022 - Sep 2022
University of Michigan
  • Assisted in collecting data and determining performance within constrained movement environments.
  • Developed a Matlab program to process and analyze EMG signals and filter the noise of EMG data.
  • Designed and implemented a rehabilitation remote game for stroke patients with adaptable difficulty and real-time evaluation..
  • Designed and constructed low-cost low fidelity adjustable mock-ups for human subject studies.
  • Preliminary data presented at the CDHW Symposium in October 2022.

Research Assistant(with Prof. Nanning Zheng)

Jun 2021 - Jun 2022
Xi’an Jaotong University
  • Implemented Graph-based Clustering and k-means++ with a pre-generated anchor graph on Matlab.
  • Collected and interpreted data, compared against the baseline algorithm in different data sets, and applied regression testing.
  • Tested the effectiveness of L2 and L1 norms and verified that L21 norms outperformed other norms.
  • Implemented KNN and PCA algorithms in Python and designed the lab procedure.
  • Co-published reports on “Robust Landmark Graph-based Clustering for High-Dimensional Data”.

Publications

  • Towards Enhanced Subtyping in Prostate Cancer: A Pathology-Based AI Approach for PAM50 Classification
  • Sun, A., et al
    American Urological Association Annual Meeting, (submitted).
  • Investigating Unintended Biases due to Simulated Impairment within Inclusive Mobility Research and Design
  • Kamolnat Tabattanon, Aaron Sun, Bernard J., Martin
    Human Factors and Ergonomics Society, 2023.
  • Robust Landmark Graph-based Clustering for High Dimensional Data
  • Ben Yang, Jinghan Wu, Aoran Sun, Naying Gao, Xuetao Zhang
    Neurocomputing Journals, 2022.
  • Design of Virtual Guiding Tasks With Haptic Feedback for Assessing the Wrist Motor Function of Patients With Upper Motor Neuron Lesions
  • Xiaoyu Liu, Yuanjie Zhu, Hongqiang Huo, Pengxu Wei, Lizhen Wang, Aoran Sun, Chaoyi Hu, Xiaofei Yin, Zeping Lv, and Yubo Fan
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019.

    Skills

    Python, C++, Java, Matlab

    TensorFlow, PyTorch

    SolidWorks, 3D Max

    Photography

    Volleyball teaching