Resume

Jieshu Wang | 汪婕舒

Postdoctoral Research Fellow at Decision Theater, Arizona State University

Google Scholar page

  • Computational Social scientist / Data Scientist
  • Research interest
    • Artificial intelligence, invention, innovation, complex adaptive system, science of science, civic technology, public engagement, women in STEM, information systems

Education

  • PhD in Human and Social Dimensions of Science and Technology, Arizona State University
  • MA in Communication, Culture & Technology, Georgetown University
  • PGD in Integrated Marketing Communications, The University of Hong Kong, School of Professional and Continuing Education, Graduate with Distinction
  • Master of Engineering in Underground Engineering, Beijing Jiaotong University
  • Double Bachelor’s Degree in Economics, Peking University, China Center for Economic Research
  • Bachelor of Engineering in Civil Engineering, Beijing Jiaotong University

Additional Trainings

  • Agent-Based Modeling: ASU and Math+ Cluster Berlin Joint Spring School on Agent-Based Modeling, Arizona State University, Tempe, AZ, U.S.
  • Socio-Technical Systems: The Consortium for the Science of Sociotechnical Systems (CSST) Summer Research Institute, University of Texas at Austin, Austin, TX, U.S.

Publications

  • Peer-Reviewed Publications
    • [In Press] Wang, J., Burcu, T., 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.
    • 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., Kiran, E., Aurora, S. R., Simeone, M., & Lobo, J. (2024). ChatGPT on ChatGPT: An Exploratory Analysis of its Performance in the Public Sector Workplace. Digital Government: Research and Practice. https://doi.org/10.1145/3676281
    • 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
    • Hsu, J. H.-P., Wang, J., & Lee, M. (2022). Towards an Expectation-Oriented Model of Public Service Quality: A Preliminary Study of NYC 311. In F. Hopfgartner, K. Jaidka, P. Mayr, J. Jose, & J. Breitsohl (Eds.), Social Informatics (pp. 447–458). Springer International Publishing. https://doi.org/10.1007/978-3-031-19097-1_31
    • Lee, M., Wang, J., Janzen, S., Winter, S., & Harlow, J. (2021). Crowdsourcing Behavior in Reporting Civic Issues: The Case of Boston’s 311 Systems. Academy of Management Proceedings, 2021(1), 16532. https://doi.org/10.5465/AMBPP.2021.16532abstract
    • Pine, K. H., Hinrichs, M. M., Wang, J., Lewis, D., & Johnston, E. (2020). For Impactful Community Engagement: Check Your Role. Communications of the ACM, 63(7), 26–28.
  •  Book Chapters
    • Hinrichs, M., Wang, J., Roe, C., & Johnston, E. W. (2025). AI integration in public service: A case study from Peoria, Illinois. In Participatory Artificial Intelligence in Public Social Services: From Bias to Fairness in Assessing Beneficiaries. Springer Nature Switzerland
    • Co-author of The Peak of Technology: In-Depth Interpretation of the 50 Breakthrough Technologies on MIT Technology Review
      • 17/50 Chapters include Deep Learning, Baxter: the Blue Collar Robot, Agile Robots, 3D Transistors, Magic Leap, Project Loon, Egg Stem Cells, Nanopore Sequencing, Internet of DNA, Agricultural Drones, etc.
  • Working Papers, Preprints, and Technical Reports
    • Li, R., & Wang, J. (2025). The Impact of Generative AI on the Urban Transportation Workforce. [Working Paper]
    • Shutters, S. T., Lobo, J., & Wang, J. (2023). The Role of Occupational Socialness on the Productivity of Metropolitan Economies. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4659919
    • Bernstein, M. J., Wang, J., Bevaresh, S., Keeler, L. W., & Basile, G. (2021). Social Value in Sustainability Assessment and Reporting. Arizona State University, School for the Future of Innovation in Society, Global Futures Laboratory, and The Global KAITEKI Center.
  • Manuscripts Submitted
    • [In Revision] Wang, J., & Solís, P. (2025). Identifying Latent Workforce Capacities for Extreme Heat Resilience: An AI-Assisted Approach. Energy & AI
    • [In Revision] Wang, J., & Maynard, A. (2025). Unpacking Gender Disparity in U.S. Patenting. Humanities and Social Science Communications
    • [Under review] Wang, J., Lobo, J., Shutters, S. T., & Strumsky, D. (2025). Comparing AI and Non-AI Inventions: Empirical Insights on Knowledge Recombination, Novelty, and Impact. Technovation
    • [Under review] Wang, J. (2025). The History of Artificial Intelligence in China (1950s—1980s). Technology and Culture
  • Conference / Workshop Presentations
    • [Accepted] Wang, J., Burcu, T., Ivic, R. K., Butler, B. S., & Lee, M. (2025, November 14). News Deserts as Information Problems: A Case Study of Local News Coverage in Alabama. 88th Annual Meeting of the Association for Information Science and Technology, Washington, D.C. https://www.asist.org/meetings-events/am/am25/
    • Aurora, S. R., Wang, J., & Fouladi, F. (2025, February). A human-AI integrative framework to co-produce futures of work at the border. The 7th Annual International Conference on Border Studies, Edinburg, TX.
    • Wang, J., Lobo, J., & Shutters, S. (2024). Poster: Using LLMs to Find Sustainability Capacity. National Sustainability Society Conference, Seattle, WA, U.S. https://keep.lib.asu.edu/items/196991
    • Wang, J., & Solís, P. (2024). Latent Workforce Capacities for Heat Solutions in Arizona. National Sustainability Society Conference, Seattle, WA, U.S.
    • Kim, S., & Wang, J. (2024). Identifying Environmental Bias in Large Language Models. National Sustainability Society Conference, Seattle, WA, U.S.
    • Wang, J., Aurora, S. R., Johnston, E. W., & Hinrichs, M. (2024). Nothing about AI, Without AI: An Integrative Framework to Co-Produce Futures of Work. Open and User Innovation Conference (Harvard Business School), Boston, MA. https://web.cvent.com/event/42f1768e-22f6-4d20-b7b1-6a7ee084d8ce/summary
    • Wang, J. (2022). Artificial Intelligence is already here. It’s just not evenly distributed. Conference on Governance of Emerging Technologies and Science, Phoenix, AZ, U.S.
    • Lee, M., Harlow, J., Gordon, E., Wang, J., Johnston, E., Janzen, S., & Winter, S. (2020). Toward Understanding Civic Data Bias in 311 Systems: An Information Deserts Perspective. ACM CSCM 2020, ACM 978-1-4503-6819-3/20/04.
    • Wang, J. (2018). The History of Artificial Intelligence in China: 1950s—1980s. Annual Meeting of the Society for the History of Technology (SHOT), St. Louis, MO, U.S. [URL]
  • Invited Talks
    • Wang, J., & Solís, P. (2025, February). LLM Insights for Policy: Enhancing Workforce Strategies to Combat Extreme Heat. Large Language Models and Policy Documents Workshop. Center on Technology, Data and Society, Arizona State University, Tempe, AZ, U.S.
    • Wang, J. (2024, October). Using LLMs to Analyze Workforce Data for Sustainability Research. Center for Behavior, Institutions and the Environment, Arizona State University, Tempe, AZ, U.S.
    • Wang, J. (2024). Workforce Safety and Development with Extreme Heat. Extreme Heat Policy Innovation Summit, Washington, D.C. https://fas.org/extreme-heat-summit/
    • Wang, J. (2024, March). Introduction to Patent Data Analysis. Decision Theater, Arizona State University, Tempe, AZ, U.S.
    • Wang, J. (2024, March). Artificial Intelligence, Science of Science, and Future of Work. Center on Technology, Data, and Society, Arizona State University, Phoenix, AZ, U.S.
    • Wang, J. (2022, December). Crowdsourcing Cities: Exploring 311 Data [remote talk]. Northwestern Institute on Complex Systems (NICO), Kellogg School of Management at Northwestern University, Evanston, IL, U.S.
    • Wang, J. (2019, January). The Future of Artificial Intelligence [Open Student Mini Workshop]. Artificial Wisdom Research Group, Graduate School of Advanced Integrated Studies in Human Survivability (GSAIS, aka Shishu-Kan), Kyoto University, Kyoto, Japan. https://aw.gsais.kyoto-u.ac.jp/images/poster20190119.pdf

Work Experience

Internship

Skills & Hobbies

  • Language Skills: Chinese (Native); English (Proficient).
  • Computational Skills
    • Programming language: Python (advanced).
    • Data Science:
      • Proficient in data cleaning, data wrangling, statistical analysis, regression models, geospatial analysis, and data visualization.
      • Familiar with libraries including Pandas, Polars, GeoPandas, Numpy, statsmodels, seaborn, Matplotlib, scikit-learn, metaknowledge.
    • Database: SQLite
    • Large Language Models: iterative prompt engineering, familiar with OpenAI APIs
    • Natural Language Processing: text reprocessing, text mining, latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) topic modeling, wordcloud analysis, libraries (NLTK, genism, textacy)
    • Other Machine Learning: Basic knowledge about CNNs, GANs, RNNs, word embedding, Bayesian networks, statistical machine learning.
    • Web: Flask (basic), WordPress (intermediate), HTML (basic), CSS (basic).
    • Network analysis: Gephi (intermediate), VOS Viewer (intermediate), networkx (intermediate).
  • Qualitative Research Skills
    • Survey: Qualtrics, Prolific, QuestionPro, Amazon Turks.
  • Others: Auto CAD (intermediate).
  • Music
  • Handicraft: fabric & leather art
  • Digital Painting

Everything we call real is made of things that cannot be regarded as real. – Niels Bohr