CV (Dec 2023)

Education

• Ph.D. in Electrical and Computer Engineering, Mississippi State University, 2018 - 2023
  • Advisor: Prof. Bo Tang
• M.S. in Computer Engineering, Stevens Institute of Technology, 2015 - 2017
• B.S. in Electrical Engineering, Xiamen University, 2011 - 2015
  • Bachelor Study Abroad Program, National Tsing Hua University, Jan - May 2013

Experience

• Postdoctoral Fellow, Tulane University, 2023 - Present
  • Supervisor: Prof. Lu Peng
  • Led an AI research project on Trustworthy & Robust AI in Federated Learning, focusing on enhancing patient privacy and tackling medical data heterogeneity in brain science research with applications on FMRI data. Developed innovative federated learning algorithms, resulting in substantial improvements in data privacy and performance increase.
  • Contributed to NIH and NSF grant writing and proposal applications, employing advanced AI/ML methods like autoencoder and transformer for time-series data analysis in unsupervised and self-supervised flash drought prediction and precipitation anomaly detection, enhancing the accuracy and reliability of meteorological forecasts.

• Machine Learning Research - Intern, Bosch AI, Mar 2023 - Aug 2022
  • Supervisor: Zhenzhen Li
  • Formulated pFairness, a pioneering fairness metric for evaluating industrial-scale Federated Learning applications, coupled with a framework that integrates personalized, groupwise, and shared models, successfully validated on Cifar10 and Tiny-ImageNet datasets, resulting in substantial improvements with a 20% performance increase.
  • Proposed the FedTPG algorithm, a federated approach for generating context-aware prompt vectors from text inputs, significantly enhancing the generalization capability of Large Language-Vision Models like CLIP, especially in diverse data classification tasks under the distributed non-iid Language-Vision tasks, accepted as FL@FM-NeurIPS’23 Oral.
  • Mentored incoming summer interns, guiding them through project onboarding, familiarization with the code base, and research project initiation, ensuring a smooth and effective integration into the team.

• Machine Learning Engineering - Intern, Devron, Aug 2022 - Dec 2022
  • Supervisor: Sidhartha Roy
  • Developed a cross-silo federated learning framework for financial fraud detection, utilizing Scikit-learn, PyTorch, and MLflow to establish a robust and scalable system, enhancing fraud detection in financial transactions.
  • Led a COVID-19 data engineering project, leveraging Pandas, SQL, and Python to tackle privacy protection challenges, implementing Privacy Enhancing Technologies (PETs) for secure and ethical data handling.
• Graduate Research Assistant, Mississippi State University, 2018 - 2023
  • Supervisor: Prof. Bo Tang
  • Dissertation title: Secure and Efficient Federated Learning.
  • Innovated the AdaER algorithm under Prof. Tang’s NSF CAREER Award, bolstering continual learning by enabling effective knowledge accumulation over time. Demonstrated superior performance in class-IL tasks on Cifar-100 and TinyImageNet image classification tasks.
  • Provide a distributed machine learning framework with multiple proposed federated learning algorithms on security, multi-server, and generalization. Cooperated with Zhe Qu from the University of South Florida and Prof. Jie Xu from the University of Miami.

Professional Service

• Program Committee
  • The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  • International Conference on Machine Learning (ICML), 2022
  • AAAI Conference On Artificial Intelligence (AAAI), 2022, 2023
  • IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023, 2024
• Journal Reviewer
  • IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
  • IEEE Transactions on Cybernetics (TCYB)
• Teaching
  • Advisor for K12 interns at the CILS Lab
• Awards
  • Travel Grant for ICML 2022

Skills

• Programming Languages and Frameworks
  • Python, Pytorch, Tensorflow, Sklearn, Pandas, Numpy, Matplotlib, MlFlow, Docker
• Developer Tools
  • Git, Unix, Shell, Vscode, Anaconda, Jupyter, AWS
• Technical
  • Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Large Language/Vision Model, Generative AI, Domain Generalization, Federated Learning, Computer Vision, Retrieval-Augmented Generation, Software Engineering, Trustworthy/Privacy
• Miscellaneous
  • Research Leadership, Scrum, Code Review, Mentoring