CV

Education

• Ph.D in Computer Science, Florida International University, 2027 (expected)
• M.S. in Information Technology and Management, The University of Texas at Dallas, 2018
• B.A. in Tourism Management, Nanjing Normal University, 2016

Research Experience

• Efficient Deployment and Inferencing of Large Language Models
  • Research on optimizing both the inference processes and scalability of Large Language Models (LLMs) by leveraging strategies like cloud-edge collaboration, distributed AI, and ML efficiency techniques (e.g., early exit, quantization).
  • Mentor: Dr. Yanzhao Wu
  • Contributions:
    • CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration
      • arXiv:2411.02829, under review at MLSys 2025
        Proposed the CE-CoLLM method to optimize the inference efficiency and accuracy of LLMs on edge devices through cloud-edge collaboration and ML efficiency techniques, addressing diverse requirements such as inference accuracy, low latency, resource constraints, and privacy preservation.
    • DA-MoE: Dynamic Expert Allocation for Mixture-of-Experts Models
      • arXiv:2409.06669, 2024
        Proposed the DA-MoE method, a dynamic expert allocation mechanism for Mixture-of-Experts (MoE) models that leverages attention-based token importance in Transformer architectures to dynamically adjust the number of experts per token, enhancing efficiency and predictive performance.
• Advanced Training Strategies and Ensemble Learning for Model Performance
  • Research on enhancing training efficiency, performance, and robustness of deep neural networks (DNNs) and large language models (LLMs).
  • Mentor: Dr. Yanzhao Wu
  • Contributions:
    • Efficient and Learning Rate Boosted Deep Ensembles
      • under review at CVPR 2025
        Proposed the LREnsemble framework, effectively utilizing diverse models, generated through learning rate (LR) tuning, to construct efficient and high-quality ensembles, avoiding the waste of sub-optimal LR-tuned models by leveraging their diversity for ensemble learning.
    • Effective Diversity Optimizations for Deep Ensembles
      • CogMI 2024
        Proposed the Synergistic Diversity metric, significantly improving ensemble accuracy and robustness to out-of-distribution samples by optimizing diversity among member models.
    • Rethinking Learning Rate Tuning in Large Language Models
      • CogMI 2023
        Introduced the LRBench++, a dynamic learning rate tuning framework, improving DNNs and LLMs training efficiency and achieving a balance between model accuracy and training cost.

Work Experience

• Cintra US
  Data Scientist, May 2022 – Aug. 2023, Austin, TX
  • Built machine learning models to improve the work efficiency of business and operation teams, including:
    1. Dynamic Pricing: Developed LightGBM (quantile) models to predict future demand and its confidence interval, enabling the identification of demand anomalies.
    2. Incident Detection: Created a prediction system using real-time vehicle status data, incident history reports, and highway pavement data.
    3. Analytics AI: Built predictive models and explainability tools for business decision-making.
  • Conducted statistical analyses (A/B tests) to quantify driver behaviors and preferences, such as peak-hour behavior and lane-changing patterns, while measuring the impact of external interventions (e.g., large events, extreme weather).
• HP Inc.
  Marketing Survey Data Analyst, Apr. 2020 – May 2022, Vancouver, WA
  • Modeled large-scale email survey data to analyze the impact of customer journey experiences on Net Promoter Scores (NPS) and provide actionable insights.
  • Prioritized customer review records for response team efficiency using Supervised LDA topic modeling and statistical learning models.
  • Assisted UX teams with power analysis, A/B testing, and general linear regression methods to optimize email survey titles and UI.

• Samsung Electronics America
  QA Engineer, Mar. 2019 – Mar. 2020, Plano, TX

• ZTE USA Inc.
  Software Test Engineer (Automation Testing), Apr. 2018 – Mar. 2019, Richardson, TX
  • Developed and implemented the “AIO” automation testing project, transitioning from manual to automated testing to enhance efficiency and quality.

Awards

• IEEE TPS 2023 NSF Travel Award, November 2023

Service and Leadership

• Reviewer: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024
• Reviewer: Asian Conference on Machine Learning (ACML), 2024
• External Reviewer: The Web Conference (WWW), 2024, 2025
• External Reviewer: Association for the Advancement of Artificial Intelligence (AAAI), 2024
• External Reviewer: International Joint Conference on Artificial Intelligence (IJCAI), 2024
• External Reviewer: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
• External Reviewer: SIAM International Conference on Data Mining (SDM), 2024