I’m a statistician working to develop and apply statistical methods to accelerate the translational impact of biomedical data. I became interested in this work through collaborations with researchers with overlapping projects. I have more than 10 years of statistical research and consulting experience in pediatric biomedical research areas. I’ve provided statistical support for numerous observational and intervention studies and have a history of successful collaboration with both clinical and basic science investigators.
My methodology and collaborative research areas are statistical genetics, design and analysis of pediatric population pharmacokinetic studies, cancer prevention behaviors and sexually transmitted infections in adolescents, HPV vaccination and its impact on the epidemiology of HPV and pain management in pediatric populations.
Some of my groundbreaking discoveries include the following:
Studying different measures of marker informativeness for ancestry and admixture mapping
Rank-based analysis of multiple genome-wide association studies (GWAS) of childhood asthma among human populations
Identifying the association between African ancestry and cluster-based childhood asthma subphenotypes
I’m honored to have received many awards, including:
Young Investigator Award (co-author, oral presentation), SPA/AAP Pediatric Anesthesiology meeting Phoenix, AZ, (2018)
First place winner (co-author), International Conference of PeriAnesthesia Nurses (ICPAN), Luna Park, Sydney, Australia (2017)
First prize winner (co-author) of the American Academy of Pediatrics John J. Downes Resident Research Award, Society for Pediatric Anesthesia/American Academy of Pediatrics (SPA/AAP) Pediatric Anesthesiology (2013)
First place Abstract Award (co-author) International Assembly for Pediatric Anesthesia, Washington DC (2012)
PhD: University of Cincinnati, OH.
MS: University of Cincinnati, OH.
BS: Changchun University of Science and Technology, Jilin, China.
Bayesian statistics; statistical genetics; population PK/PD study design and modeling
Biostatistics and Epidemiology
Temporal Trends in Public Stroke Knowledge, 1995-2021. Stroke. 2023; 54:3169-3172.
Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods. Scientific Reports. 2023; 13:11279.
Neonatal AVPR1a Methylation and In-Utero Exposure to Maternal Smoking. Toxics. 2023; 11:855.
Dataset used to refine a treatment protocol of a biofeedback-based virtual reality intervention for pain and anxiety in children and adolescents undergoing surgery. Data in Brief. 2023; 49:109331.
Multilevel Analysis of Racial and Ethnic Disparities in COVID-19 Hospitalization among Children with Allergies. Annals of the American Thoracic Society. 2023; 20:843-853.
Feasibility and acceptability of virtual parental presence on induction of anesthesia-A pilot study. Pediatric Anesthesia. 2023; 33:398-399.
Retrospective study comparing outcomes of multimodal epidural and erector spinae catheter pain protocols after pectus surgery. Journal of Pediatric Surgery. 2023; 58:397-404.
143. Association Between Number of Human Papillomavirus (HPV) Vaccine Doses and Detection of Vaccine-type HPV and Non-vaccine-type HPV Genetically Related to HPV16 and HPV18 Among Vaccinated Adolescent and Young Adult Women and Men in a Real-world Setting. Journal of Adolescent Health. 2023; 72:s81-s82.
Abstract 71: Temporal Trends In 30-day And 5-year Stroke Case Fatality Rates. Stroke. 2023; 54:a71.
Abstract WMP107: Temporal Trends In Public Awareness Of Stroke: 1996-2021. Stroke. 2023; 54:awmp107.