About Me

Hi, my name is Harry Wang (Chinese: 王建楠). I am a Full Professor of Management Information Systems at the University of Delaware with more than 18 years’ research, teaching, and management experience in AI, Data Science, Business Process Management, and Enterprise Systems. I am also an affiliated faculty at the Data Science Institute and the Institute for Financial Services Analytics.

I currently serve as the Founder and CEO of Takin.AI, a platform dedicated to supporting the teaching and learning of Generative AI. I was the Chief Scientist of Tezign Inc. (a unicorn startup based in Shanghai) and an Independent Director for So-Young International Inc. (NASDAQ: SY). I also served as the Founding Director of OneConnect (NYSE: OCFT) US Research Institute based in New York City from 2018 to 2019 and the VP of Technology for the Association for Information Systems from 2015 to 2018. I was one of the founding members of the Institute for Financial Services Analytics at the University of Delaware and a JPMorgan Chase Fellow from 2014 to 2018. I also founded Conferency in 2015 to provide SaaS solutions for academic conferences.

My recent research interests include Artificial Intelligence, Data Science, Social Computing, and Human-Computer Interaction. My research has been published in academic journals, such as Management Information Systems Quarterly, Information Systems Research, INFORMS Journal on Computing, and Decision Support Systems.

In my spare time, I enjoy coding, sports, music, design, art, and woodworking. I created a brand KU (which means warehouse in Chinese) for my woodworking projects for fun: KuWarehouse.com.

Selected Tutorials and Projects

Please check my blog for more tutorials and projects.

• Data Labeling using GPT APIs: what I learned about labeling data using OpenAI API.
• MVP (large language Model + Vector database + Prompt engineering): how to create a chatbot using OpenAI and your own data. MVP is another name I created for RAG.
• A Practical Guide to Quasi-Experimental Methods (PSM and DID): PSM and DID explained using simple datasets in Python and R.
• When Data Science Meets NFT: a Kaggle NFT dataset I created for teaching data analysis.
• Tailpages (Tailwind + Github Pages): a Jekyll website template based on TailwindCSS, which can be hosted by Github for free.
• A Minimalist End-to-End Machine Learning Tutorial: a set of notebooks covering key basic components of real-world machine learning projects
• Color + Culture + AI: culture-inspired multi-modal color palette generation and colorization
• Calligraphy + AI: abstract art generation based on Chinese calligraphy
• A Minimalist End-to-End Scrapy Tutorial: my most popular tutorial on web scraping with >130K views since posted in September 2019.

Selected Teaching

• Introduction to Generative AI, Spring 2024
• Machine Learning for Business
• Full-Stack Development

Selected Publications

• “Generating Architectural Floor Plans through Conditional Large Diffusion Model”, with Ziming He, Xiaomei Li, Pengfei Wu, Ling Fan, Ning Wang, Li Mingxuan, and Youquan Chen, HCI International 2024 Conference, Washington DC, USA, 2024.
• “Towards an Automatic Prompt Optimization Framework for AI Image Generation”, with Ling Fan, Kunpeng Zhang, Zilong Pei, and Anjun Li, HCI International 2023 Conference, Copenhagen, Denmark, 2023.
• “Revamping Interior Design Workflow Through Generative Artificial Intelligence”, with Ziming He, Xiaomei Li, and Ling Fan, HCI International 2023 Conference, Copenhagen, Denmark, 2023.
• “The Impact of AI-based Diagnosis System on Medical Aesthetic Service Purchases”, with Wenli Zou, Mengxin Li, Cheng Yi, 2022 Symposium on Statistical Challenges in Electronic Commerce Research, Madrid, Spain, 2022
• “The Impacts of Community Composition Diversity on Non-fungible Token Project Success”, with Ainong Duan, Daning Hu, Tao Lu, Michael Chau, 2022 SIGBIT Workshop on Web3 Technologies and Applications, Copenhagen, Denmark, 2022
• “Semantically Enriched Music Visualization via Multimodal Color Generation”, with Yufan Li, Jinggang Zhuo, and Ling Fan, New Interfaces for Musical Expression Conference, 2021.
• “Culture-inspired Multi-modal Color Palette Generation and Colorization: A Chinese Youth Subculture Case”, with Yufan Li, Jinggang Zhuo, and Ling Fan, IEEE 4th International Conference on Multimedia Information Processing and Retrieval, 2021.
• “Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization” (with Rachel Zheng, Kunpeng Zhang, Ling Fan, and Zhe Wang), IEEE Big Data 2021 (arXiv link).
• “Topic-Guided Abstractive Text Summarization: a Joint Learning Approach” (with Chujie Zheng, Kunpeng Zhang, Ling Fan, and Zhe Wang), 2021 (arXiv link).
• “Unifying Online and Offline Preference for Social Link Prediction” (with Fan Zhou, Bangying Wu, Kunpeng Zhang, and Yi Yang), INFORMS Journal on Computing, Volume 33, Issue 4, 2021.
• “Peer Effects in Competitive Environments: Field Experiments on Information Provision and Interventions” (with Zhuoxin Li and Gang Wang), MIS Quarterly, Vol. 45 Issue 1, p163-191, 2021.
• “A Two-Phase Approach for Abstractive Podcast Summarization” (with Chujie Zheng, Kunpeng Zhang, and Ling Fan), Text REtrieval Conference (TREC) Podcasts Track, 2020 (arXiv link).
• “A Baseline Analysis for Podcast Abstractive Summarization” (with Chujie Zheng, Kunpeng Zhang, and Ling Fan), Workshop on Podcast Recommendations, ACM Recommender Systems Conference (RecSys), 2020 (arXiv link).
• “A Framework and Dataset for Abstract Art Generation via CalligraphyGAN” (with Jinggang Zhuo and Ling Fan), NeurIPS Workshop on Machine Learning for Creativity and Design, Vancouver, Canada, 2020 (arXiv link).
• “Recommendation with diversity: an adaptive trust-aware model” (with Ting Yu, Wenhua Li, Junpeng Guo, and Ling Fan), Decision Support Systems, 2019.
• “An intelligent conversation engineering framework for developing dialogue systems,” Winter Conference on Business Analytics, March 7-9, Snowbird, Utah, 2019.
• “The OneConn-MemNN System for Knowledge-Grounded Conversation Modeling,” (with Junyuan Zheng, Surya Kasturi, Mason Lin, Xin Chen, and Onkar Salvi), The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), January 27-February 1, Honolulu, Hawaii, USA, 2019.
• “Personalized recommendation based on time-weighted overlapping community detection,” (with Haoyuan Feng, Jin Tian, and Minqiang Li), Information and Management, Volume 52, Issue 7, 2015, Pages 789–800.
• “An Analytical Framework for Understanding Knowledge Sharing Processes in Online Q&A Communities,” (with G. Alan Wang, Jiexun Li, Weiguo Fan, and Alan Abrahams), ACM Transactions on Management Information Systems, Volume 5 Issue 4, 2015.
• “An Intelligent Approach to Data Extraction and Task Identification for Process Mining,” (with Jiexun Li and Xue Bai), Information Systems Frontiers, Volume 17, Issue 6, pp 1195-1208, 2015.
• “On Risk Management with Information Flows in Business Processes,” (with Xue Bai, Ramayya Krishnan, and Rema Padman), Information Systems Research, Volume 24, Issue 3, pp. 731-749, 2013.