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Polaris Project
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papers

Behnia, F., Karbowski, D., and Sokolov, V. (2023), “Deep generative models for vehicle speed trajectories,” Applied Stochastic Models in Business and Industry, 39, 701–719.
Bendre, S., Maharaj, S., Polson, N., and Sokolov, V. (2023), “On the probability of magnus carlsen reaching 2900,” Applied Stochastic Models in Business and Industry, 39, 372–381.
Polson, N. G., and Sokolov, V. (2023b), “Generative AI for bayesian computation,” arXiv preprint arXiv:2305.14972.
Nareklishvili, M., Polson, N., and Sokolov, V. (2023a), “Deep partial least squares for instrumental variable regression,” Applied Stochastic Models in Business and Industry.
Nareklishvili, M., Polson, N., and Sokolov, V. (2023b), “Generative causal inference,” arXiv preprint arXiv:2306.16096.
Gupta, A., Maharaj, S., Polson, N., and Sokolov, V. (2023), “On the value of chess squares,” Entropy, MDPI, 25, 1374.
Polson, N., Sokolov, V., and Xu, J. (2023), “Quantum bayesian computation,” Applied Stochastic Models in Business and Industry.
Polson, N., and Sokolov, V. (2023a), “Deep learning: A tutorial,” arXiv preprint arXiv:2310.06251.
Baker, E., Barbillon, P., Fadikar, A., Gramacy, R. B., Herbei, R., Higdon, D., Huang, J., Johnson, L. R., Ma, P., Mondal, A., and others (2022), “Analyzing stochastic computer models: A review with opportunities,” Statistical Science, Institute of Mathematical Statistics, 37, 64–89.
Zha, Y., Parker, S. T., Foster, J. J., and Sokolov, V. (2022), “Housing market forecasting using home showing events,” arXiv preprint arXiv:2201.04003.
Schultz, L., Auld, J., and Sokolov, V. (2022), “Bayesian calibration for activity based models,” arXiv preprint arXiv:2203.04414.
Nareklishvili, M., Polson, N., and Sokolov, V. (2022a), “Deep partial least squares for iv regression,” arXiv preprint arXiv:2207.02612.
Polson, N., Sokolov, V., and Xu, J. (2022), “Quantum bayes AI,” arXiv preprint arXiv:2208.08068.
Schultz, L., and Sokolov, V. (2022), “Deep learning gaussian processes for computer models with heteroskedastic and high-dimensional outputs,” arXiv preprint arXiv:2209.02163.
Wang, Y., Polson, N., and Sokolov, V. O. (2022), “Data augmentation for bayesian deep learning,” Bayesian Analysis, International Society for Bayesian Analysis, 1, 1–29.
Ley, H., Auld, J., Verbas, Ö., Weimer, R., Driscoll, S., Mohammadian, K., Golshani, N., Rahim, E., Shabanpour, R., Li, Z., and others (2022), Coordinated transit response planning and operations support tools for mitigating impacts of all-hazard emergency events, United States. Department of Transportation. Federal Transit Administration.
Nareklishvili, M., Polson, N., and Sokolov, V. (2022b), “Feature selection for personalized policy analysis,” arXiv preprint arXiv:2301.00251.
Fotouhi, H., Mori, N., Miller-Hooks, E., Sokolov, V., and Sahasrabudhe, S. (2021), “Assessing the effects of limited curbside pickup capacity in meal delivery operations for increased safety during a pandemic,” Transportation Research Record, SAGE Publications Sage CA: Los Angeles, CA, 2675, 436–452.
Zavareh, M., Maggioni, V., and Sokolov, V. (2021), “Investigating water quality data using principal component analysis and granger causality,” Water, MDPI, 13, 343.
Polson, N., Sokolov, V., and Xu, J. (2021), “Deep learning partial least squares,” arXiv preprint arXiv:2106.14085.
Sokolova, A. O., Marshall, C. H., Lozano, R., Gulati, R., Ledet, E. M., De Sarkar, N., Grivas, P., Higano, C. S., Montgomery, B., Nelson, P. S., and others (2021), “Efficacy of systemic therapies in men with metastatic castration resistant prostate cancer harboring germline ATM versus BRCA2 mutations,” The Prostate, 81, 1382–1389.
Bhadra, A., Datta, J., Polson, N., Sokolov, V., and Xu, J. (2021), “Merging two cultures: Deep and statistical learning,” arXiv preprint arXiv:2110.11561.
Hsu, Y. L., Jeng, C. C., Murali, P. S., Torkjazi, M., West, J., Zuber, M., and Sokolov, V. (2021), “Bayesian learning: A selective overview,” arXiv preprint arXiv:2112.12722.
Polson, N., and Sokolov, V. (2020), “Deep learning: Computational aspects,” Wiley Interdisciplinary Reviews: Computational Statistics, John Wiley & Sons, Inc. Hoboken, USA, 12, e1500.
Huang, X., Li, B., Peng, H., Auld, J. A., and Sokolov, V. O. (2020), “Eco-mobility-on-demand fleet control with ride-sharing,” IEEE Transactions on Intelligent Transportation Systems, IEEE, 23, 3158–3168.
Sokolov, V. (2020), “Discussion of ‘multivariate generalized hyperbolic laws for modeling financial log-returns—empirical and theoretical considerations’,” Applied Stochastic Models in Business and Industry, John Wiley & Sons, 36, 777–779.
Dixon, M. F., Polson, N. G., and Sokolov, V. O. (2019), “Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading,” Applied Stochastic Models in Business and Industry, 35, 788–807.
Warren, J., Lipkowitz, J., and Sokolov, V. (2019), “Clusters of driving behavior from observational smartphone data,” IEEE Intelligent Transportation Systems Magazine, IEEE, 11, 171–180.
Polson, N. G., and Sokolov, V. (2019), “Bayesian regularization: From tikhonov to horseshoe,” Wiley Interdisciplinary Reviews: Computational Statistics, John Wiley & Sons, Inc. Hoboken, USA, 11, e1463.
Wang, Y., Polson, N. G., and Sokolov, V. O. (2019), “Scalable data augmentation for deep learning,” arXiv preprint arXiv:1903.09668.
Sokolov, V., and Polson, M. (2019), “Strategic bayesian asset allocation,” arXiv preprint arXiv:1905.08414.
Li, D., Liu, J., Park, N., Lee, D., Ramachandran, G., Seyedmazloom, A., Lee, K., Feng, C., Sokolov, V., and Ganesan, R. (2019), “Solving large-scale 0-1 knapsack problems and its application to point cloud resampling,” arXiv preprint arXiv:1906.05929.
Chen, H., Jajodia, S., Liu, J., Park, N., Sokolov, V., and Subrahmanian, V. (2019), “FakeTables: Using GANs to generate functional dependency preserving tables with bounded real data.” in IJCAI, pp. 2074–2080.
Nicholas G. Polson, V. O. S. (2019), “Deep learning,” Wiley StatsRef: Statistics Reference Online.
Schultz, L., and Sokolov, V. (2018a), “Bayesian optimization for transportation simulators,” Procedia computer science, Elsevier, 130, 973–978.
Schultz, L., and Sokolov, V. (2018b), “Deep reinforcement learning for dynamic urban transportation problems,” arXiv preprint arXiv:1806.05310.
Sokolov, V., Imran, M., Etherington, D. W., Karbowski, D., and Rousseau, A. (2018), “Effects of predictive real-time traffic signal information,” in 2018 21st international conference on intelligent transportation systems (ITSC), IEEE, pp. 1834–1839.
Schultz, L., and Sokolov, V. (2018c), “Practical bayesian optimization for transportation simulators,” arXiv preprint arXiv:1810.03688.
Polson, N., and Sokolov, V. (2017a), “Bayesian particle tracking of traffic flows,” IEEE Transactions on Intelligent Transportation Systems, IEEE, 19, 345–356.
Sokolov, V., Larson, J., Munson, T., Auld, J., and Karbowski, D. (2017), “Maximization of platoon formation through centralized routing and departure time coordination,” Transportation Research Record, SAGE Publications Sage CA: Los Angeles, CA, 2667, 10–16.
Sokolov, V. (2017), “Discussion of ‘deep learning for finance: Deep portfolios’,” Applied Stochastic Models in Business and Industry, 33, 16–18.
Auld, J., Sokolov, V., and Stephens, T. S. (2017), “Analysis of the effects of connected–automated vehicle technologies on travel demand,” Transportation Research Record, SAGE Publications Sage CA: Los Angeles, CA, 2625, 1–8.
Polson, N. G., and Sokolov, V. (2017b), “Deep learning: A bayesian perspective.”
Verbas, Ö., Sokolov, V., Auld, J., and Ley, H. (2017), “Time-dependent capacitated transit routing with real-time demand and supply data.”
Auld, J., Hope, M., Ley, H., Sokolov, V., Xu, B., and Zhang, K. (2016b), “POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations,” Transportation Research Part C: Emerging Technologies, Pergamon, 64, 101–116.
Auld, J., Karbowski, D., Sokolov, V., and Kim, N. (2016a), “A disaggregate model system for assessing the energy impact of transportation at the regional level,” in TRB 2016 annual meeting, washington, DC.
Karbowski, D., Sokolov, V., and Jongryeol, J. (2016b), “Fuel saving potential of optimal route-based control for plug-in hybrid electric vehicle,” IFAC-PapersOnLine, Elsevier, 49, 128–133.
Jacquier, E., Polson, N., and Sokolov, V. (2016), “Sequential bayesian learning for merton’s jump model with stochastic volatility,” arXiv preprint arXiv:1610.09750.
Larson, J., Munson, T., and Sokolov, V. (2016), “Coordinated platoon routing in a metropolitan network,” in 2016 proceedings of the seventh SIAM workshop on combinatorial scientific computing, Society for Industrial; Applied Mathematics, pp. 73–82.
Karbowski, D., Kim, N., Auld, J., and Sokolov, V. (2016a), “Assessing the energy impact of traffic management and vehicle hybridisation,” International Journal of Complexity in Applied Science and Technology, Inderscience Publishers (IEL), 1, 107–124.
Polson, N., and Sokolov, V. (2015), “Bayesian analysis of traffic flow on interstate i-55: The LWR model.”
Sokolov, V., Karbowski, D., Kim, N., and Auld, J. (2015), “Assessing the energy impact of traffic management and vehicle hybridization,” in 25th ITS annual meeting.
Luo, Q., Auld, J., and Sokolov, V. (2015), “Addressing some issues of map-matching for large-scale, high-frequency GPS data sets,” in TRB 2015 annual meeting, washington, DC.
Karbowski, D., Sokolov, V., and Rousseau, A. (2015), Vehicle energy management optimization through digital maps and connectivity, Argonne National Lab.(ANL), Argonne, IL (United States).
Richey, A. S., Richey, J. E., Tan, A., Liu, M., Adam, J. C., and Sokolov, V. (2015), “Assessing the use of remote sensing and a crop growth model to improve modeled streamflow in central asia,” in AGU fall meeting abstracts, pp. H44F–08.
Sokolov, V., Karbowski, D., and Kim, N. (2014a), “Assessing the energy impact of traffic management and ITS technologies,” in 21st ITS world congress.
Wang, M., Sabbisetti, R., Elgowainy, A., Dieffenthaler, D., Anjum, A., Sokolov, V., and others (2014), “GREET model: The greenhouse gases, regulated emissions, and energy use in transportation model,” Chicago, USA: Argonne National Laboratory.
Sokolov, V. O., Zhou, X., and Langlois, P.-A. (2014b), “A framework for arterial traffic flow modeling-POLARIS.”
Auld, J., Hope, M., Ley, H., Xu, B., Zhang, K., and Sokolov, V. (2013), “Modelling framework for regional integrated simulation of transportation network and activity-based demand (polaris),” in International symposium for next generation infrastructure.
Auld, J., Sokolov, V., Fontes, A., and Bautista, R. (2012), “Internet-based stated response survey for no-notice emergency evacuations,” Transportation Letters, Taylor & Francis, 4, 41–53.
Sokolov, V., Auld, J., and Hope, M. (2012), “A flexible framework for developing integrated models of transportation systems using an agent-based approach,” Procedia Computer Science, Elsevier, 10, 854–859.
Datta, B. N., and Sokolov, V. (2011), “A solution of the affine quadratic inverse eigenvalue problem,” Linear Algebra and its Applications, North-Holland, 434, 1745–1760.
Park, Y. S., Manli, E., Hope, M., Sokolov, V., and Ley, H. (2010), Fuzzy rule-based approach for evacuation trip demand modeling.
Wang, M., Sabbisetti, R., Elgowainy, A., Dieffenthaler, D., Anjum, A., Sokolov, V., and GREET, M. (2010), “The greenhouse gases, regulated emissions, and energy use in transportation model,” Center for Transportation Research Argonne National Laboratory, Argonne, IL.
Datta, B. N., Deng, S., Sokolov, V., and Sarkissian, D. (2009), “An optimization technique for damped model updating with measured data satisfying quadratic orthogonality constraint,” Mechanical Systems and Signal Processing, Academic Press, 23, 1759–1772.
Datta, B. N., and Sokolov, V. (2009), “Quadratic inverse eigenvalue problems, active vibration control and model updating,” Applied and Computational Mathematics, 8, 170–191.
Sokolov, V. O. (2008), “Quadratic inverse eigenvalue problems: Theory, methods, and applications,” PhD thesis, Northern Illinois University.
Are, S., Dostert, P., Ettinger, B., Liu, J., Sokolov, V., Wei, A., and Wiegand, K. (2006), “Reservoir model optimization under uncertainty.”
Krukier, L., Pichugina, O., Sokolov, V., and Vulkov, L. (2006), “Numerical investigation of krylov subspace methods for solving non-symmetric systems of linear equations with dominant skew-symmetric part,” International Journal of Numerical Analysis and Modeling, University of Alberta, 3, 115–124.

software

  • POLARIS: Designer. Developer. Transportation systems simulations framework (C++)
  • GREET: Designer. Lead Developer. An implementation of The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model. (C#, .NET, SQLite). more then 800 unique users within first year of release, 2013
  • MATCOM: Contributor. Distributed on CD with Numerical Linear Algebra and Applications, Second Edition book By Biswa Nath Datta, SIAM. (MATLAB)
  • TRANSIMS: Contributor. An agent-based forecast software for modeling regional transport systems. (C++); 22,295 total downloads since 2006
  • Advanced Numerical Methods II: Sole Developer. Package for solving large scale control problems. (Mathematica); an experimental library that was not published

research coverage

  • Demystifying the future of connected and autonomous vehicles (Newswise
  • Argonne wins grant to help transit agencies cope with emergencies (Chicago Tribune)
  • Argonne will research how transportation systems should respond to natural hazards (WBEZ)
  • What Happens When Developers, Scientists and Super-Computers Connect on Urban Design (Next City)
  • UM wins $2.7M grant to study driverless cars (The Detroit News)
  • UM teams with Argonne, Idaho national labs to study potential energy savings of connected vehicles (Michigan News)
  • Argonne to study emergency response of Chicago transit (Chicago Sun Times)
  • Designing future cities (phys.org)
  • Using a Real Life SimCity to Design a Massive Development (Curbed Chicago)