Dr. Satish Ukkusuri
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​​​Reilly Professor of Civil Engineering
Transportation and Infrastructure Systems Engineering Group
Lyles School of Civil Engineering
Purdue University
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HAMP G167D, 550 Stadium Mall Drive
West Lafayette, IN 47907-2051 Phone: (765) 494-2296
Fax: (765) 494-7996
Email: sukkusur@purdue.edu

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UMNILAB COVID-19 Research Papers

  1. Verma, R., Yabe, T., & Ukkusuri, S. V. (2021). Spatiotemporal contact density explains the disparity of COVID-19 spread in urban neighborhoods. Scientific Reports, 11(1), 1-11.​
  2. Yabe, T., Tsubouchi, K., Fujiwara, N., Wada, T., Sekimoto, Y., & Ukkusuri, S. V. (2020). Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic. Scientific reports, 10(1), 1-9. 
  3. Qian, X., Sun, L., & Ukkusuri, S. V. (2021). Scaling of contact networks for epidemic spreading in urban transit systems. Scientific reports, 11(1), 1-12.
  4. Qian, X., & Ukkusuri, S. V. (2020). Modeling the spread of infectious disease in urban areas with travel contagion. arXiv preprint arXiv:2005.04583.
  5. Lei, Z., Ukkusuri, S.V. (2022). Understanding the Recovery of On-Demand Mobility Services in the COVID-19 Era. J. Big Data Anal. Transp.


BIOGRAPHY     
Dr. Satish V. Ukkusuri is a Professor in the Lyles School of Civil Engineering at Purdue University since July 2014 where he teaches courses in transportation systems and freight and logistics planning. Previously, he was an Associate Professor at Purdue University from 2009-2014 and on the faculty of the Department of Civil and Environmental Engineering at the Rensselaer Polytechnic Institute from August 2005 - August 2009. Dr. Ukkusuri is a member of the Transportation and Infrastructure group at Purdue. Dr. Ukkusuri is a co-lead of the Building Sustainable Communities cluster hire at Purdue University with a goal of hiring seven faculty in this interdisciplinary area.
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AREA OF INTERESTS
Dr. Ukkusuri's lab focuses on developing innovative solutions for complex network problems in transportation systems coupled with other networks to improve their performance, resilience and sustainability. Dr Ukkusuri's research focus in the following areas:
  • Transportation Network Modelling
  • Emergency Management Issues
  • Freight Transportation
  • Intelligent Transportation Systems
  • Energy, Environment, Sustainability in Transportation
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RECENT ACTIVITIES

May 2022: New paper "Progression of hurricane evacuation-related dynamic decision-making with information processing” has been published on Transportation Research Part D.
Mar. 2022: New paper "Quantifying the spatial homogeneity of urban road networks via graph neural networks” has been published on Nature Machine Intelligence.
Feb. 2022: New paper "Toward data-driven, dynamical complex systems approaches to disaster resilience” is published on PNAS.
Nov. 2021: Dr. Washim Mondal received the best paper award in the Cooperative AI workshop at NeurIPS 2021 for his work "On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)” .
June 2021: Congratulation on the graduation of Dr. Hemant Gehlot!

Lab Dinner


July 2020: New paper "Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks” is now online.
Apr. 2020: Our new paper "Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data” has been published on Sustainable Cities and Society.
Mar. 2019: New paper "A-RESCUE 2.0: A High-Fidelity, Parallel, Agent-Based Evacuation Simulator” is now online.
Dec. 2018: Congratulation on the graduation of Dr. Xinwu Qian and Dr. Wenbo Zhang!

Lab Dinner


Nov. 2018: New paper "User equilibrium with a policy-based link transmission model for stochastic time-dependent traffic networks” is now online.
Nov. 2018: New paper "Joint modeling of evacuation departure and travel times in hurricanes" is now online.
Oct. 2018: Our work in the recent research foundation news "A little help from your friends is key to natural disaster recovery, Purdue research study suggests".
Oct. 2018: New paper "A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data" is now online.
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