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

Orcid

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

Isabelle Clerc-Urmès | Statistical | Best Researcher Award

Dr. Isabelle Clerc-Urmès , Institut national de recherche et de sécurité, France.

Isabelle Clerc-Urmès, Ph.D. , is a skilled statistician and educator specializing in advanced statistical modeling and health behavior analysis. A graduate of Aix-Marseille University, she has honed expertise in multilevel models, survival analysis, and econometrics. With extensive experience in biostatistics at CHRU Nancy and INRS, Isabelle also contributes as a lecturer, guiding Master’s students in quantitative research. Her academic and professional journey reflects her commitment to applying robust statistical methods to public health, epidemiology, and occupational sciences. Isabelle’s passion for education and research underscores her influential role in both academia and applied biostatistics.

Publication profile

Education

2011: Ph.D. in Economic Sciences, Aix-Marseille University, with honors.
2007: Master in Applied Mathematics, Biostatistics, University Henri Poincaré.
2005: Bachelor’s in Mathematics, University Henri Poincaré.
2004: DEUG MIAS, University Henri Poincaré.

Experience

2022-Present: Lecturer in Statistics, Master’s in Biomechanics.
2021-Present: Statistical Studies Officer, INRS.
2014-2021: Biostatistician, CHRU Nancy, co-developing statistical tools and mentoring.
2013-2014: INSERM Researcher on survival estimation methods.
2008-2011: Lecturer, Aix-Marseille University, teaching mathematics and econometrics.

Suitability For The Award

Dr. Isabelle Clerc-Urmès demonstrates exceptional expertise in biostatistics and health economics, with extensive experience in research, teaching, and professional practice. Her multidisciplinary background integrates advanced statistical modeling with health behavior analysis, making her a distinguished candidate for the Best Researcher Award.

Professional Development

Isabelle Clerc-Urmès excels in applying advanced statistical techniques to real-world challenges . She has extensive experience teaching statistics and econometrics at various academic institutions, including Aix-Marseille University and the School of Public Health in Nancy . Her collaboration with INRS and CHRU Nancy strengthened her expertise in survival analysis, multilevel models, and econometrics . Isabelle actively mentors students, promoting quantitative research . Proficient in software like R, STATA, and SAS, she develops innovative solutions, including statistical packages . Her commitment to applying rigorous analytical methods contributes significantly to public health and occupational safety fields.

Research Focus

Isabelle’s research focuses on applying statistical and econometric models to public health, epidemiology, and occupational health . Her expertise lies in multilevel modeling, survival analysis, and econometric methods, addressing challenges like healthcare access disparities, disease surveillance, and treatment adherence . She has contributed to developing innovative tools for data analysis, enhancing predictive modeling capabilities. Isabelle’s interdisciplinary approach integrates advanced statistical methods with applied health research . Her projects, from modeling antiretroviral treatment compliance to analyzing health behaviors, aim to improve public health policies and interventions, fostering equitable healthcare systems globally .

Awards and Honors

2014: Qualification for Maître de Conférences by the French National University Council (CNU).
2011: Doctoral Thesis with honors and jury commendations at Aix-Marseille University.
2020: Official tenure as Hospital Biostatistician, CHRU Nancy.
Developed statistical tools and packages recognized for innovation in R and STATA.
Contributed to impactful epidemiological and public health studies at INSERM and INRS.

Publications

“From unknown to familiar: An exploratory longitudinal field study on occupational exoskeletons adoption”
“A New Approach to Prevent Injuries Related to Manual Handling of Carts: Correcting Resistive Forces between Floors and Wheels to Evaluate the Maximal Load Capacity”
“The Adoption of Occupational Exoskeletons: From Acceptability to Situated Acceptance, Questionnaire Surveys”
“Predictive factors of return-to-work trajectory after work-related rotator cuff syndrome: A prospective study of 96 workers”
“Arterial Cannulation Simulation Training in Novice Ultrasound Users”
“A Magnetic Resonance Imaging Index to Predict Crohn’s Disease Postoperative Recurrence: The MONITOR Index”
“Training novice in ultrasound-guided venipuncture: A randomized controlled trial comparing out-of-plane needle-guided versus free-hand ultrasound techniques on a simulator”
“Incidence of and Risk Factors for Colorectal Strictures in Ulcerative Colitis: A Multicenter Study”
“Risk of thrombosis, pregnancy morbidity or death in antiphospholipid antibodies positive patients with or without thrombocytopenia”