Dongping Zhang

dzhang[at]u[dot]northwestern[dot]edu

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 pursuade 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.

Publication

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

PricewaterhouseCoopers

Economic Research Assistant
Summer 2016

Education

Skills

Programming Languages
Tools
Methods
  • 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.

2020-2021

Regents' and Chancellor's Scholarship

University of California, Berkeley

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

2012-2016

Berkeley Club of Hong Kong Undergraduate Scholarship

University of California, Berkeley

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

2014-2015

Contact

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