#  Jeremy Wei 

Program Instructor, DCE

Adjunct Professor, USTC

Founder, iDox.ai

 

 

 



   ![Jeremy Wei](/sites/g/files/omnuum8171/files/styles/hwp_4_5__320x400/public/harvard-iqss/files/jeremy-wei_2025.png?itok=lzUU2LRR) 

 



 

 email [jeremy\_wei@fas.harvard.edu](mailto:jeremy_wei@fas.harvard.edu) 

 laptop\_windows [LinkedIn](https://www.linkedin.com/in/jeremywei/) 

 

 



 

 Jeremy Wei has extensive experience in research methodologies, ethics, and data analysis, having facilitated various research projects at New York University and UTSC. His research employs advanced statistical techniques and machine learning to examine complex relationships among variables. His current study explores the connection between AI and data privacy, the resistance to adopting AI, and the impact of reflective practices on users' intentions to adopt AI technologies. He assists organizations in transforming disadvantages into strengths. Additionally, he is the founder of iDox.ai, a leading AI-driven data privacy management organization.

 Appointments:  
Program Instructor, DCE, Harvard  
Adjunct Professor, USTC

 Education:  
EdD, New York University: New York, New York  
MS, Columbia University: New York, NY, US| (Institute of Technology Management)  
MA, NCCU: Taipei, Taiwan  
BA, Catholic FuJen University, Taipei, Taiwan

 Publications:  
Navigating AI Adoption from Resistance to Acceptance: How Collective Reflexivity Emerges to Catalyze Software Engineers' Perspective Shift. Available at SSRN 4790326.  
2024 |w-paper

 Reflective learning: a new leadership development framework driving engineering innovation  
Reflective Practice.  
DOI: 10.1080/14623943.2024.2320141

 RCAM-Transformer: A Novel Approach to Table Reconstruction Using Row-Column Attention Mechanism2024 | book-chapter  
DOI: 10.1007/978-3-031-70442-0\_7

 MuraNet: Multi-task Floor Plan Recognition with Relation Attention  
Document Analysis and Recognition – ICDAR 2023 Workshops  
2023 | conference-paper  
DOI: 10.1007/978-3-031-41498-5\_10

 Neural Graph Matching for Modification Similarity Applied to Electronic Document Comparison2022 | working-paper

 DocPro  
World Academy of Science, Engineering and Technology  
2020 | journal-article



 

 

 





 

 

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