About Me

I am a Ph.D. student in the Department of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology, advised by Prof. Hossein Azizpour, Prof. Andrea Nascetti, and Prof. Josephine Sullivan.
My research interests lie in the general area of machine learning, particularly in deep learning, temporal modeling and uncertainty estimation, as well as their applications in satellite imagery and biomedical scenarios.
I also worked closely with Prof. Takeo Igarashi, M.D. Taichi Kin and Prof. Xi Yang at the University of Tokyo on the project of 3D reconstruction and medical image processing.
I received my Bachelor’s Degree in Chien-Shiung Wu College, Southeast University. My undergraduate research supervisor is Prof. Chuan Zhang.

Publications

Qingwen Zhang, Yi Yang, Heng Fang, Ruoyu Geng, Patric Jensfelt, “DeFlow: Decoder of Scene Flow Network in Autonomous Driving”, International Conference on Robotics and Automation (ICRA) 2024, May 13-17, 2024. [paper]
Beril Sirmaçek, S Gupta, Fermín Mallor, Hossein Azizpour, Yifang Ban, Hamidreza Eivazi, Heng Fang, Farzin Golzar, Iolanda Leite, Gaspar Isaac Melsión, Kevin Smith, F Fuso Nerini, Ricardo Vinuesa, “The potential of artificial intelligence for achieving healthy and sustainable societies”, The ethics of artificial intelligence for the sustainable development goals, Springer, 2023. [paper]
Heng Fang, Xi Yang, Taichi Kin, Takeo Igarashi, “Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps”, IEEE International Symposium on Biomedical Imaging (ISBI) 2021, April 13-16, 2021. [paper]

Conference & Journal Reviewer: Transactions on Geoscience & Remote Sensing (TGRS), ISBI 2021-2024, Neurips workshop ICBINB 2021-2022, ML Reproducibility Challenge 2020-2021, NeurIPS 2023 Workshop DGM4H.
Organizer: KTH Machine Learning Seminars, KTH RPL Summer School 2022
Volunteer: AISTATS 2021, ICML 2022

Deepwise (November 2020 - January 2021)
- Machine Learning Engineering Intern, AI Lab
- Contributed to domain adaptation algorithm to address challenges in medical imaging.
- Collaboratively worked on a project aiming to predict pediatric pneumonia using a novel approach that involved training models on adult X-Rays (with labels) and applying them to children’s X-Rays (without labels), enhancing our understanding of cross-age diagnostic techniques.
Alibaba Group (June 2019 - August 2019)
- Data Analyst Intern, Apsara Stack, Alibaba Cloud
- Engaged in the management, visualization, integration, and analysis of data, with a focus on developing predictive models to enhance decision-making processes.
- Assisted in monitoring demand and resource utilization, contributing insights for optimizing cloud infrastructure efficiency.