Biography

Hi Welcome 👋. I’m a Research Assistant in the FORTIS(ASAP) Lab at USC, mentored by Prof. Yue Zhao. Here, I focus on exploring the Trustworthy of AI(LLM) and ML Systems. Additionally, I maintain a long-term collaboration with Dr. Jiechao Gao, together dedicating our efforts to the research of Decentralized Machine Learning and LLM.

My research interests broadly fall within the areas of the Trustworthy AI (LLMs), ML Systems, and the AutoML, with a primary focus on developing efficient and robust AI systems and ensuring the trustworthiness of AI. My long-term vision is to enhance the effectiveness of AI systems and ensure the trustworthiness of the AI produced. Moreover, I am also interested in applying AI for science, health, and exploring emerging directions from foundation models and generative AI (GenAI+X), where “X” represents various tasks or domains. I am always open to collaborations and discussions, so feel free to schedule a meeting with me.

🌟Looking for a PhD position in Fall 2025.🌟

Recent Works:
  • Trustworthy AI
    • Trustworthy Model - Reducing Hallucinations in LLMs
    • Trustworthy Data
      • LLMs for Anomaly and OOD Detection
      • NLP Anomaly Detection Benchmark and Survey
  • MLSys(AutoML)
    • Better Inference - Model Generation Framework for Optimal Compressed LLMs
    • Better Training - Automated ML Systems (Auto Parameter Tuning, Auto Model Selection)
  • Decentralized Learning
    • Communication, Heterogeneity in (Hierarchical) Federated Learning
    • Federated Learning + Health
Interests
  • Trustworthy AI (LLMs)
  • MLSys & AutoML
  • GenAI + Science
  • Decentralized Learning
  • Cloud Computing & AIoT / Edge AI
Education
  • M.S. in Computer Science, 2024

    University of Southern California (3.83/4)

  • B.S. in Software Engineering, 2021

    Beijing City University (3.6/4, Top 1/150)

News

  • [Nov 2024] A paper is accepted to BIBM 2024!
  • [Oct 2024] A paper is accepted to SSTM @ ICDM 2024 - see publication for details!
  • [Sep 2024] A paper on AI + Digital Twin is published as a Preprint!
  • [Jul 2024] A paper is accepted to ACM MM 2024 - see publication for details!
  • [Jan 2024] I join UVA as a Research Assistant advised by Dr. Jiechao Gao and Prof. Brad Campbell!
  • [Sep 2023] I join USC’s FORTIS(ASAP) Lab as a Research Assistant advised by Prof. Yue Zhao!
  • [Mar 2023] I join USC’s DeepUSC Lab as a Research Assistant!
  • [Jan 2022] I join SenseTime(商汤科技) as a Infrastructure Engineer.
  • [Jan 2022] I join USC as a Master student in Spring 2022!
  • [May 2021] I join the ISCAS(Institute of Software, Chinese Academy of Sciences) as a Research Enginner.
  • [Dec 2020] My personal website has been built! ✨

Academia Experience

 
 
 
 
 
University of Southern California
Research Assistant
July 2023 – Present California

Advisor: Prof. Yue Zhao - NLP Anomaly Detection Toolkit and Benchmarking Development | LLM, AutoML, NLP, Anomaly Detection, PyTorch

  • Created a robust benchmarking system for evaluating algorithmic efficacy with over 40 PyOD library O.D. algorithms and 3 end2end algorithms across 20 datasets to facilitate comprehensive performance analysis and comparison.
  • Redesigned NLP datasets into a unified format for anomaly detection, addressing a field gap, and developed a system to automatically recommend optimal AD algorithms based on benchmarks.
  • Co-first authored a systematic review on AI-aided digital twin design, analyzing how machine learning enhances digital twins and their applications across multiple domains.
  • Constructed causal reasoning datasets, fine-tuned LLMs, and evaluated using hallucination benchmarks.
  • Investigated generative AI techniques to tackle Anomaly and Out-of-Distribution Detection problems and explored the capabilities of LLMs in politics.
 
 
 
 
 
University of Virginia
Researche Collaboration
University of Virginia
September 2024 – Present Remote

Advisor: Dr. Yue Cheng - Effective Machine Learning Systems Development | LLM Inference, LLM compression

  • Researched an automated method to generate the optimal LLM inference compression algorithm for user-specific tasks.
 
 
 
 
 
University of Virginia
Independent Researcher
University of Virginia
July 2024 – September 2024 Remote

Advisor: Dr. Jiechao Gao - Federated Learning System Development | Federated Learning, PyTorch, Python, Spatio-temporal data

  • Developed the FedMetaMed, integrating federated learning and meta-learning to enhance personalized medication strategies across distributed healthcare systems, improving model adaptability and privacy preservation.
  • Single-handedly developed FedLDR, a brand new federated learning algorithm that employs GCN to enhance spatio-temporal data analysis through local data integration and node-centric optimization. = FedMetaMed and FedLDR[were accepted by BIBM and ICDM respectively, and I gave talks on these research.
 
 
 
 
 
University of Virginia
Independent Researcher
University of Virginia
January 2024 – July 2024 Remote

Advisor: Dr. Jiechao Gao, Prof. Brad Campbell - Hierarchical Federated Learning System Development | Hierarchical Federated Learning, PyTorch, Python, Sparse Network

  • Independently developed H-FedSN pushes the boundaries of IoT with a unique approach that uses masking techniques to train a sparse network, enhancing personalization through client-based transfer learning. Applied to non-IID IoT datasets, it achieves high accuracy and boosts communication efficiency by at least 58x.
  • Solely developed and integrated innovative federated learning algorithms—FedAvg, FedCAMS, FedPer, PerFedAvg, and FedRS—into a hierarchical framework to optimally benchmark against H-FedSN.
 
 
 
 
 
University of Southern California
Research Assistant / Engineer
March 2023 – July 2023 California

Advisor: Dr. Iordanis Fostiropoulos - Distributed ML Execution Framework Development | MLSys, AutoML, Ray, Docker, Github Action, PyTorch, Pytest

  • Contributed to “Ablator”, an open-source Deep Learning framework used by 40+ USC researchers for horizontal scaling of ablation experiments and hyperparameter tuning, encompassing 70 pull requests.
  • Implemented distributed experiment execution with Ray, managed open-source projects, set up CI pipelines via GitHub Actions, oversaw release management and version control, and authored pytest unit tests with 97% coverage.
  • Solely launched ‘python-rclone’ on PyPI, a Python API for RClone that streamlines cloud data synchronization for ‘Ablator’, removing pre-installation requirements and enabling automatic binary selection (python-rclone).
 
 
 
 
 
Institute of Software, Chinese Academy of Sciences
Research Assistant / Engineer
Institute of Software, Chinese Academy of Sciences
May 2021 – October 2021 Beijing

Advisor: Prof. Guoquan Wu - Automated Testing Platform Development | Docker, Node.js, JSON, Vue.js, RobotFramework

  • Contributed to the R&D of a web-based automated testing tool using Record and Playback technology, significantly enhancing test case management by enabling streamlined recording, editing, execution, analysis, and result generation. This implementation boosted end-to-end testing efficiency by 300% and saved over 15 hours per week.
  • Independently developed a script parser using Node.js that converts user actions recorded in JSON format into executable Robot Framework and Selenium scripts, enabling the replay and repeated execution of these user actions.
  • Single-handedly created innovative UI components using Vue.js and AceEditor, orchestrated the optimal containerization of the program with Docker, and automated the DevOps pipeline to maximize development efficiency.

Industry Experience

 
 
 
 
 
SenseTime
Infrastructure Engineer & Researcher
SenseTime
January 2022 – January 2023 Bejing

SaaS Platform Development {Demo} | Kubernetes, Docker, Go, CRD, Operator-SDK, Helm3, Prometheus, Grafana

  • Developed “RocketMQ as a Service”, akin to “RabbitMQ as a Service” in AWS Marketplace, offering fully managed SaaS-based RocketMQ clusters, increasing 100% creation speed and saving 10+ hours/week in manual operations.
  • Utilized Operator SDK to build a Kubernetes-based RocketMQ Operator and CRD automating lifecycle management.
  • Employed Helm3 to package RocketMQ’s components into Helm charts, simplifying Kubernetes deployment.
  • Implemented Prometheus and Grafana for real-time monitoring of critical service metrics and node health.
  • Automated workflows, including unit tests, image builds, and Helm3 Chart updates, via GitLab CI/CD.
  • Researched and evaluated container runtimes (sysbox, crun, youki) for suitability as replacements for Docker in the SaaS platform, ensuring CRI-O compliance and robust community support.
  • Optimized a machine learning training pipeline using GPUs on Kubernetes for enhanced computational efficiency.
 
 
 
 
 
Full Stack Engineer
Xiaoniu Translations (Beijing) Technology Co., Ltd.
January 2021 – April 2021 Beijing

Text Translation Platform Development | Java, SpringBoot, Spring, Java Persistence API, Maven, Nginx, MySQL, Git

  • Developed an AI document translation system with Java/Spring/Maven, independently created a PDF/XML parsing module attracting 30,000 MAUs, used Nginx for reverse proxy, and managed version control with Git.

Projects

Apache/RocketMQ Operator

Apache/RocketMQ Operator

RocketMQ Operator is to manage RocketMQ service instances deployed on the Kubernetes cluster. It is built using the Operator SDK, which is part of the Operator Framework.

PyRCLONE

PyRCLONE

A Python wrapper for rclone, available on PyPI, conveniently includes the rclone binary (version v1.62.2) eliminating the need for pre-installation of rclone. It caters to various operating systems like Windows, Mac, and Linux, and supports both amd64 and x86_64 architectures. When a user downloads the package, the appropriate rclone binary file is installed based on their system type.

SLinux OS

SLinux OS

This project is a simple operating system implementation, designed to provide a hands-on learning experience for understanding the fundamentals of operating system development. It includes components such as a bootloader, kernel, and basic system utilities.

USC/ABLATOR

USC/ABLATOR

ABLATOR is a DISTRIBUTED EXECUTION FRAMEWORK designed to enhance ablation studies in complex machine learning models. It automates the process of configuration and conducts multiple experiments in parallel.

USC/Hexagon Adventure (2D Game)

USC/Hexagon Adventure (2D Game)

Hexagon Adventure is a 2D game developed using Unity3D. The game is designed to be a fun and challenging experience for players of all ages.

USC/EventMaster

USC/EventMaster

Event search platform

Transformer from Scratch

Transformer from Scratch

This project implements a Transformer model from scratch for a machine translation task. The goal is to build a functional Transformer model starting from the basic principles and gradually developing it into a full-fledged model capable of translating text between languages.