Wen (Wendy) Ye

Education

Publication

• Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution Wen Ye*, Yizhou Zhang*, Wei Yang*, Defu Cao, Lumingyuan Tang, Yan Liu.
  ArXiv Preprint
• GeoAI-enhanced community detection on spatial networks with graph deep learning Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao
  Computers, Environment and Urban Systems
• Timedit: General-purpose diffusion transformers for time series foundation model Defu Cao*,Wen Ye*, Yizhou Zhang, Yan Liu.
  ArXiv Preprint
  ICML 2024 Foundation Model in the Wild Workshop
• TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu.
  The Twelfth International Conference on Learning Representations (ICLR 2024)
• Region2vec: Community detection on spatial networks using graph embedding with node attributes and spatial interactions. Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao.
  Proceedings of the 30th International Conference on Advances in Geographic Information Systems
• Understanding the spatiotemporal heterogeneities in the associations between COVID-19 infections and both human mobility and close contacts in the United States Wen Ye, Song Gao.
  Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Spatial Computing for Epidemiology

Awards

• USC Annenberg Fellowship 2023
• Student Research Paper Competition (SRC) Second Place at ACM SIGSPATIAL 2022
• Best Paper Award from ACM SIGSPATIAL Spatial Epidemiology Workshop 2022
• Hilldale Undergraduate/Faculty Research Fellowship (2022-2023): Understanding spatial inequality to healthcare access in Wisconsin through deep learning-based network analysis
• MinneAnalytics Scholarship 2021

Teaching

• Teaching Assisstant for USC CSCI 566 Deep Learning and Its Applications, Fall 2024.
• Undergraduate Teaching Assistant fr UW-Madison CS 320 Data Science Programming II Fall 2020, Spring 2021.

Work Experiences

• Modified the Non-Maximum Suppression Algorithm to update boundaries of regional tiles to prevent overlap which makes the downstream task of scoring trips within regions easier
• Investigated the data quality issue regarding trip duplication and created a scrip to flag trips that are duplicate by trip id (duplicate trips by collection) or by geolocation-trail and timestamp (semantically duplicate trips)

• Set up a data pipeline and built enriched trip dataset with urban/rural features using Sedona and Spark
• Designed and implemented an algorithm to detect user relocation by examining their trips, determining change in dwelling locations, and peaks in their traveling distances