Résumé/CV

PDF:   [Résumé]

Education

• Ph.D in Computer Science, University of Illinois, Urbana-Champaign, 2027 (expected)
• M.S. in Computer Science, University of Illinois, Urbana-Champaign, 2022
• B.S. in Computer Science, University of Illinois, Urbana-Champaign, 2020
• B.S. in Applied Mathematics, University of Illinois, Urbana-Champaign, 2020

Experience

• Summer 2024 & 2025: Applied Scientist Intern - Amazon
  • Designed a multi-modal item representation learning framework that integrates item-item relationships and diverse content modalities, achieving up to a 39.2% improvement over current methods across five real-world datasets, with strong effectiveness in cold-start and sparse data scenarios.
  • Implemented an invariant item representation learning framework to address spurious multi-modal item information in sequential recommendation, demonstrating up to a 10.5% improvement over existing methods on multiple real-world datasets, particularly excelling in cold-start scenarios. <!– * Designed a multi-modal item representation learning framework that incorporates item-item relationships and diverse content modalities to enhance substitute and complementary item recommendations.
  • Demonstrated up to a 39.2% improvement over current methods across five real-world datasets, highlighting its effectiveness, particularly in cold-start and sparse data scenarios. –>
• Summer 2022: Applied Scientist Intern - Amazon
  • Created a cross-domain representation learning framework for entity-based personalization, enabling a unified user representation across multiple catalog domains.
  • Delivered over a 5% improvement compared to the existing system, validated through both internal and external datasets for recommendation applications.