Dr. Qing Li
Diversity and Determination: Professor Li’s Impact in STEM
Professor Li, an accomplished academic in the field of electrical engineering and industrial engineering. Professor Li’s journey in STEM began in China, where she pursued her undergraduate studies in electrical engineering. Seeking further opportunities, she moved to the United States and earned her master’s degree in electrical engineering from the University of Rochester. However, her passion for statistics led her to switch her focus to a PhD in statistics at Virginia Tech.
Professor Li’s journey in academia serves as a testament to resilience and determination, illuminating the challenges and triumphs faced by women in STEM fields. After completing her doctorate, she navigated through various academic roles, including a visiting assistant professorship at the University of Wisconsin-Madison’s Department of Statistics, before finding her home at Iowa State University’s Industrial Engineering Department in 2018.
Reflecting on her undergraduate years, Professor Li candidly shared the hurdles she encountered as a female student in a predominantly male field. Initially the only woman in her cohort, she grappled with isolation and struggled with hands-on experiments. However, through perseverance and resourcefulness, she found solidarity among fellow female students and formed study groups to enhance her learning experience. Her journey underscores the importance of seeking support and camaraderie in overcoming obstacles and achieving success in academia.
Professor Li also sheds light on the gender representation challenges in engineering, particularly the declining ratio of female students as one progresses to higher education and faculty positions. She attributes this trend to a variety of factors, including differences in interests and family responsibilities. Drawing from her personal experience of balancing career aspirations with family obligations, she emphasizes the realities that impact the representation of women in STEM fields.
In her current research pursuits, Professor Li focuses on quality-related studies in advanced manufacturing and healthcare data analytics. Through her work, she aims to improve manufacturing processes, product quality, and healthcare outcomes, demonstrating her commitment to making tangible contributions to society.
For aspiring Asian women in STEM, Professor Li offers valuable advice: “Do not give up.” Drawing from her own journey, she highlights the importance of perseverance, even in the face of adversity. Despite encountering doubts and difficulties along the way, the support from friends, family, and mentors served as pillars of strength, propelling her forward. Professor Li’s story serves as an inspiring example of the transformative power of determination and resilience in achieving one’s goals, making her a beacon of hope for aspiring women in STEM.
In conclusion, Professor Li’s journey in STEM is a testament to the power of determination and the importance of mentorship in overcoming challenges. As a female academic in traditionally male-dominated fields, she has faced isolation and gender differences, but her unwavering perseverance and support from mentors have propelled her forward. For a more comprehensive insight into Professor Li’s inspiring journey, we invite you to watch the full interview provided above.
Professor Li’s Profile
Professor Li, an accomplished academic in the field of electrical engineering and industrial engineering. Professor Li’s journey in STEM began in China, where she pursued her undergraduate studies in electrical engineering. Seeking further opportunities, she moved to the United States and earned her master’s degree in electrical engineering from the University of Rochester. However, her passion for statistics led her to switch her focus to a PhD in statistics at Virginia Tech.
Education
- Ph.D. Statistics, Virginia Tech, 2015
- M.S. Electrical Engineering, University of Rochester, 2010
- B.E. Information and Electronics Engineering, Tsinghua University, 2008
Publications
Safaei, N., Seyedekrami, S., Talafidaryani, M., Masoud, A., Wang, S. D., Moqri, M., Li, Q., and Zhang, W. L. (2022). E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262895, PLOS ONE, 17(5): e0262895.
Li, Q.*, Liu, L. J., Li, T. Q., and Yao, K. H. (2021). Bayesian change-points detection assuming power-law process in the recurrent-event context, Communications in Statistics Part B: Simulation and Computation, https://www.tandfonline.com/doi/full/10.1080/03610918.2021.2006711. 1–23
Jiang, Y. Q., Wang, S. D., Qin, H. T., Li, B. W., and Li, Q.*. Similarity quantification of 3D surface topography measurements via Fourier transform, Measurement,110207. https://www.sciencedirect.com/science/article/abs/pii/S0263224121011179?via%3Dihub
Wang, S.D., Zhang, X., Zheng, Y., Li, B.W., Qin, H.T., and Li, Q.* (2019). Similarity evaluation of 3D surface topography measurements, Measurement Science and Technology, 32:125003. https://iopscience.iop.org/article/10.1088/1361-6501/ac1b41
Jiang, Y. Q., Li, Q.*, Trevisan, G, Linhares, D., and MacKenzie, C. (2021). Investigating the relationship of porcine reproductive and respiratory syndrome virus RNA detection between adult/sow farm and wean-to-market age categories, PLOS ONE, 16:e0253429. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253429