Dr. Jieshu Wang
Department of Technology and Society
Ph.D., Arizona State University
Dr. Jieshu Wang is an Assistant Professor in the Department of Technology and Society (DTS) in Stony Brook University’s College of Engineering and Applied Sciences (CEAS). She is an interdisciplinary researcher studying the human and social dimensions of artificial intelligence (AI) and how people can thrive in an AI-integrated future. She combines computational methods with qualitative insights to trace technology trends and understand their broader societal impact. At Stony Brook University, she is also affiliated with AI Innovation Institute (AI3), and the Center for Changing Systems of Power.
Dr. Wang earned her Ph.D. in Human and Social Dimensions of Science and Technology (HSD) from Arizona State University, after earlier degrees in Civil Engineering (Beijing Jiaotong University), Economics (Peking University), and Communication, Culture and Technology (Georgetown University). Before joining Stony Brook University, she was a postdoctoral research scholar at ASU’s Decision Theater. She has also worked as a patent examiner, an editor at a popular science magazine, and co-founded Synced (机器之心), an AI-focused media company in China.
Her research looks both backward and forward. Backward-looking, she examines how AI and other emerging technologies are created, who creates them, and who is missing from the process. Forward-looking, she studies how AI is transforming the way we live, connect, invent, work, and adapt, as well as how AI might help address challenges such as climate change and workforce transitions. She also explores how AI shapes and is shaped by our collective imaginaries of the future, including in science fiction movies.
Dr. Wang publishes in high-impact journals such as Communications of the ACM, Technological Forecasting and Social Change (TFSC), Energy and AI, Humanities and Social Sciences Communications (HSSC), and Environmental Innovation and Societal Transitions (EIST). Her work has also been presented at leading conferences, including the Annual Meeting of the AOM, the Annual Meeting of the ASIS&T, the Hawaii International Conference on System Sciences (HICSS), the Open and User Innovation Conference (OUI), the International Conference on Social Informatics (SocInfo), and the Annual Meeting of the Society for the History of Technology (SHOT).
Contact Information and Other Links:
Email: jieshu.wang@stonybrook.edu
Office: Old Computer Science 1419
Google Scholar | Google Site | Personal Blog | LinkedIn | Translations
Selected publications:
Wang, J., & Maynard, A. (2025). Gender disparity in U.S. patenting. Humanities and Social Sciences Communications, 12(1), 1730. https://doi.org/10.1057/s41599-025-06038-6
Wang, J., & Solís, P. (2025). Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach. Energy and AI, 21, 100580. https://doi.org/10.1016/j.egyai.2025.100580
Wang, J., Gundogdu, T. B., Ivic, R. K., Butler, B. S., & Lee, M. (2025). News Deserts as Information Problems: A Case Study of Local News Coverage in Alabama. Proceedings of the Association for Information Science and Technology, 62(1), 741–753. https://doi.org/10.1002/pra2.1293
Wang, J., Gundogdu, T. B., Butler, B. S., & Lee, M. (2025). Spatial Dynamics of Local News: Mapping City Co‐Mentions in Alabama. Proceedings of the Association for Information Science and Technology, 62(1), 1708–1710. https://doi.org/10.1002/pra2.1516
Wang, J., Kiran, E., Aurora, S. R., Simeone, M., & Lobo, J. (2025). ChatGPT on ChatGPT: An Exploratory Analysis of its Performance in the Public Sector Workplace. Digit. Gov.: Res. Pract., 6(2), 29:1-29:28. https://doi.org/10.1145/3676281
Wang, J., & Lobo, J. (2024). Extensive growth of inventions: Evidence from U.S. patenting. Technological Forecasting and Social Change, 207, 123586. https://doi.org/10.1016/j.techfore.2024.123586
Wang, J., Lobo, J., Shutters, S. T., & Strumsky, D. (2024). Fueling a net-zero future: The influence of government-funded research on climate change mitigation inventions. Environmental Innovation and Societal Transitions, 51, 100836. https://doi.org/10.1016/j.eist.2024.100836
Wang, J., Maynard, A., Lobo, J., Michael, K., Motsch, S., & Strumsky, D. (2024). Knowledge Combination Analysis Reveals That Artificial Intelligence Research Is More Like “Normal Science” Than “Revolutionary Science.” Proceedings of the 57th Hawaii International Conference on System Sciences, 5598--5607. https://hdl.handle.net/10125/107058