Dongping Zhang


I am a Ph.D. candidate in Technology and Social Behavior, a joint Ph.D. program in Computer Science and Communication Studies at Northwestern. I work with Prof. Jessica Hullman at Midwest Uncertainty Collective (MU Collective).

My research investigates effective means to quantify and communicate prediction uncertainty inherent in machine learning models, aiming to enhance human-in-the-loop, data-driven decision-making. For instance, how can we better understand individual decision-making and aggregate social welfare in shared prediction contexts where predictions can influence the outcomes they try to predict? How can we process the uncertainty in network predictions where the occurrences of predicted ties can be dependent on each other? How do we make predictions and their associated uncertainty more explainable and transparent for "Black-Box" AI Models?

My research produces tools and insights that enable users to make more informed decisions, thereby enhancing the efficiency of system outcomes. By adopting an interdisciplinary mixed-method approach, I utilize ML/AI models to dissect and understand large, complex systems. This process entails designing intuitive uncertainty visualizations and conducting extensive, large-scale online experiments.

Before starting my doctoral study at Northwestern, I received a B.A. in Economics and a B.A. in Statistics from UC Berkeley and later an M.A. in Computational Social Science from UChicago. You can find my academic CV and one-page résumé here.


Peer-reviewed Conference Journal Paper

Conference Presentation

  • Zhang, D. , Chatzimparmpas, A., Kamali, N., & Hullman, J. (October, 2023). Uncertainty Quantification using Conformal Prediction for Deep Learning Classifiers. Presented at A Symposium on Human+AI at The University of Chicago.
  • Zhang, D. (October 29, 2021). Visualizing Uncertainty Embedded in Probabilistic Graph Models. IEEE VIS 21 Virtual.
  • Antone, B., Zhang, D., Li, H., Zhang, T., Kudaravalli, A., Xu, Y., DeChurch, L.A., Leonardi, P.M., & Contractor, N.S. (October, 2019). Predictive Extensions to ERGMs and Applications in Real-time Monitoring of Organizational Social Networks. Presented at the Seventh International Workshop on Social Network Analysis (ARS'19), Vietre sul Mare, Italy.

Open-sourced Model

  • Zhang, D., & Wilensky, U. (2018). NetLogo Taxi Cabs Model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Research Experience

Compass Lexecon

Economic Research Assistant
Summer 2017


Economic Research Assistant
Summer 2016



Programming Languages
  • Social Network Analysis
  • Agent-based Simulation
  • Geospatial Analysis
  • Bayesian Statistics & Modeling
  • Data and Uncertainty Visualizations
  • Web-based Prototyping
  • Survey & Design of Experiment
  • Big Data
  • Machine Learning
  • Artificial Neural Network

Fellowship & Scholarship

Segal Design Institute Research Cluster Fellowship

Northwestern University

Selected as an interdisciplinary design research fellow to advance knowledge of design innovation.


Regents' and Chancellor's Scholarship

University of California, Berkeley

The most prestigious scholarship awarded to the top 2% of undergraduates.


Berkeley Club of Hong Kong Undergraduate Scholarship

University of California, Berkeley

Scholarship awarded based on academic merit and extra-curricular achievements.



Northwestern University
Department of Computer Science
Mudd Hall 3538
2233 Tech Drive
Evanston, IL 60208