Yifan Zhang

Yifan Zhang

Ph.D. Student at Vanderbilt University

Vanderbilt University


Yifan Zhang (中文: 张一凡, 日本語: チャン・イーファン, Русский: Ифань Чжан) is a Ph.D. student in Computer Science at Vanderbilt University (Vandy), under the supervision of Prof. Kevin J. Leach and Prof. Yu Huang at the Institute for Software Integrated Systems (ISIS). He is also jointly pursuing the Online Master of Science in Computer Science (OMSCS) at Georgia Institute of Technology (GaTech). Before joining Vanderbilt & GaTech, he earned his B.A., B.Eng., and M.Eng. from China University of Petroleum (CUP) at Beijing and worked as a machine learning engineer at the Intelligent Risk Management Lab at JD.COM. He has also worked as a research associate at the University of Hong Kong (HKU) and Duke University (Duke). His research interests lie in AI for programming languages (AI4Code), code representation learning, cognitive process in software engineering, binary code analysis, and medical imaging.

  • AI for Programming Languages (AI4Code)
  • Code Representation Learning
  • Cognitive Process in Software Engineering
  • Binary Code analysis
  • Medical Imaging
  • Vanderbilt University, 2022-2026

    Ph.D. in Computer Science, AI for Software Engineering

  • Georgia Institute of Technology, 2022-2025

    M.Sc. in Computer Science, Machine Learning

  • China University of Petroleum, 2012-2019

    B.A., B.Eng., M.Eng. in English & Petroleum Engineering

  • University of Calgary, 2015-2016

    Undergraduate Exchange Program in Petroleum Engineering



  • [04/23/2024] On paper got accepted to TOSEM with IF of 4.978.

  • [04/09/2024] Invited as a PC member for CCS'24 AE.

  • [03/15/2024] Selected as a top reviewer for DMLR@ICLR'24 with TR of 9.41%.

  • [01/25/2024] Invited as a PC member for MICCAI'24.

  • [01/23/2024] One paper got accepted to WWW'24 with AR of 20.2%.

  • [01/22/2024] One paper got accepted to FSE'24 with direct AR of 11.59%.

  • [01/11/2024] Invited as a PC member for DMLR@ICLR'24.

  • [01/04/2024] One paper got accepted to ICPC‘24 RENE.


  • [12/19/2023] Passed my Ph.D. Preliminary Examination.

  • [09/05/2023] One paper got accepted to TMI with IF of 11.037.

  • [07/13/2023] Invited as a PC member for AAAI'24.

  • [07/07/2023] One paper got accepted to ASE'23 NIER.

  • [03/19/2023] Invited as a PC member for MLSys'23 AE.

  • [03/02/2023] One paper got accepted to InteNSE@ICSE'23.

  • [03/02/2023] Accepted a RS intern position at ByteDance in San Jose, CA, USA.

  • [02/06/2023] Invited as a PC member for MICCAI'23.

  • [01/06/2023] Invited as a PC member for DCAA@AAAI'23.


  • [12/04/2022] Invited as a Junior PC member for MSR'23.

  • [11/28/2022] Attended NeurIPS'22 physically in New Orleans, LA, USA.

  • [10/10/2022] Attended ASE'22 physically in Detroit, MI, USA and presented one paper.

  • [09/22/2022] Attended MICCAI'22 virtually in Singapore and presented one paper.

  • [08/05/2022] One paper got accepted to ASE'22 DS.

  • [08/01/2022] Invited as a PC member for AAAI'23.

  • [07/18/2022] One paper got accepted to CaPTion@MICCAI'22.

  • [07/02/2022] Invited as a PC member for CaPTion@MICCAI'22.

  • [06/01/2022] Started my Ph.D. in Computer Science at Vanderbilt University.



Yifan Zhang, Haoyu Dong, Nicholas Konz, Hanxue Gu, Maciej Mazurowski: REPLICA: Enhanced Feature Pyramid Network by Local Image Translation and Conjunct Attention for High-Resolution Breast Tumor Detection. Preprint.

Submitted & Working Papers

Yichang He, Yifan Zhang, Yunpeng Fan, U-Xuan Tan,: Real-time Vibration Compensation with Long Short-term Memory Recurrent Neural Network and Adaptive Filter, Under Review by IEEE-TMech.

Yifan Zhang, Chen Huang, Yueke Zhang, Kevin Cao, Scott Thomas Anderson, Huajie (Jay) Shao, Kevin Leach, Yu Huang: Pre-Training Representations of Binary Code Using Contrastive Learning. Under Review.

Yifan Zhang, Junwen Yang, Haoyu Dong, Qingchen Wang, Huajie (Jay) Shao, Kevin Leach, Yu Huang: ASTRO: An AST-Assisted Approach for Generalizable Neural Clone Detection. Accepted by ICSEW 2023.

Haoyu Dong, Yifan Zhang, Hanxue Gu, Nicholas Konz, Maciej Mazurowski: SWSSL: Sliding-Window based Self-Supervised Learning Framework for Anomaly Detection. IEEE-TMI.

Haoyu Dong, Yifan Zhang, Nicholas Konz, Hanxue Gu, Maciej Mazurowski: Localized Semi-Supervised Anomaly Detection for Breast Tomosynthesis Lesion Screening. Under Review by CMPB.

Chen Huang, Yifan Zhang, Kevin Leach, Yu Huang, Wenqiang Lei, Jiancheng Lv: Cross-Domain Filters for Graph Convolutional Networks by Multiple Kernel Learning. Under Review by AAAI 2024.

Journal & Conference Papers

(2022). Lightweight Transformer Backbone for Medical Object Detection. In MICCAIW 2022.

PDF Cite Poster Slides Video

(2017). Thickness and Stability of Water Film Confined Inside Nanoslits and Nanocapillaries of Shale and Clay. IJCG 2017.

PDF Cite


Research Scientist Intern
May 2023 – Aug 2023 San Jose, CA, USA

Infrastructure Systems Lab

  • Enhancing the SQL pipeline using a hint recommendation system based on representation learning. This is an ongoing project by the time of completing this item.
Duke University
Research Associate
Jul 2021 – Jun 2022 Durham, NC, USA

Duke University Health System

  • Designed a feature interpolation and reconstruction pipeline for injecting tumors into healthy images as an augmented dataset, and conjuncted a ViT on the outputs of a ResNet as inputs to a FPN in Faster R-CNN for tumor detection. The model mitigates the data-hungry problem of attention and achieves 13.1% improvement in AP50 for detecting tumors (accepted by MICCAI workshop).
  • Introduced a method for detecting anomalies in high-resolution medical images by sliding patches, and a domain generalization method by imposing constraints on the feature space and its projection space. Both of the two model achieve state-of-the-art in anomaly detection and domain generalization accuracy (submiteed to MIA and IEEE-TMI).
The University of Hong Kong
Senior Research Assistant
Mar 2021 – Sep 2021 Hong Kong SAR

HKU Business School

  • Built an entire intelligent debt collection system using data-driven deep reinforcement learning models. The model utilizes transformer as the feature extractor and attaches a offline policy gradient model trained on the embedded sequential-aware hidden features to propose long-term dependent decisions.
  • Explored the application of GNNs on Finance and converted some of the ideas to a paper for addressing code clone detection (accepted by ICSE workshop).
Machine Learning Engineer
Dec 2018 – Mar 2021 Beijing, CN

Intelligent Risk Management Lab, JD Technology

  • Built Bi-GRU and DeepFM models on user behavior features to predict the credit use rate and overall profit of every user in cash loan and consumer debt. The model can propose decisions to increase their credit limit for maximizing income, and achieved 21.4% overall profit increase.
  • Built a heterogeneous graph on different types of user connections, and applied GNN models to propagate the credit score. The model can improve the overall accuracy of the XGB model by 5% in user classification.
  • Invented one kind of GAN-styled model using differential privacy to improve the efficiency and security of federated learning. Applied for 10 CN patents based on the research outputs, and was listed as 1st or 2nd inventor in 8 of them. One of the patents was awarded as 1st Runner-up in the 3rd JD Discovery Cup Patent Competition (Top 0.1%).
  • Authored Chapter 5 of Federated Learning: Technologies and Practices.
Data Science Mentor
May 2018 – Apr 2019 Shanghai, CN

Institute for Data Science

  • Taught VIP students Python, statistics, machine learning and data mining
  • Organized online community activities and created instructional materials
  • Awarded Oustanding Mentor of Udacity China (Top 1%)
University of Calgary
Undergraduate Exchange Student
Dec 2015 – May 2016 Calgary, AB, CA

Schulich School of Engineering

  • Fullly funded by Chinese National Fellowship for overseas research
  • Worked on an undergraduate thesis project in the Reservoir Simulation Group with Professor Keliu Wu.
  • Collaborated on one paper accepted by International Journal of Coal Geology (IF: 6.806).

Funds & Awards

First Runner-up in the 3rd JD.COM Patent Competition
Employee award for distinguished profit-making technical innovation at JD.COM (Top %0.1 company-wide)
Silver Medal Award of Distinguished JD.COM Technical Recruiter
Employee award for distinguished interviewers at JD.COM (Top %5 company-wide)
Bronze Medal Award of Certified JD.COM Technical Instructor
Employee award for certified JD.COM technical instructor
Beijing Outstanding Graduate Award
Student award for outstanding graduates at Beijing (Top 0.1% nationwide)
Roberto Roca Education Fellowship
Fellowship for outstanding graduate students in China (Top 10 nationwide)
Chinese National Fellowship
Fellowship for undergraduate students in overseas study (CA$6000/4 months)
Dean’s List for the Best Undergraduate Students
Student award for the best undergraduates (Top 4 university-wide)
Schlumberger Engineering Fellowship
Fellowship for outstanding undergraduate students (Top 8 university-wide)
Chinese National Scholarship
Scholarship for top undergraduate students in China (Top 0.2% nationwide)


Academic Mentorship

  • 2024 Manish Acharya, undergraduate student in CS & Math at Vanderbilt University
  • 2023 Jieyu Li, bachelor in EEE at Shanghai Jiaotong University, master in ECE at Vanderbilt University
  • 2023 Luka Mushkudiani, undergraduate student in CS & Math at Vanderbilt University
  • 2022 Eric Li, undergraduate student in CS & Math at Vanderbilt University

Program Comittee (PC) Member

  • 2024 ACM Conference on Computer and Communications Security (CCS'24) Artifact Evaluation Track
  • 2024 Data-centric Machine Learning Research Workshop at ICLR (DMLR@ICLR'24)
  • 2024 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'24)
  • 2024 AAAI Conference on Artificial Intelligence (AAAI'24)
  • 2023 Conference on Machine Learning and Systems (MLSys'23) Artifact Evaluation Track
  • 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'23)
  • 2023 AAAI Workshop on DL-Hardware Co-Design for AI Acceleration (DCAA@AAAI'23)
  • 2023 International Conference on Mining Software Repositories (MSR'23)
  • 2023 AAAI Conference on Artificial Intelligence (AAAI'23)
  • 2022 MICCAI Workshop on Cancer Prevention through early detecTion (CaPTion@MICCAI'22)

External Conference Reviewer

  • 2024 International Conference on Very Large Data Bases (VLDB'24)
  • 2023 USENIX Security Symposium (USENIX'23)
  • 2022 Computer Vision and Pattern Recognition Conference (CVPR'22)