Regent Retrospect Musekwa | Statistics | Best Researcher Award

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Mr. Regent Retrospect Musekwa | Statistics | Best Researcher Award

Research Assistant, Botswana International University of Science and Technology, Botswana

Musekwa Regent is a passionate and skilled statistician currently pursuing a PhD in Statistics at Botswana International University of Science and Technology (BIUST). With a strong foundation in applied statistics, he has excelled in diverse fields such as finance, environmental science, and education, demonstrating a remarkable ability to convert complex data into actionable insights. 📊✨

Publication Profile

Google Scholar

Education

Musekwa holds an MSc in Statistics from BIUST (2023) and a BSc in Statistics from Midlands State University, Zimbabwe (2020). He is currently working towards his PhD, further enhancing his expertise in statistical theory and applications. 🎓📚

Experience

As a Teaching Assistant at BIUST since August 2021, Musekwa has contributed to various courses including Statistics for Non-Mathematicians and Multivariate Analysis. He also serves as an Examination Administrator, ensuring compliance with examination regulations. Previously, he worked as a Statistician at Simbisa Brands, where he optimized operational efficiency and analyzed customer preferences. 👩‍🏫📈

Research Focus

Musekwa’s research primarily revolves around statistical modeling, data analysis, and the development of new statistical distributions. He is particularly interested in applying innovative techniques to real-world problems, contributing to both theoretical and applied statistics. 🔍📖

Awards and Honors

Throughout his academic career, Musekwa has received recognition for his contributions to statistical research. His ongoing PhD research has garnered attention, and he has co-authored several publications in esteemed journals, showcasing his commitment to advancing statistical knowledge. 🏆📜

Publication Top Notes

  1. Musekwa, R. R., & Makubate, B. (2023). Statistical analysis of Saudi Arabia and UK Covid-19 data using a new generalized distribution. Scientific African, 22, e01958. Link
  2. Nyamajiwa, V. Z, Musekwa, R. R., & Makubate, B. (2024). Application of the New Extended Topp-Leone Distribution to Complete and Censored Data. Revista Colombiana de Estadística, 47. Link
  3. Musekwa, R. R., & Makubate, B. (2024). A flexible generalized XLindley distribution with application to engineering. Scientific African, 24, e02192. Link
  4. Musekwa, R. R., Gabaitiri, L., & Makubate, B. (2024). A new technique of creating families of continuous distributions. Revista Colombiana de Estadística. Link
  5. Makubate, B., & Musekwa, R. R. (2024). A novel technique for generating families of distributions. Statistics, Optimization & Information Computing. Link

Mario Flores | Computational Biology | Next-Generation Science Trailblazer Award

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Assist Prof Dr. Mario Flores | Computational Biology | Next-Generation Science Trailblazer Award

Biomedical, University of Texas at San Antonio, United States

Profile

Google Scholar

Short Bio

Dr. Mario A. Flores is an Assistant Professor at the University of Texas at San Antonio, specializing in artificial intelligence models for disease phenotype predictions, biomarker identification, and explainable mechanisms. His innovative research integrates various AI techniques to enhance our understanding of disease progression, particularly in oncology.

Education

Dr. Flores holds a Bachelor’s degree in Electronics Engineering from the Metropolitan Autonomous University, a Master’s in Applied Mathematics, and a PhD in Electrical Engineering (Computational Biology) from the University of Texas at San Antonio. He completed his postdoctoral fellowship at the National Center for Biotechnology Information (NCBI), NIH.

Experience

Since 2020, Dr. Flores has served as an Assistant Professor with joint appointments in Electrical and Computer Engineering (ECE) and Biomedical Engineering (BME) at UTSA. His prior roles include NIH Postdoctoral Fellow at NCBI and Research Associate at the Greehey Children’s Cancer Research Institute, showcasing his extensive experience in computational biology and bioinformatics.

Research Interests

Dr. Flores’s research focuses on developing AI tools for disease gene dependence prediction, utilizing spatially resolved transcriptomics, single-cell RNA sequencing, and Electronic Health Records (EHRs) to analyze tumor microenvironments. His work aims to bridge gaps in understanding disease mechanisms and improve patient outcomes through precision medicine.

Awards

Dr. Flores has received numerous awards for his research, including funding from the NIH for projects on neural circuits inhibiting pain, and recognition from the AIM-AHEAD Fellowship program, supporting his efforts to address health disparities in minority populations.

Publications Top Notes

Dr. Flores has authored several impactful publications, including:

New tools for spatial biology transcriptomics & proteomics in immuno-oncology, Immuno-Oncology Insights, 2023.

Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis, Brief in Bioinformatics, 2022.

Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions, Cancers, 2022.