Majid Mirzanezhad
Education
- Postdoctoral Research Fellow, University of Michigan – Ann Arbor, 2024
- Ph.D. in Computer Science, Tulane University
- B.Sc. and M.Sc. in Computer Science, Tehran Polytechnic
Research Interests
- Computational Geometry
- Geographic Information Systems (GIS)
- Spatial Algorithms
- Transportation
Biography
Majid Mirzanezhad, Ph.D., is a computer scientist whose expertise bridges the theoretical and applied facets of computer science. His research focuses on computational geometry, shape and image analysis, spatial systems, and transportation. Dr. Mirzanezhad is dedicated to designing efficient and impactful computational frameworks that enhance the equity of transportation services for underserved populations. His work aims to improve access to medical facilities for vulnerable individuals, fostering a stigma-free community by providing actionable policy insights. These insights empower policymakers and practitioners in adjacent disciplines to refine infrastructure and optimize logistics for equitable commuting solutions.
Dr. Mirzanezhad has served as a Research Fellow at the University of Michigan's Transportation Research Institute (UMTRI) and as a Research Associate at global policy think tank RAND Corporation, where he contributed to the design of community-centered commuting services. He has received the Best Paper Award at the International Workshop on Spatial Computing and Computation (IWISC) and has been actively involved in numerous high-impact projects at the national level, spanning academia, think tanks, and the private sector. His collaborators include the National Science Foundation, Ford Motor Company, and RAND Corporation.
Dr. Mirzanezhad has been recognized with a Best Paper Award and has built a significant record of publications in peer-reviewed journals and conferences. His contributions extend beyond academia, having collaborated on research initiatives with the RAND Corporation, the National Science Foundation (NSF), and Ford Motor Company. He has also filed a non-provisional patent and actively participates in professional service, including chairing poster and workshop sessions. Moreover, Dr. Mirzanezhad has conducted close to 40 paper reviews for leading scientific communities.
Selected Publications
H. Akitaya, M. Buchin, M. Mirzanezhad, L. Ryvkin, and C. Wenk, "Realizability of Free Spaces of Curves'', In the 34th International Symposium on Algorithms and Computation (ISAAC) 283(5): 1--20, 2023. To appear in the Journal of Computational Geometry: Theory and Applications (CGTA) (Special Issue).
M. Kerkhof, I. Kostitsyna, M. Löffler, M. Mirzanezhad and C. Wenk. "Global Curve Simplification'', European Symposium on Algorithms (ESA), 144:1–14, Munich, Germany, 2019.
M. Mirzanezhad, "On Approximate Near-Neighbors Search under the (Continuous) Fréchet Distance in Higher Dimensions'', Information Processing Letters, 183(C): 1-10, 2023.
M. Mirzanezhad, Andrea Broaddus, Richard Twumasi-Boakye, Tayo Fabusuyi, “Predicting Demands for Online Package Delivery using Limited Historical Observations'', 102nd Annual Meeting on Transportation Research Board (TRB), Washington D.C., 2023. To appear in Transportation Research Board – Part B (Methodological).
J. Gudmundsson, M . Mirzanezhad, A. Mohades, C. Wenk. "Fast Fréchet Distance Between Curves with Long Edges". Intl. J. Computational Geometry & Applications, 29(2):161–187, 2019. The Best Paper Award at International Workshop of Interactive and Spatial Computing (IWISC'18) pages 52-28.
Patent
- US Patent. Energy Efficient Sampling for Last-Mile Delivery Systems, May 2024. Available on Google Patents.
Journal Article, Academic Journal (3)
- Akitaya, H., Buchin, M., Mirzanezhad, M., Ryvkin, L., Wenk, C. (2025). Realizability of free spaces of curves. Computational Geometry; 127: 102151. https://doi.org/10.1016/j.comgeo.2024.102151.
- Mirzanezhad, M., Twumasi-Boakye, R., Fabusuyi, T., Broaddus, A. (2024). Generating online freight delivery demand during COVID-19 using limited data. Transportation Research Part B: Methodological; 190: https://api.elsevier.com/content/abstract/scopus_id/85207771515.
- Mirzanezhad, M. (2024). On approximate near-neighbors search under the (continuous) Fréchet distance in higher dimensions. Information Processing Letters; 183: https://api.elsevier.com/content/abstract/scopus_id/85159179190.
Conference Proceeding (3)
- Mirzanezhad, M. (2025). Fair-Coverage Facility Location: Optimizing Distance and Diversity. New York: 33rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2025); 1154–1157. https://33rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2025).
- Mirzanezhad, M., Rafiey, A. (2025). Approximate and Exact Geometric Generalized Minimum Spanning Trees. Toronto: 37th Canadian Conference on Computational Geometry (CCCG 2025); 173-181. https://37th Canadian Conference on Computational Geometry (CCCG 2025).
- Filtser, O., Mirzanezhad, M., Wenk, C. (2025). Min-Complexity Graph Simplification under Fréchet-Like Distance. The 36th International Workshop on Combinatorial Algorithms (IWOCA 2025); 44-57. https://The 36th International Workshop on Combinatorial Algorithms (IWOCA 2025).
Conference, Poster (2)
- Fabusuyi, T., Wang, Y., Mirzanezhad, M., Twumasi-Boakye, R. (2026). A Scalable Methodology for Estimating E-Commerce Volume at Localized Geographic Levels. Washington D.C.: 105th Annual Meeting on Transportation Research Board (TRB); https://105th Annual Meeting on Transportation Research Board (TRB).
- Mirzanezhad, M. (2024). Clustering Points with Line Segments under the Hausdorff Distance is NP-hard. 31st Annual Fall Workshop on Computational Geometry, 2024; 5 pages. https://www.cs.tufts.edu/research/geometry/FWCG24/.