Alabi Banjoko | Statistics | Best Researcher Award

Dr. Alabi Banjoko | Statistics | Best Researcher Award

ย Seniorย Lecturerย at University of Ilorin, Nigeria

Dr. Alabi Waheed Banjoko is a Postdoctoral Research Fellow at the Africa Health Research Institute (AHRI), South Africa, specializing in Human Leukocyte Antigen (HLA) research. He holds a PhD in Statistics from the University of Ilorin, Nigeria, where he developed a hybrid multi-objective optimization method for genomic studies. With over a decade of experience as a lecturer and researcher, he has supervised 42 BSc and 4 MSc students. His expertise spans biostatistics, machine learning, and data analysis. Proficient in SPSS, R, and Python, he actively contributes to academia through publications and mentorship. ๐ŸŒ๐Ÿ“‘๐Ÿ”ฌ

Publication Profile

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Expertise in Statistics & Research ๐Ÿ”ฌ

Dr. Alabi Banjoko is a Postdoctoral Research Fellow at the Africa Health Research Institute (AHRI), South Africa, specializing in Human Leukocyte Antigen (HLA) research. From 2012 to 2023, he served as a Lecturer in the Department of Statistics at the University of Ilorin, Nigeria, contributing to teaching, research, and community service. His earlier experience includes roles as a Mathematics Teacher (NYSC) in Enugu (2010โ€“2011), an Industrial Trainee at the National Population Commission, Lagos (2007), and participation in the Student Industrial Work Experience Scheme (2005) at the Ministry of Economic Planning and Budget, Lagos. His expertise spans statistical analysis, data collection, and research methodologies. ๐Ÿ“ˆ๐ŸŽ“๐Ÿ“‘

Academic Excellence in Statistics ๐Ÿ“Š

Dr. Alabi Banjoko holds a PhD in Statistics from the University of Ilorin, Nigeria (2019), where he developed a Hybrid Multi-Objective Sequential-Based Optimization Method for genomic study feature selection and classification. He earned his M.Sc. in Statistics (2013) with a dissertation on Taguchiโ€™s loss function approach and a First-Class B.Sc. in Statistics (2010). His academic journey began with an ND in Statistics (2007) from Federal Polytechnic, Ede, where he analyzed death rates by gender. Currently, he is a Postdoctoral Research Fellow at AHRI, South Africa, advancing research in biostatistics and statistical modeling. ๐Ÿ“ˆ๐Ÿ“š๐Ÿ”ฌ

Recognized for Excellence in Statistics ๐Ÿ“Š

Dr. Alabi Banjoko has received multiple awards for his outstanding contributions to statistics and research. In 2013, he was honored with the University of Ilorin Award for his Ph.D. program in Statistics ($1,500). In 2019, the Federal Government of Nigeria awarded him a learned conference grant to Pahang, Malaysia ($4,500). He received the Professional Statistician Society of Nigeria Conference Grant in 2022 ($25). In 2023, his expertise was further recognized with the International Biometry Society Travel Award Fund to attend the IBS SUSAN conference ($1,250). ๐Ÿ…๐Ÿ“ˆ๐ŸŽ“

Research Focus Areas ๐Ÿ“Š

Dr. Alabi Banjoko specializes in statistical modeling, machine learning, bioinformatics, and computational statistics. His research spans cancer classification, genomic data analysis, support vector machines (SVMs), and data mining in medical diagnostics. He has contributed significantly to feature selection algorithms, econometric modeling, and Bayesian methods. His work also extends to survival analysis, multivariate regression, and optimization techniques. With impactful studies on heart disease prediction, cancer risk assessment, and foreign aid economics, he blends mathematics, statistics, and health analytics to drive data-driven decision-making. ๐Ÿ“ˆ๐Ÿงฌ๐Ÿ’ก

Publication Top Notes

  • Efficient Support Vector Machine Classification of Diffuse Large B-Cell Lymphoma and Follicular Lymphoma mRNA Tissue Samples ๐Ÿ“Š๐Ÿงฌ โ€“ Cited by: 15 โ€“ Year: 2015
  • IMPROVED BAYESIAN FEATURE SELECTION AND CLASSIFICATION METHODS USING BOOTSTRAP PRIOR TECHNIQUES ๐Ÿ”ข๐Ÿ“Š โ€“ Cited by: 13 โ€“ Year: 2016
  • Weighted support vector machine algorithm for efficient classification and prediction of binary response data โš–๏ธ๐Ÿ“ˆ โ€“ Cited by: 12 โ€“ Year: 2019
  • SURVIVAL ANALYSIS WITH MULTIVARIATE ADAPTIVE REGRESSION SPLINES USING COX-SNELL RESIDUAL ๐Ÿฅ๐Ÿ“‰ โ€“ Cited by: 10 โ€“ Year: 2015
  • On the Use of Linear Programming Model Approach in Profit Optimization of a Product Mix Company ๐Ÿ’ฐ๐Ÿ“Š โ€“ Cited by: 6 โ€“ Year: 2020
  • Multiclass response feature selection and cancer tumour classification with support vector machine ๐Ÿฉบ๐Ÿงฌ โ€“ Cited by: 6 โ€“ Year: 2019
  • Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test โค๏ธ๐Ÿ“Š โ€“ Cited by: 5 โ€“ Year: 2021
  • On seemingly unrelated regression and single equation estimators under heteroscedastic error and non-Gaussian responses ๐Ÿ“Š๐Ÿงฎ โ€“ Cited by: 4 โ€“ Year: 2020
  • Econometric Analysis of the Effects of Land Size on Cereals Production in Nigeria ๐ŸŒพ๐Ÿ“‰ โ€“ Cited by: 4 โ€“ Year: 2020
  • The nexus between foreign aid and foreign direct investment in Nigeria: simultaneous equations approach ๐ŸŒ๐Ÿ’ฐ โ€“ Cited by: 3 โ€“ Year: 2023
  • Data Mining Genome-Based Algorithm for Optimal Gene Selection and Prediction of Colorectal Carcinoma ๐Ÿงฌ๐Ÿ“Š โ€“ Cited by: 3 โ€“ Year: 2020
  • Sequential optimization-based feature selection algorithm for efficient cancer classification and prediction ๐Ÿฅโš™๏ธ โ€“ Cited by: 3 โ€“ Year: 2018
  • Genetic diagnosis, classification, and risk prediction in cancer using next-generation sequencing in oncology ๐Ÿงฌ๐Ÿ”ฌ โ€“ Cited by: 2 โ€“ Year: 2024
  • A Test Procedure for Ordered Hypothesis of Population Proportions Against a Control ๐Ÿ“Š๐Ÿ“ โ€“ Cited by: 2 โ€“ Year: 2016
  • Modelling determinants of antenatal care services utilization in Nigeria ๐Ÿคฐ๐Ÿ“ˆ โ€“ Cited by: 1 โ€“ Year: 2022

 

Hao Zhang | Biomaterials | Best Researcher Award

Dr. Hao Zhang | Biomaterials | Best Researcher Award

Senior Researcher at EVLiXiR Biotechnology Co., Ltd., China

Dr. Hao Zhang is a researcher at the Nanjing Bell Mountain Institute of Molecular Medicine & EVLiXiR, specializing in biomaterials, proteomics, and drug delivery. He holds a Ph.D. in Biological Science & Medical Engineering from Southeast University (2019โ€“2024), an MPhil from Nanjing University, and a B.Sc. from Nanjing Forestry University. With prior experience as Director of R&D at Skyraying Biotechnology and R&D Manager at Genscript & Genloci, his expertise lies in high-throughput extracellular vesicle (EV) extraction, peptidomics, and proteomics. His work focuses on mass spectrometry-based biomarker discovery and engineering EVs for targeted drug delivery. ๐Ÿ“ง scottzhang09@gmail.com | ORCID: 0000-0001-9744-1707.

 

Publication Profile

Orcid

๐ŸŽ“ Education Background

Dr. Hao Zhang has a strong academic foundation in biological sciences and medical engineering. He is currently pursuing a Ph.D. (2019โ€“2024) at the School of Biological Science and Medical Engineering, Southeast University, China, under the guidance of Prof. Weiguo Andy Tao. He previously earned an MPhil (2007โ€“2010) from the School of Life Sciences, Nanjing University, mentored by Prof. Chenyu Zhang and A/Prof. Chen Xu. His academic journey began with a B.Sc. (2003โ€“2007) from the School of Forest Resources and Environment, Nanjing Forestry University, where he was guided by Prof. Yulong Ding. ๐ŸŽ“๐Ÿ”ฌ

 

๐Ÿ’ผ Professional Experience

Dr. Hao Zhang has extensive experience in molecular medicine and biotechnology. Since October 2018, he has been a Researcher at the Nanjing Bell Mountain Institute of Molecular Medicine & EVLiXiR, focusing on biomaterials and drug delivery. Previously, he served as Director of R&D at Skyraying Biotechnology Co., Ltd. (2015โ€“2018), where he led innovative research projects. From 2010 to 2015, he worked as an R&D Manager at Genscript & Genloci, gaining expertise in proteomics and extracellular vesicle engineering. His career reflects a strong commitment to advancing biomedical research and therapeutic applications. ๐Ÿงฌ๐Ÿ”ฌ

 

๐Ÿ”ฌ Research Interests

Dr. Hao Zhang’s research focuses on biomaterials, proteomics, and drug delivery. He specializes in high-efficiency and high-throughput extraction of extracellular vesicles (EVs) from body fluids, advancing clinical diagnostics. His expertise extends to peptidomics, proteomics, and modified proteomics technologies, utilizing mass spectrometry to identify EV biomarkers for disease detection. Additionally, he works on engineering extracellular vesicles as targeted drug delivery systems, leveraging scaffold proteins for precision medicine. His contributions enhance therapeutic applications and biomedical innovations, bridging fundamental research with clinical advancements. ๐Ÿงฌ๐Ÿ’Š

 

Publication Top Notes

  • ๐Ÿ“„ High-Efficiency Capture and Proteomic Analysis of Plasma-Derived Extracellular Vesicles through Affinity Purification | Analytical Chemistry | 2025 | DOI: 10.1021/acs.analchem.4c04269
  • ๐Ÿงฌ Transcriptional and Post-Transcriptional Regulation of CARMN in Cervical Cancer | Journal of Experimental & Clinical Cancer Research | 2024 | DOI: 10.1186/s13046-024-03229-y
  • ๐Ÿท Label-Free MRM-MS for Quantifying Phosphopeptides from Extracellular Vesicles | Analytical Chemistry | 2024 | DOI: 10.1021/acs.analchem.4c03492
  • ๐Ÿ’Š Chondrocyte-Targeted EV Delivery for Osteoarthritis Therapy | Advanced Healthcare Materials | 2024 | DOI: 10.1002/adhm.202303510
  • ๐Ÿงช Urine Sample Collection & Processing for EV-Based Proteomics | The Analyst | 2024 | DOI: 10.1039/d4an00296b
  • ๐Ÿ”ฌ Surface Functionalization of Exosomes for siRNA Delivery & Cartilage Regeneration | Journal of Controlled Release | 2024 | DOI: 10.1016/j.jconrel.2024.04.009
  • โšก Targeted Phosphoproteomics of Human Saliva EVs via MRMยณ | Analytical Chemistry | 2024 | DOI: 10.1021/acs.analchem.3c04464
  • ๐Ÿฅ DIA Phosphoproteomics of Urinary EVs for RCC Grade Differentiation | Molecular & Cellular Proteomics | 2023 | DOI: 10.1016/j.mcpro.2023.100536
  • ๐Ÿ”ฌ UPLC-MS/MS for Steroids & Glucocorticoids in Human Urine | Analytical Letters | 2023 | DOI: 10.1080/00032719.2022.2092124
  • ๐Ÿงฌ Automated EV Extractor for Phosphoproteomics Analysis | Science Discovery | 2022 | DOI: 10.11648/j.sd.20221002.15