Mingwei Li
Postdoctoral Scholar, Computer Science
Research in visualization, deep learning, and human-centered AI.
Tufts University, Department of Computer Science
Somerville, MA
Appointments
- Tufts University — Postdoctoral Scholar, 2024–present
- Vanderbilt University — Postdoctoral Scholar, 2021–2024
Education
Doctor of Philosophy in Computer Science
Major in Computer Science, minor in Mathematics.
Thesis: Algebraic Visual Design for Deep Learning.
Advisor: Prof. Carlos Scheidegger.
Bachelor of Engineering
Electronic Engineering, minor in Mathematics.
Thesis: Wi-Fi based Indoor Localization.
Advisor: Prof. Shenghui Song.
Fellowships & Awards
- Graduate Assistantship, Department of Computer Science, University of Arizona, 2016–2021
- GPSC Travel Grant, University of Arizona, Oct 2018
- Dean’s List, School of Engineering, HKUST, 2011–2014
- Scholarship for Continuing Undergraduate Students, HKUST, 2011–2014
- Honorable Mention, IEEE SciVis Contest, 2025
- Best Submission, IEEE VIS Workshop on Visualization for AI Explainability (VISxAI), 2020
- Best Paper, International Symposium on Graph Drawing and Network Visualization, 2020
Publications
- F. Fajardo-Rojas, R. Anderson, Mingwei Li, R. Chang, and D. A. Gómez-Gualdrón, “Data-driven insights on the impact of functionalization on metal-organic framework free energies,” Chemistry of Materials, vol. 37, no. 15, pp. 5502–5514, 2025.
- S. Jeong, Mingwei Li, M. Berger, and S. Liu, “Concept Lens: Visual comparison and evaluation of generative model manipulations,” IEEE Transactions on Visualization and Computer Graphics, 2025.
- [Honorable Mention], S. Li, F. Fajardo-Rojas, D. Gomez-Gualdron, R. Chang, and Mingwei Li, “Alloylens: A visual analytics tool for high-throughput alloy screening and inverse design,” 2025. arXiv:2511.02133.
- J. Rogers, L. Shen, A. Mosca, E. Peck, Mingwei Li, A. Hakone, K. Potter, and R. Chang, “House advantage or house of cards? stacking the deck for data videos leads to null results,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, 2025, pp. 1–14.
- Mingwei Li, S. Jeong, S. Liu, and M. Berger, “CAN: Concept-aligned neurons for visual comparison of deep neural network models,” Computer Graphics Forum, vol. 43, 2024, e15085.
- B. Montambault, G. Appleby, J. Rogers, C. D. Brumar, Mingwei Li, and R. Chang, “DimBridge: Interactive explanation of visual patterns in dimensionality reductions with predicate logic,” IEEE Transactions on Visualization and Computer Graphics, 2024.
- Y. Zhao, Mingwei Li, and M. Berger, “CUPID: Contextual understanding of prompt-conditioned image distributions,” Computer Graphics Forum, vol. 43, 2024, e15086.
- K. Gray, Mingwei Li, R. Ahmed, M. K. Rahman, A. Azad, S. Kobourov, and K. Börner, “A scalable method for readable tree layouts,” IEEE Transactions on Visualization and Computer Graphics, vol. 30, no. 2, pp. 1564–1578, 2023.
- S. Jeong, Mingwei Li, M. Berger, and S. Liu, “Concept Lens: Visually analyzing the consistency of semantic manipulation in GANs,” in 2023 IEEE Visualization and Visual Analytics (VIS), IEEE, 2023, pp. 221–225.
- Y. Zhao, Mingwei Li, and M. Berger, “Graphical perception of saliency-based model explanations,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023, pp. 1–15.
- R. Ahmed, F. De Luca, S. Devkota, S. Kobourov, and Mingwei Li, “Multicriteria scalable graph drawing via stochastic gradient descent, (SGD)2,” IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 6, pp. 2388–2399, 2022.
- K. Gray, Mingwei Li, R. Ahmed, and S. Kobourov, “Visualizing evolving trees,” in International Symposium on Graph Drawing and Network Visualization, Springer International Publishing Cham, 2022, pp. 319–325.
- K. Gray, Mingwei Li, R. Ahmed, M. K. Rahman, A. Azad, S. Kobourov, and K. Börner, “A map-based interactive system for visualizing large networks with semantic zooming,” 2022.
- M. Espadoto, G. Appleby, A. Suh, D. Cashman, Mingwei Li, C. Scheidegger, E. W. Anderson, R. Chang, and A. C. Telea, “Unprojection: Leveraging inverse-projections for visual analytics of high-dimensional data,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 2, pp. 1559–1572, 2021.
- Mingwei Li, “Algebraic visual design for deep learning,” Ph.D. dissertation, The University of Arizona, 2021.
- Mingwei Li and C. Scheidegger, “Comparing deep neural nets with UMAP Tour,” arXiv preprint arXiv:2110.09431, 2021.
- Z. Wang, D. Cashman, Mingwei Li, J. Li, M. Berger, J. A. Levine, R. Chang, and C. Scheidegger, “Neuralcubes: Deep representations for visual data exploration,” in 2021 IEEE International Conference on Big Data, pp. 550–551.
- Mingwei Li, Z. Zhao, and C. Scheidegger, “Visualizing neural networks with the grand tour,” Distill, vol. 5, no. 3, e25, 2020.
- [Best Paper], R. Ahmed, F. De Luca, S. Devkota, and Mingwei Li, “Graph drawing via gradient descent, (GD)2,” in Graph Drawing and Network Visualization: 28th International Symposium, GD 2020, Revised Selected Papers, Springer International Publishing, 2020, pp. 3–17.
- [Best Submission], Mingwei Li, and C. Scheidegger, “Toward comparing DNNs with UMAP Tour,” in 3rd Workshop on Visualization for AI Explainability (VISxAI 2020), 2020. visxai.io/2020.
- M. Correll, Mingwei Li, G. Kindlmann, and C. Scheidegger, “Looks good to me: Visualizations as sanity checks,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 830–839, 2018.
Talks
- NSF ID4 Conference, Colorado School of Mines, Oct 2025 — AlloyLens: Visual Analytics for Interactive Inverse Material Design.
- NSF HDR Ecosystem Conference, Ohio State University, Sep 2025 — DimBridge: Interpreting Visual Patterns in Dimensionality Reduction Plots.
- NSF ID4 Conference, UCLA, May 2025 — AI Visualization for Scientific Discovery.
- NSF ID4 Conference, Harvard University, May 2024 — DimBridge: Interpreting Visual Patterns in Projections.
Teaching & Mentoring
- Co-Instructor, CS 175 Computer Graphics, Tufts University, Fall 2025.
- Co-Instructor, CS 178 Visual Analytics, Tufts University, Spring 2025.
- Guest Lecturer, CS 4247/5247 Data Visualization, Vanderbilt University, Spring 2024 & Spring 2023.
- Teaching Assistant & Guest Lecturer, CSC 245 Introduction to Discrete Structures, University of Arizona, Summer 2018.
- Teaching Assistant, CSC 337 Web Programming, University of Arizona, Fall 2016.
Service
- Conference Reviewer, IEEE Visualization (VIS), 2018–present.
- Conference Reviewer, IEEE Pacific Visualization (PacificVis), 2025–present.
- Journal Reviewer, IEEE Transactions on Visualization and Computer Graphics (TVCG), 2018–present.
- Journal Reviewer, ACM Transactions on Interactive Intelligent Systems (TiiS), 2024–present.
- Journal Reviewer, IEEE Computer Graphics and Applications (CGA), 2023–present.
- Journal Reviewer, Journal of Graph Algorithms and Applications (JGAA), 2022–present.
- Journal Reviewer, WIREs Computational Statistics, 2021–present.
- Session Chair, IEEE VIS Conference, 2022 — Visual Analytics, Decision Support, and Machine Learning.