About me

My name is Fanjie Kong, a fourth-year Ph.D. candidate in the Electrical and Computer Engineering(ECE) Department at Duke University, working with Prof. Ricardo Henao. My current research mainly focuses on open-vocabulary object detection, efficient vision models and Fairness in AI. For additional details regarding my academic and professional experiences, please refer to my CV.

With an anticipated graduation in 2024, I am actively seeking opportunities in industry roles, such as scientist positions.

Recent Papers

1. Hyperbolic Learning with Synthetic Captions for Open-World Detection [PDF]
   Fanjie Kong, Yanbei Chen, Jiarui Cai, Davide Modolo
   IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
2. Mitigating Test-Time Bias for Fair Image Retrieval [PDF]
   Fanjie Kong, Shuai Yuan, Weituo Hao, Ricardo Henao
   Advances in Neural Information Processing Systems (NeurIPS), 2023
3. Neural Insights for Digital Marketing Content Design [PDF]
   Fanjie Kong, Yuan Li, Houssam Nassif, Tanner Fiez, Ricardo Henao, Shreya Chakrabarti
   ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2023.
4. Efficient Classification of Very Large Images with Tiny Objects [PDF]
   Fanjie Kong, Ricardo Henao
   IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
5. Physics-enhanced machine learning for virtual fluorescence microscopy [PDF]
   Colin L Cooke, Fanjie Kong , Amey Chaware, Kevin C Zhou, Kanghyun Kim, Rong Xu, D Michael Ando, Samuel J Yang, Pavan Chandra Konda, Roarke Horstmeyer
   IEEE/CVF International Conference on Computer Vision (ICCV), 2021.
6. The Synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation [PDF]
   Fanjie Kong, Bohao Huang, Kyle Bradbury, Jordan Malof
   IEEE/CVF winter conference on applications of computer vision (WACV), 2020.

Industrial Experience

• Applied Scientist Intern at Amazon AWS AI Lab
• Applied Scientist Intern at Amazon.com

Teaching Assistant

• ECE 586 - Vector Space Methods with Applications, for graduate students, Fall 2021.
• ECE 684 - Natural Language Processing, for graduate students, Fall 2022.

Videos

KDD 2023 - Neural Insights for Digital Marketing Content Design

NeurIPS 2023 - Mitigating Test-Time Bias for Fair Image Retrieval

Reviewer Service

• EMNLP 2023, ICLR 2024, CVPR 2024
• TPAMI