Baosheng Liang | Biostatistics | Best Researcher Award

Assist. Prof. Dr. Baosheng Liang, Peking University, China.

Dr. Baosheng Liang is an Assistant Professor in the Department of Biostatistics at Peking University, specializing in biostatistics, survival analysis, and causal inference. With a Ph.D. in Probability Theory & Mathematical Statistics from Beijing Normal University, he has held key research roles, including a postdoctoral position at The University of Hong Kong. His expertise lies in developing robust statistical models and innovative data analysis techniques. Dr. Liang actively contributes to recurrent-event data analysis, meta-analysis, and Bayesian regression research, advancing statistical methodologies in healthcare and medical sciences.

Publivation Profiles

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Education and Experience

Education:

Ph.D. in Probability Theory & Mathematical Statistics, Beijing Normal University (2012-2016)
M.S. in Probability Theory & Mathematical Statistics, Beijing Normal University (2009-2012)
B.S. in Mathematics & Applied Mathematics, Qingdao University (2005-2009)

Experience:

Assistant Professor, Peking University (2018–Present)
Postdoctoral Researcher, The University of Hong Kong (2016–2018)
Joint Training Ph.D., University of North Carolina at Chapel Hill (2013–2014)

Suitability summary

Dr. Baosheng Liang, an Assistant Professor in the Department of Biostatistics at Peking University, is a distinguished researcher in the field of biostatistics. With an extensive background in probability theory, statistical learning, and causal inference, he has made significant contributions to advancing statistical methodologies for biomedical and clinical research. His academic journey spans prestigious institutions such as the University of North Carolina at Chapel Hill, the University of Hong Kong, and Beijing Normal University, where he honed his expertise in survival analysis, semiparametric models, and robust statistical methods. His research has significantly enhanced the understanding of recurrent-event data analysis and meta-analysis techniques, making him a strong candidate for the Best Researcher Award.

Professional Development

Dr. Liang has significantly contributed to biostatistical research, focusing on survival analysis, semiparametric models, and causal inference. His work extends to recurrent-event data analysis, robust statistical methods, and machine learning applications. Through collaborative projects and academic publications, he has enhanced statistical modeling for healthcare and epidemiology. His methodological advancements in incomplete data analysis and subgroup analysis provide deeper insights into public health and medical research. As an academic mentor and researcher, Dr. Liang continually refines statistical learning techniques, ensuring their adaptability in real-world applications.

Research Focus

Dr. Liang’s research primarily revolves around biostatistics, exploring advanced methodologies to analyze complex medical data. His expertise includes survival analysis, where he develops statistical models for patient survival trends, and semiparametric modeling, which balances flexibility and interpretability in data analysis. He also specializes in causal inference, crucial for determining cause-and-effect relationships in medical studies. His work in robust statistics enhances data reliability, while his contributions to meta-analysis and Bayesian regression improve predictive modeling in biostatistics. His research continuously drives innovation in healthcare data science.

Awards And Honours

Outstanding Young Researcher Award – Peking University
Best Paper Award – International Biostatistics Conference
Excellence in Teaching Award – Peking University
Research Grant Recipient – National Natural Science Foundation of China
Invited Speaker – Leading Biostatistics and Causal Inference Conferences

Publication Top Noted

Clinical association between plan complexity and the local-recurrence-free-survival of non-small-cell lung cancer patients receiving stereotactic body radiation therapy (2024) – Physica Medica
Efficacy of high-dose-rate brachytherapy with different radiation source activities among cervical cancer patients and risk factors for long-term outcomes: A 6-year retrospective study (2024) – Brachytherapy
PM2.5 constituents associated with childhood obesity and larger BMI growth trajectory: A 14-year longitudinal study (2024) – Environment International
Stereotactic ablative brachytherapy with or without assistance of 3D-printing templates for inoperable locally recurrent or oligometastatic soft-tissue sarcoma: a multicenter real-world study (2023) – American Journal of Cancer Research
Impact of Comorbidity on the Duration from Symptom Onset to Death in Patients with Coronavirus Disease 2019: A Retrospective Study of 104,753 Cases in Pakistan (2023) – Diseases
The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model (2023) – Diagnostics
The association between exposure to PM2.5 components from coal combustion and mortality in female breast cancer patients (2023) – Environmental Research Letters
An Improved Dunnett’s Procedure for Comparing Multiple Treatments with a Control in the Presence of Missing Observations (2023) – Mathematics
Assessing the Risk of APOE-ϵ4 on Alzheimer’s Disease Using Bayesian Additive Regression Trees (2023) – Mathematics
Variable selection for mixed panel count data under the proportional mean model (2023) – Statistical Methods in Medical Research
Diagnosis of malignant pleural effusion with combinations of multiple tumor markers: A comparison study of five machine learning models (2023) – The International Journal of Biological Markers
Prognostic Factors Analysis of Metastatic Recurrence in Cervical Carcinoma Patients Treated with Definitive Radiotherapy: A Retrospective Study Using Mixture Cure Model (2023) – Cancers
Biomarker Alteration after Neoadjuvant Endocrine Therapy or Chemotherapy in Estrogen Receptor-Positive Breast Cancer (2022) – Life