Louis Hickman

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Assistant Professor, Industrial-Organizational Psychology, Department of Psychology
Office Hours
By appointment
Office Address
119 Williams Hall
Short Bio
My research focuses on the intersection of technology and work, with an emphasis on applications of machine learning and artificial intelligence to organizational science and practice (e.g., automatically scored interviews). More broadly, I use computers to measure verbal, paraverbal, and nonverbal behaviors in order to advance our understanding of how interpersonal perceptions form and how cultural, racial, and gender biases function. My current research projects include: understanding how first impressions form in professional settings, mitigating algorithmic bias, understanding how biases influence hiring decisions, and using machine learning and artificial intelligence to help individuals with personal and professional development. In my research, I collaborate with scholars in psychology, management, information sciences, and computer science.

I am accepting graduate student applications for the Fall 2025 semester. Feel welcome to reach out to me via email if you are interested in applying and want to learn more about me, my lab, and/or Virginia Tech's I-O program.
  • Personnel assessment
  • Interpersonal perceptions and biases
  • Fairness, bias, diversity, equity, and inclusion (including algorithmic bias)
  • Research methods (machine learning, artificial intelligence, and natural language processing)
  • Remote work

Select Publications
  • Zhang, N., Wang, M., Xu, H., Koenig, N., Hickman, L., Kuruzovich, J., Ng, V., Arhin, K., Wilson, D., Song, Q. C., Tang, C., Alexander III, Leo., & Kim, Y. (2023). Reducing subgroup differences in personnel selection through the application of machine learning. Personnel Psychology, 76(4), 1125-1159. *First author of Paper 2, fourth author of Paper 3 (manuscript has three papers). https://doi.org/10.1111/peps.12593
  • Lira, B., Gardner, M., Quirk, A., Stone, C., Rao, A., Ungar, L., Hutt, S., Hickman, L., D'Mello, S. K., & Duckworth, A. L. (2023). Using artificial intelligence to assess personal qualities in college admissions. Science Advances, 9(41), eadg9405.
  • Hickman, L., Herde, C. N., Lievens, F., & Tay, L. (2023). Automatic scoring of speeded interpersonal assessment center exercises via machine learning: Initial psychometric evidence and practical guidelines. International Journal of Selection and Assessment, 31(2), 225-239. https://doi.org/10.1111/ijsa.12418
  • Hickman, L., Bosch, N., Ng, V., Saef, R., Tay, L., & Woo, S. E. (2022). Automated video interview personality assessments: Reliability, validity, and generalizability investigations. Journal of Applied Psychology, 107(8), 1323–1351. https://doi.org/10.1037/apl0000695
  • Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146. https://doi.org/10.1177/1094428120971683
  • Hickman, L., Song, Q. C., & Woo, S. E. (2022). Evaluating data. In K. Murphy (Ed.), Data, Methods, and Theory in Organizational Sciences. SIOP Organizational Frontiers Series.
  • Hickman, L., Saef, R., Ng, V., Tay, L., Woo, S. E., & Bosch, N. (2021). Developing and evaluating language-based machine learning algorithms for inferring applicant personality in video interviews. Human Resource Management Journal, early online access. https://doi.org/10.1111/1748-8583.12356
  • Tay, L., Woo, S. E., Hickman, L., Booth, B. M., & D’Mello, S. (2021). A conceptual framework for investigating machine learning measurement bias. Advances in Methods and Practices in Psychological Science, early online access. https://doi.org/10.1177/25152459211061337
  • Booth, B. M., Hickman, L., Subburaj, S. K., Rao, A., Tay, L., Woo, S. E., & D’Mello, S. K. (2021). Identifying and addressing latent bias in affective computing: Integrating approaches across disciplines and a case study of gender bias in automated video interview scoring. IEEE Signal Processing Magazine, 38(6), 84-95. https://doi.org/10.1109/MSP.2021.3106615
  • Booth, B. M., Hickman, L., Subburaj, S. K., Tay, L., Woo, S. E., & D’Mello, S. K. (2021). Bias and fairness in multimodal machine learning: A case study of automated video interviews. Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI ’21). https://doi.org/10.1145/3462244.3479897
  • Saha, K., Yousuf, A., Hickman, L., Gupta, P., Tay, L., & De Choudhury, M. (2021). A social media study on demographic differences in perceived job satisfaction. Proceedings of the ACM: Human Computer Interaction, 5(CSCW1), 1-29. https://doi.org/10.1145/3449241
  • Tay, L., Woo, S. E., Hickman, L., & Saef, R. (2020). Psychometric and validity issues in machine learning approaches to personality assessment: A focus on social media text mining. European Journal of Personality, 34(5), 826-844. https://doi.org/10.1002/per.2290
  • Hickman, L., Tay, L., & Woo, S. E. (2019). Validity investigation of off-the-shelf language-based personality assessment using video interviews: Convergent and discriminant relationships with self and observer ratings. Personnel Assessment and Decisions, 5(3), 12-20. https://doi.org/10.25035/pad.2019.03.003
  • Assistant Professor, Industrial-Organizational Psychology, Virginia Tech (2022-current)
  • Senior Fellow, Wharton People Analytics, University of Pennsylvania (2022-current)
  • Postdoctoral Researcher, Wharton People Analytics, University of Pennsylvania (2021-2022)
  • Research Fellow, ghSMART (2021-current)
  • Ph.D., Industrial-Organizational Psychology, Purdue University (2021)
  • M.S., Computer and Information Technology specializing in Natural Language Processing, Purdue University (2015)
  • B.A., Creative Writing and English, Purdue University (2009)