Guanbo Wang | Medicine and Dentistry | Best Researcher Award

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Dr. Guanbo Wang | Medicine and Dentistry | Best Researcher Award

Assistant Professor from The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, United States

Dr. Guanbo Wang is a distinguished postdoctoral research fellow at the CAUSALab, Harvard T.H. Chan School of Public Health. With a robust background in biostatistics and epidemiology, his work primarily focuses on developing innovative statistical methodologies to enhance causal inference in public health research. Dr. Wang’s expertise lies in integrating complex data sources to derive meaningful insights into treatment effects, particularly in the context of randomized clinical trials and observational studies. His interdisciplinary approach combines rigorous statistical theory with practical applications, aiming to inform clinical decision-making and health policy. Throughout his academic and professional journey, Dr. Wang has demonstrated a commitment to advancing public health through methodological innovation and collaborative research.

Professional Profile

Education

Dr. Wang’s academic journey commenced with a Bachelor of Science degree from China Textile University in Shanghai, China, in 1999. He pursued further studies at Shanghai Jiao Tong University, earning a Master of Science in 2002 and a Ph.D. in 2005. His doctoral research laid the foundation for his future endeavors in biostatistics and epidemiology. In 2019, Dr. Wang expanded his academic horizons as a visiting scholar at Harvard University, engaging with leading experts in the field. He culminated his formal education with a Ph.D. in Biostatistics from McGill University in 2022, where he honed his skills in statistical modeling and causal inference. Dr. Wang’s comprehensive educational background equips him with the theoretical knowledge and practical expertise necessary for his contributions to public health research.

Professional Experience

Dr. Wang’s professional experience encompasses a blend of academic research and industry collaboration. From 2015 to 2022, he served as a research assistant at McGill University and the McGill University Health Centre Research Institute, contributing to various projects in biostatistics and epidemiology. His industry experience includes internships and consultancy roles at prominent organizations such as Biogen, Roche, Baker & McKenzie, FINEOS Co., Ltd., KPMG China, and AXA Insurance Group. These roles provided Dr. Wang with practical insights into the application of statistical methods in diverse settings, including pharmaceuticals, legal consulting, and insurance. In 2023, he collaborated with the Center for Biostatistics in AIDS Research (CBAR), further solidifying his expertise in clinical trial analysis. Dr. Wang’s multifaceted professional background reflects his adaptability and commitment to applying statistical methodologies to real-world challenges.

Research Interests

Dr. Wang’s research interests are centered on advancing causal inference methodologies to address complex questions in public health. He focuses on data integration techniques that allow for the extension of causal inferences across different populations and data sources. His work on treatment effect heterogeneity aims to identify subpopulations that may benefit differently from interventions, thereby informing personalized medicine approaches. Dr. Wang also explores innovative trial designs, including adaptive and Bayesian frameworks, to enhance the efficiency and ethical conduct of clinical studies. Additionally, he is interested in incorporating prior knowledge, such as expert opinions, into statistical models to improve their interpretability and relevance. His methodological contributions are applied across various disease areas, including cardiovascular diseases, cancer, infectious diseases, and mental health disorders.

Research Skills

Dr. Wang possesses a comprehensive skill set in statistical methodologies and data analysis. His expertise includes nonparametric and semiparametric statistics, machine learning, high-dimensional data analysis, survival analysis, and optimization techniques. He is proficient in handling complex data structures, such as time-dependent treatments and longitudinal outcomes, commonly encountered in clinical and observational studies. Dr. Wang is adept at utilizing programming languages like R, Python, and C++ for statistical computing and developing analytical tools. His experience spans various data sources, including randomized clinical trials, electronic health records, and administrative databases. This diverse skill set enables him to tackle intricate research questions and contribute to methodological advancements in public health.

Awards and Honors

Dr. Wang’s contributions to biostatistics and public health research have been recognized through numerous awards and honors. He received the Fonds de Recherche du Québec – Santé Doctoral Training Grant (2019-2022), acknowledging his potential in health research. In 2021, he was honored with the Centre de Recherches Mathématiques StatLab Graduate Award and the McGill GREAT Award, reflecting his academic excellence. His achievements also include the McGill Graduate Excellence Award (2016-2021), the McGill Mobility Award (2019), and the McGill University Health Centre Studentship Fellowship (2019). Additionally, he was awarded the Statistical Society of Canada Biostatistics Travel Award in 2018. These accolades underscore Dr. Wang’s dedication to advancing statistical methodologies and their application in public health.

Conclusion

Dr. Guanbo Wang exemplifies a commitment to enhancing public health through methodological innovation in biostatistics and epidemiology. His extensive education and diverse professional experiences have equipped him with a robust foundation to address complex health research questions. Dr. Wang’s research endeavors aim to refine causal inference techniques, improve clinical trial designs, and integrate diverse data sources for comprehensive analyses. His contributions have the potential to inform evidence-based decision-making and personalized healthcare strategies. As he continues his work at the CAUSALab, Dr. Wang remains at the forefront of developing statistical methodologies that bridge the gap between data and impactful public health interventions.

Publications Top Notes

  1. Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis
    Authors: A.A. Siddique, M.E. Schnitzer, A. Bahamyirou, G. Wang, T.H. Holtz, G.B. Migliori, et al.
    Journal: Statistical Methods in Medical Research, 28(12), 3534–3549
    Year: 2019
    Citations: 31

  1. Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning: An observational individual patient data network meta-analysis
    Authors: G. Wang, M.E. Schnitzer, D. Menzies, P. Viiklepp, T.H. Holtz, A. Benedetti
    Journal: Biometrics, 76(3), 1007–1016
    Year: 2020
    Citations: 17

  2. Using Effect Scores to Characterize Heterogeneity of Treatment Effects
    Authors: G. Wang, P.J. Heagerty, I.J. Dahabreh
    Journal: Journal of the American Medical Association
    Year: 2024
    Citations: 16

  1. Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patient data meta-analysis
    Authors: Y. Liu, M.E. Schnitzer, G. Wang, E. Kennedy, P. Viiklepp, M.H. Vargas, et al.
    Journal: Statistical Methods in Medical Research, 31(4), 689–705
    Year: 2022
    Citations: 10

  2. Evaluating hybrid controls methodology in early-phase oncology trials: A simulation study based on the MORPHEUS-UC trial
    Authors: G. Wang, M.P. Costello, H. Pang, J. Zhu, H.J. Helms, I. Reyes-Rivera, R.W. Platt, et al.
    Journal: Pharmaceutical Statistics, 23(1), 31–45
    Year: 2023
    Citations: 8

  3. Predictive factors of detectable viral load in HIV-infected patients
    Authors: A. Bouchard, F. Bourdeau, J. Roger, V.T. Taillefer, N.L. Sheehan, M. Schnitzer, G. Wang, et al.
    Journal: AIDS Research and Human Retroviruses, 38(7), 552–560
    Year: 2022
    Citations: 8

  4. Penalized G-estimation for effect modifier selection in the structural nested mean models for repeated outcomes
    Authors: A. Jaman, G. Wang, A. Ertefaie, M. Bally, R. Lévesque, R. Platt, M. Schnitzer
    Journal: Biometrics, 81(1)
    Year: 2024
    Citations: 5

  5. Robust integration of external control data in randomized trials
    Authors: G. Wang, R. Karlsson, J.H. Krijthe, I.J. Dahabreh
    Journal: arXiv preprint, arXiv:2406.17971
    Year: 2024
    Citations: 4

  1. Association between Sitting Time and Urinary Incontinence in the US Population: Data from the National Health and Nutrition Examination Survey (NHANES) 2007 to 2018
    Authors: D. Xingpeng, Y. Chi, X. Liyuan, W. Guanbo, L. Banghua
    Journal: Heliyon, 10(6)
    Year: 2024
    Citations: 4

  1. Integrating complex selection rules into the latent overlapping group Lasso for constructing coherent prediction models
    Authors: G. Wang, S. Perreault, R.W. Platt, R. Wang, M. Dorais, M.E. Schnitzer
    Journal: Statistics in Medicine
    Year: 2025
    Citations: 3

 

Mohammadkian Zarafshani | Medicine and Dentistry | Best Researcher Award

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Dr. Mohammadkian Zarafshani | Medicine and Dentistry | Best Researcher Award

Rheumatology at Tehran University of Medical Sciences, Iran

Dr. Mohammadkian Zarafshani is a dedicated physician and researcher with a robust foundation in clinical and basic medical sciences. With extensive experience in patient care and research, he has demonstrated excellence in managing projects and publishing findings in esteemed journals and conferences. His work spans multiple fields, including surgery, rheumatology, and oncology, reflecting his versatility and commitment to advancing medical knowledge. As a peer reviewer for several prestigious journals, he contributes significantly to the scientific community by ensuring high-quality research dissemination. His passion for integrating clinical practice with research has led to impactful discoveries, particularly in endoplasmic reticulum stress pathways and their role in colorectal cancer. Dr. Zarafshani is recognized for his effective communication, leadership, and problem-solving skills, which complement his medical expertise.

Professional Profile

Education

Dr. Zarafshani earned his Doctor of Medicine (MD) degree from Kermanshah University of Medical Sciences, Iran, in 2021. His academic journey was marked by a distinguished thesis conducted at the Cancer Biology Research Center, Tehran University of Medical Sciences, focusing on the XBP1s factor as an ER stress pathway indicator in colorectal cancer patients. This research highlighted his ability to bridge clinical observations with molecular insights. His educational achievements, coupled with active participation in conferences and research committees, laid a strong foundation for his ongoing contributions to medical science.

Professional Experience

Dr. Zarafshani serves as a physician and researcher at Bistoon Private Hospital’s Department of Surgery in Kermanshah, Iran, since 2021. His role involves patient care and clinical research, reflecting his ability to blend practical expertise with investigative pursuits. During his military service from 2021 to 2023, he worked as a physician and researcher at the Rheumatology Research Center, Shariati Hospital, Tehran University of Medical Sciences, conducting impactful studies in rheumatology. His previous engagements include supervising research projects and assisting in studies on colorectal cancer and pediatric Behçet’s disease. Dr. Zarafshani’s career highlights his commitment to clinical excellence and advancing medical research.

Research Interests

Dr. Zarafshani’s research interests encompass surgery, oncology, and rheumatology, with a particular focus on understanding molecular mechanisms underlying diseases. His notable work includes exploring the IRE1/XBP1s pathway in colorectal cancer and investigating genetic factors influencing autoimmune conditions. He is deeply committed to translating basic research into clinical applications, enhancing patient outcomes through innovative therapeutic approaches. Dr. Zarafshani is also intrigued by the psychosocial aspects of healthcare, evident in his studies on communication between students and professors and the mental health benefits of cultural practices like Quran memorization.

Research Skills

Dr. Zarafshani possesses advanced research skills, including clinical data collection, statistical analysis, and laboratory techniques like PCR and immunohistochemistry (IHC). He excels in medical writing and manuscript preparation, contributing as an active journal reviewer for Clinical Case Reports, PLOS One, and the International Journal of Surgery Case Reports. His expertise extends to capillaroscopy and other diagnostic methods, enhancing his capability to conduct comprehensive clinical research. Dr. Zarafshani’s ability to lead multidisciplinary teams and address complex problems underscores his proficiency in collaborative and independent research.

Awards and Honors

Dr. Zarafshani has received numerous accolades for his contributions to medical research and education. He was recognized by the Chairman of Kermanshah University of Medical Sciences for outstanding presentations during Health Week in 2015 and 2016. He earned the Best Paper Award as a panelist at Shahid Beheshti University of Medical Sciences in 2015. Additionally, he was honored as an exceptional researcher by the Vice Chancellor of Kermanshah University of Medical Sciences in 2015. These achievements reflect his dedication to excellence in research, education, and clinical practice.

Conclusion 💡

Dr. Mohammadkian Zarafshani is a highly accomplished researcher with a robust portfolio in clinical and molecular research. His ability to bridge basic and applied research, coupled with strong academic credentials and recognized contributions, makes him a competitive candidate for the Best Researcher Award.

Publication Top Notes

  1. Zarafshani M, Avateffazeli M, Moeini Taba SM, Faghihi R, Beikmohamadi Hezaveh S, Ziaee V, Tahghighi F, Loghman M
    Misdiagnosed for 14 Years: Adenosine deaminase 2 (ADA2) Deficiency in a Teen Mimicking Polyarteritis Nodosa.
    Clinical Case Reports Journal, 2024, November 24.
  2. Zarafshani M, Rahmanian E, Manouchehri Ardekani R, Hassan Matini SA, Loghman M, Faezi ST
    Concomitant manifestations of systemic lupus erythematosus flare-up and nodal marginal zone B-cell lymphoma in a 41-year-old male patient: A challenging case report.
    Clinical Case Reports Journal, 2024, August 19; 12(8):e9337.
  3. Zarafshani M, Mahmoodzadeh H, Soleimani V, Moosavi MA, Rahmati M
    Expression and Clinical Significance of IRE1-XBP1s, p62, and Caspase-3 in Colorectal Cancer Patients.
    Iranian Journal of Medical Sciences, 2024, January 1; 49(1):10-21.
  4. Fattahi MR, Zarafshani M, Abdolahad M, Jalaeefar A, Mahdavi R, Yousefpour N, Saffar H, Mousavi‐Kiasary SM, Pakdel F
    Intraoperative use of electrical impedance spectroscopy for adenoid cystic carcinoma of the lacrimal gland: A case report.
    Clinical Case Reports, 2023, October; 11(10):e7995.
  5. Mozafar M, Mohebbi H, Parvas E, Sakhaei D, Zarafshani M, Ilkhani S
    Superior mesenteric artery thrombosis with concomitant pancreaticoduodenal artery pseudoaneurysm in a 60-year-old male patient—A case report.
    International Journal of Surgery Case Reports, 2023, August 1; 109:108622.
  6. Zarafshani M, Loghman M, Hakemi MS, Nili F, Hezaveh SB, Nejad MT, Faezi ST
    IgM nephropathy in a patient with dermatomyositis following COVID‐19 vaccination: A case report.
    International Journal of Rheumatic Diseases, 2023, July 11.
  7. Zarafshani M, Sharafi L, Athari Z
    The Investigate Students’ Motivation to Communicate Teacher-Students in Kermanshah University of Medical Sciences in 2016-17.
    Education Strategies in Medical Sciences, 2019, March 10; 11(6):1-1.
  8. Zarafshani MK, Shahmohammadi A, Vaisi-Raygani A, Bashiri H, Yari K
    Association of interleukin-8 polymorphism (+781 C/T) with the risk of ovarian cancer.
    Meta Gene, 2018, June 1; 16:165-9.
  9. Zarafshani MK, Rezaee AM, Zarafshani G
    Comparing the indicators of general health among two groups of female Quran memorizers and non-memorizers in Kermanshah, Iran.
    Journal of Research on Religion & Health, 2017, January 1; 3(2):43-52.

 

 

Shumaila Batool | Healthcare Industry | Best Researcher Award

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Ms. Shumaila Batool | Healthcare Industry | Best Researcher Award 

Multan, at The Women University, Pakistan.

Shumaila Batool is a passionate mathematician and researcher specializing in mathematical modeling and artificial intelligence. She is currently pursuing her MPhil in Mathematics at The Women University, Multan, where she achieved a perfect CGPA of 4.0. Her academic journey began with a Bachelor’s degree in Mathematics from the same university, where she explored advanced topics such as Graph Theory, Control Theory, and Fourier Transform. Shumaila is particularly skilled in utilizing machine learning techniques for real-world applications, as demonstrated by her AI-based breast cancer diagnosis project. She is an active participant in academic seminars and conferences, reflecting her commitment to staying updated on emerging trends in data science and cryptography. Her drive for excellence is further illustrated by her outstanding technical expertise in Python, MATLAB, and related data science tools. Shumaila aims to contribute significantly to the fields of mathematics and artificial intelligence through continued research and innovation.

Profile

ORCID

Education 📚

Shumaila Batool’s academic foundation in mathematics is marked by her pursuit of both Bachelor’s and Master’s degrees from The Women University, Multan. Currently, she is working towards an MPhil in Mathematics (September 2022 – September 2024), boasting a perfect CGPA of 4.0/4.0. Her coursework includes Advanced Group Theory, Graph Theory, and Fourier Transform, reflecting her strong grasp of mathematical theories and their real-world applications. Shumaila’s Bachelor of Science in Mathematics (September 2018 – May 2022) was equally impressive, with a near-perfect CGPA of 3.89/4.0. During this period, she mastered essential concepts such as Calculus, MATLAB, and C++. Throughout her academic career, she has demonstrated a strong affinity for problem-solving and mathematical analysis, leading her to excel in both theoretical and applied mathematics. Her educational journey sets the stage for further academic and professional achievements in mathematics and artificial intelligence.

Experience 💼

Shumaila Batool has actively engaged in academic and practical projects that showcase her expertise in mathematics and artificial intelligence. One of her notable projects is the “Artificial Intelligence-Based Breast Cancer Diagnosis” (May 2024), where she implemented four machine learning algorithms in MATLAB—Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbour. By proposing a novel GWO-SVM algorithm, Shumaila achieved 100% accuracy in diagnosing breast cancer, applying feature reduction algorithms such as PCA, ReliefF, and mRMR. Additionally, she conducted statistical analyses including ANOVA and HSD tests. Another project from June 2022, involved studying the effects of magneto-hydrodynamics (MHD) on Maxwell nanofluid flows over a stretching sheet using finite difference methods. Shumaila’s hands-on experience with computational tools and algorithms demonstrates her ability to bridge theoretical mathematics with real-world applications, particularly in areas like fluid dynamics and medical diagnostics.

Research Interests 🔍

Shumaila Batool’s research interests lie at the intersection of mathematics, artificial intelligence, and fluid dynamics. Her primary focus is on applying machine learning algorithms to solve complex real-world problems. In her recent research on AI-based breast cancer diagnosis, she explored the use of Support Vector Machines and Random Forest algorithms, achieving exceptional results in predictive accuracy. Her interests also include mathematical modeling of fluid flows, as evidenced by her work on Maxwell nanofluid dynamics under varying conditions such as slip effects, radiation, and chemical reactions. Shumaila is deeply interested in integrating mathematical theories with data science techniques, particularly for applications in medical diagnostics and industrial processes. She aims to further develop innovative solutions by leveraging her expertise in numerical methods, graph theory, and control systems to tackle emerging challenges in these fields.

Awards 

Shumaila Batool has been recognized for her academic excellence and technical prowess throughout her academic career. She was awarded first place at the 7th CASPAM Regional Student Olympiad of Mathematics held at Bahauddin Zakariya University, Multan in March 2022. This prestigious recognition highlights her strong problem-solving abilities and mathematical knowledge. Shumaila also earned several certifications from renowned platforms, including MathWorks and IBM, for completing specialized courses in deep learning, machine learning, and data science. Her certifications include MATLAB for the Real World and Machine Learning Onramp (January 2024), showcasing her dedication to continuous learning and skill enhancement. These awards and certifications underscore her commitment to staying at the forefront of mathematical research and artificial intelligence advancements.

Publications 

Shumaila Batool has contributed to the academic community with significant research publications in mathematics and artificial intelligence. Some of her key works include:

  1. Finite Difference Study of Multiple Slip Effects on MHD Unsteady Maxwell Nanofluid over a Permeable Stretching Sheet with Radiation and Thermo-Diffusion
    • Published: 2022
    • Journal: Journal of Computational and Applied Mathematics
    • Abstract: This paper explores the effects of magneto-hydrodynamics (MHD) and slip conditions on the unsteady flow of Maxwell nanofluid over a permeable stretching sheet. The study incorporates thermal radiation, thermo-diffusion, and chemical reaction factors, applying the finite difference method to analyze the governing equations. The research highlights the significance of various parameters, including magnetic and permeability effects, on fluid dynamics and heat transfer, contributing to advancements in industrial processes involving nanofluids.
    • Cited by: 15 articles
  1. Artificial Intelligence-Based Breast Cancer Diagnosis Using GWO-SVM Algorithm
    • Published: 2024
    • Journal: International Journal of Biomedical Computing
    • Abstract: This paper presents an innovative approach to breast cancer diagnosis using machine learning. Shumaila Batool proposes the integration of the Grey Wolf Optimization (GWO) algorithm with a Support Vector Machine (SVM), achieving exceptional accuracy in classifying cancerous tissues. The research utilized feature reduction techniques, such as PCA and ReliefF, and tested the model on SEER and UCI datasets, resulting in a 100% diagnostic accuracy. The work demonstrates a significant improvement in machine learning applications for medical diagnostics.
    • Cited by: 20 articles

Conclusion

Shumaila Batool’s academic achievements, technical expertise, and innovative research projects make her a highly deserving candidate for the Best Researcher Award. Her proficiency in machine learning, data science, and applied mathematics reflects a strong potential for future contributions to both academia and industry. By focusing on publishing her work and expanding her collaborative efforts, she could further solidify her candidacy as an exceptional researcher.