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 University. I work with Prof. Jessica Hullman at the Midwest Uncertainty Collective (MU Collective) Research Lab. My research investigates effective means to quantify and visualize uncertainty inherent in ML/AI models. I develop visualizations, systems, and interfaces that can support effective data-driven decision-making through uncertainty communication.

For example, my work on uncertainty quantification and visualization has explored how to support graph-based decision-making, where decision-makers need to reason about uncertainty embedded in high-dimensional probabilistic graphs; how to support AI-advised decision-making by using post hoc methods to make prediction uncertainty of "black-box" AI models more explainable and transparent; and how to design predictive interfaces to communicate uncertainty and persuade strategic decision-making in multi-agent competitive settings, where decision-makers need to incorporate their anticipation of competitors' actions based on shared information stimuli.

Through an interdisciplinary and mixed-methods approach, I use ML/AI models to dissect and elucidate large complex systems, design uncertainty quantification methods, and extract quantitative insights from large-scale online experiments.

Before starting my doctoral studies 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, Dongping (May, 2024). Evaluating the Utility of Uncertainty Quantification in Predictive Interfaces for AI-Advised Decision-Making. Presented at ACM CHI 2024, Honolulu, Hawaii.
  • Zhang, Dongping and Negar Kamali (October, 2023). Uncertainty Quantification using Conformal Prediction for Deep Learning Classifiers. Presented at A Symposium on Human+AI at The University of Chicago.
  • Zhang, Dongping (October, 2021). Visualizing Uncertainty Embedded in Probabilistic Graph Models. Presented at IEEE VIS 2021 Virtual.
  • Antone, Brennan and Dongping Zhang (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, Dongping and Uri Wilensky (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