Feng Guo

Education

• Ph.D. in Statistics
  University of Connecticut, Storrs, CT, 2007
  Dissertation title: Modelling Genetic Data using Bayesian Hierarchical Models
• Ph.D. in Transportation Engineering
  University of Connecticut, Storrs, CT, 2010
  Dissertation title: Nationwide Freight Generation Models
• M.S. Transportation Economics and Management
  Tongji University, Shanghai, China, 2000
• B.S. Highway and Traffic Engineering
  Tongji University, Shanghai, China, 1995

Honors and Awards

• 58th “Most Talked About” Academic Study in 2014, (more than 70 news including CBS, NBC, NPR, Associated Press, Fox News, and Routers) Altmetric.com 2014
• Chair, Transportation Statistics Interest group of American Statistical Association. 2016-current
• Associate Editor,  Sankhya Series B. 2016-current
• 2015 Taylor Technical Talent Award, “Impact of Roadway Lighting on Crash Safety” Ronald Gibbons, Feng Guo,  Alejandra Medina, Travis Terry, Jianhe Du, Paul Lutkevich, Qing Li
• Best Poster Award: “Senior Fitness-to-Drive Evaluation using Naturalistic Driving Study Data”, Feng Guo, Youija Fang, Jonathan Antin,  The Fourth International Symposium on Naturalistic Driving Study
• Section on Bayesian Statistical Science best student paper award, Joint Statistical Meeting, Salt Lake City, 2007
• Gottfried Noether Award, University of Connecticut, For excellence in mathematical statistics, January 2004
• Graduate Predoctoral Fellowship, University of Connecticut,  2004

Professional Memberships

• American Statistical Association
• Mu Sigma Rho national statistical honor society
• International Chinese Statistical Association

Professional Service

• Co-Chair, Data Analysis Committee. SAE Crash Data Collection and Analysis
• Vice-Chair, Transportation Statistics Interest group of American Statistical Association.
• Member of the Transportation Research Board Committee on Statistical Methods, (ABJ80)
• Member of the Transportation Research Board Committee on Safety Data, Analysis, and Evaluation (ANB20)
• National Academy of Sciences Panel Study on Research Methodologies and Statistical Approaches to Understanding Driver Fatigue Factors in Motor Carrier Safety and Driver Health.
• Member of the Transportation Research Board Subcommittee on Traffic Surrogate Measures

Computational Statistics

Spatial Statistics

• Bayesian Models
• Transportation Safety Modeling
• Spatial Statistics
• Generalized Linear Models

• Liu, L., Racz, D., Vaillancourt, K., Michelman, J., Barnes, M., Mellem, S., Eastham, P., Green, B., Armstrong, C., Bal, R., O’Banion, S., & Guo, F. (2023). Smartphone-based hard-braking event detection at scale for road safety services. Transportation Research Part C: Emerging Technologies, 146, 103949.
• Shi, L., Qian, C., & Guo, F.* (2022). Real-time driving risk assessment using deep learning with XGBoost. Accident Analysis & Prevention, 178, 106836. https://doi.org/https://doi.org/10.1016/j.aap.2022.106836
• Guo, F., & Lu, D. (2022). How many crashes does cellphone use contribute to? Population attributable risk of cellphone use while driving. Journal of Safety Research, 82, 385-391. https://doi.org/https://doi.org/10.1016/j.jsr.2022.07.005
• Lu, D., Guo, F*., & Li, F. (2020). Evaluating the causal effects of cellphone distraction on crash risk using propensity score methods. Accident Analysis & Prevention, 143, 105579. https://doi.org/https://doi.org/10.1016/j.aap.2020.105579
• Guo, F. (2019). Statistical Methods for Naturalistic Driving Studies. Annual Review of Statistics and Its Application, 6(1), 309-328. https://doi.org/10.1146/annurev-statistics-030718-105153
• Guo, F., Kim, I., & Klauer, S. G. (2019). Semiparametric Bayesian models for evaluating time-variant driving risk factors using naturalistic driving data and case-crossover approach. Statistics in Medicine, 38(2), 160-174. https://doi.org/https://doi.org/10.1002/sim.7574
• Atwood, J. R., Guo, F.*, & Blanco, M. (2019). Evaluate driver response to active warning system in level-2 automated vehicles. Accident Analysis & Prevention, 128, 132-138. https://doi.org/https://doi.org/10.1016/j.aap.2019.03.010
• Guo, F., Klauer, S. G., Fang, Y., Hankey, J. M., Antin, J. F., Perez, M. A., Lee, S. E., & Dingus, T. A. (2016). The effects of age on crash risk associated with driver distraction. International Journal of Epidemiology, 46(1), 258-265. https://doi.org/10.1093/ije/dyw234
• Dingus, T. A., Guo, F., Lee, S., Antin, J. F., Perez, M., Buchanan-King, M., & Hankey, J. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences, 113(10), 2636-2641. https://doi.org/doi:10.1073/pnas.1513271113
• Guo, F., Fang, Y., & Antin, J. F. (2015). Older driver fitness-to-drive evaluation using naturalistic driving data. Journal of Safety Research, 54, 49.e29-54. https://doi.org/https://doi.org/10.1016/j.jsr.2015.06.013
• Klauer, S. G. ^·, Guo, F. ^· , Simons-Morton, B. G., Ouimet, M. C., Lee, S. E., & Dingus, T. A. (2014). Distracted Driving and Risk of Road Crashes among Novice and Experienced Drivers. New England Journal of Medicine, 370(1), 54-59. https://doi.org/10.1056/NEJMsa1204142
• Guo, F., Simons-Morton, B. G., Klauer, S. E., Ouimet, M. C., Dingus, T. A., & Lee, S. E. (2013). Variability in Crash and Near-Crash Risk among Novice Teenage Drivers: A Naturalistic Study. The Journal of pediatrics, 163(6), 1670-1676. http://linkinghub.elsevier.com/retrieve/pii/S0022347613009335?showall=true
• Guo, F.*, & Fang, Y. (2013). Individual driver risk assessment using naturalistic driving data. Accident Analysis & Prevention, 61, 3-9. https://doi.org/https://doi.org/10.1016/j.aap.2012.06.014
• Guo, F., Klauer, S. G., Hankey, J. M., & Dingus, T. A. (2010). Near Crashes as Crash Surrogate for Naturalistic Driving Studies. Transportation Research Record, 2147(1), 66-74. https://doi.org/10.3141/2147-09
• Guo, F., Dey, D. K., & Holsinger, K. E. (2009). A Bayesian Hierarchical Model for Analysis of Single-Nucleotide Polymorphisms Diversity in Multilocus, Multipopulation Samples. Journal of the American Statistical Association, 104(485), 142-154. https://doi.org/10.1198/jasa.2009.0010