Beata Naumnik | Data Science | Best Researcher Award

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Prof. Beata Naumnik | Data Science | Best Researcher Award

Medical University of Bialystok, Poland

Prof. Beata Naumnik is a highly accomplished nephrologist and transplant specialist whose career reflects exceptional contributions to medical science, research, and education. As Head of the Department of Nephrology and Transplantation with Dialysis Unit at the Medical University of Bialystok, she has advanced pioneering studies in chronic kidney disease, dialysis, and post-transplantation management. She has authored over 200 publications and book chapters, with her research presented at leading international congresses including ASN, ERA-EDTA, and ISN. Her leadership extends beyond academia as an organizer of major scientific conferences, editorial board member, mentor for Ph.D. scholars, and active participant in national and international medical societies. Through her dedication to research excellence, patient care, and global collaboration, Prof. Naumnik continues to play a transformative role in shaping the future of nephrology.

Professional Profile

Scopus Profile | ORCID Profile 

Education

Prof. Beata Naumnik pursued her medical studies at the Faculty of Medicine, Medical University of Bialystok, graduating with honors and later completing her Ph.D. in medical sciences at the same institution. She specialized in internal medicine, nephrology, and clinical transplantology, establishing strong academic and clinical expertise. Her habilitation and professorship further consolidated her academic standing, enabling her to take leadership roles in both teaching and research. Over the years, she has combined academic excellence with professional training, equipping herself with deep insights into nephrology and transplantation medicine. As an educator, she has lectured across multiple faculties, supervised doctoral and specialist training programs, and contributed to coursebooks. Her academic foundation is marked by dedication, innovation, and excellence, reflecting her commitment to advancing nephrology, transplantation, and patient-centered medical education.

Experience

Prof. Beata Naumnik’s professional journey reflects a rich blend of clinical practice, academic leadership, and organizational achievements. She began her career in nephrology practice and steadily advanced into academic positions, eventually becoming Head of the 1st Department of Nephrology and Transplantation with Dialysis Unit at the Medical University of Bialystok. Her teaching experience spans medical, dentistry, dietetics, and pharmaceutical faculties, where she prepared examination sessions, coordinated curricula, and supervised specialist training. She has also played significant roles in state examination commissions, quality of education committees, and university governance boards. In parallel, she has organized numerous national and international conferences, chaired scientific committees, and developed specialized dialysis monitoring systems. Through these roles, she has demonstrated exceptional experience in research, teaching, clinical practice, and organizational leadership within the medical and academic community.

Research Interest

Prof. Beata Naumnik’s research interests are deeply rooted in nephrology, chronic kidney disease, dialysis, and transplantation medicine. Her work explores endothelial dysfunction in chronic kidney disease, mineral bone disorders in renal failure, peritoneal dialysis monitoring, and post-transplantation complications such as viral infections. She has investigated the effects of heparins on endothelial markers, coagulation pathways, and growth factors, linking them to cardiovascular complications in renal patients. A major part of her research has focused on donor-specific antibodies and their impact on transplantation outcomes, advancing clinical approaches to long-term graft survival. Her dedication to innovative methodologies has enabled better diagnostic and therapeutic strategies for kidney patients. By bridging clinical care with scientific discovery, she has established herself as a leading voice in advancing nephrology and improving patient outcomes globally.

Awards and Honors

Prof. Beata Naumnik’s remarkable contributions have been recognized through numerous awards and honors in academia and medical research. She has received prestigious scientific awards for her groundbreaking studies on endothelial dysfunction and heparin effects in renal failure, as well as multiple academic and didactic excellence awards from her university. Her recognition extends internationally, with prizes for outstanding oral presentations at major nephrology congresses. Additionally, she has been consistently acknowledged for her collaborative projects, mentorship, and contributions to advancing medical education. Her leadership roles within professional societies and her influence on nephrology practice have further reinforced her reputation as a distinguished researcher and educator. These honors underscore her sustained commitment to research innovation, clinical excellence, and knowledge dissemination, reflecting her profound impact on nephrology and medical sciences worldwide.

Research Skill

Prof. Beata Naumnik possesses exceptional research skills spanning experimental design, clinical trial leadership, and international collaboration. She has successfully led multiple research grants and projects investigating dialysis, transplantation, and vascular complications, often partnering with leading European and Asian research institutions. With more than 200 publications in high-impact journals, she demonstrates expertise in scientific writing, data analysis, and translational research. She has served as reviewer for internationally recognized journals including the Journal of the American Society of Nephrology and Nephrology Dialysis Transplantation, reflecting her critical scientific evaluation skills. Her editorial roles in nephrology-focused journals highlight her leadership in shaping research directions. Additionally, her ability to mentor doctoral students and specialists demonstrates her capacity to transfer research skills to future generations, ensuring long-term contributions to nephrology and medical science.

Publication Top Notes

  • Title: iFGF23:cFGF23 Ratio Is a Questionable Diagnostic Marker for Distinguishing Between Acute and Chronic Kidney Disease
    Authors: Joanna Szczykowska-Miller; Tomasz Hryszko; Ewa Koc-Żórawska; Beata Naumnik
    Year: 2025
    Citation: International Journal of Molecular Sciences, 26(16), 7952. DOI: 10.3390/ijms26167952

  • Title: C-terminal and intact FGF23 in critical illness and their associations with acute kidney injury and in-hospital mortality
    Authors: K. Rygasiewicz; T. Hryszko; A. Siemiatkowski; S. Brzosko; A. Rydzewska-Rosolowska; B. Naumnik
    Year: 2018
    Citation: Cytokine, 111: 44–50. DOI: 10.1016/j.cyto.2017.12.024

  • Title: Age and gender predict OPG level and OPG/sRANKL ratio in maintenance hemodialysis patients
    Authors: Beata Naumnik; K. Klejna; E. Koc-Zórawska; M. Myśliwiec
    Year: 2013
    Citation: Advances in Medical Sciences, 58(2): 292–297. DOI: 10.2478/ams-2013-0026

  • Title: Comparison of Post-Transplantation Lymphoproliferative Disorder Risk and Prognostic Factors between Kidney and Liver Transplant Recipients
    Authors: Krzysztof Mucha; Rafał Staros; Bartosz Foroncewicz; Bogna Ziarkiewicz-Wróblewska; Maciej Kosieradzki; Sławomir Nazarewski; Beata Naumnik; Joanna Raszeja-Wyszomirska; Krzysztof Zieniewicz; Leszek Pączek
    Year: 2022
    Citation: Cancers, 14(8): 1953. DOI: 10.3390/cancers14081953

  • Title: Clinicopathologic correlations of renal pathology in the adult population of Poland
    Authors: A. Perkowska-Ptasinska; A. Bartczak; M. Wagrowska-Danilewicz; A. Halon; K. Okon; A. Wozniak; M. Danilewicz; H. Karkoszka; A. Marszałek; J. Kowalewska; B. Naumnik et al.
    Year: 2017
    Citation: Nephrology Dialysis Transplantation, 32(suppl_2): ii192–ii200. DOI: 10.1093/ndt/gfw365

  • Title: Factors Influencing Longevity of Humoral Response to SARS-CoV-2 Vaccination in Patients with End Stage Kidney Disease Receiving Renal Replacement Therapy
    Authors: Irena Glowinska; Barbara Labij-Reduta; Jerzy Juzwiuk; Magdalena Lukaszewicz; Adam Pietruczuk; Agata Poplawska; Anna Daniluk-Jamro; Katarzyna Kakareko; Alicja Rydzewska-Rosolowska; Beata Naumnik et al.
    Year: 2022
    Citation: Journal of Clinical Medicine, 11(17): 4984. DOI: 10.3390/jcm11174984

  • Title: Prognostic value of osteoprotegerin and sRANKL in bronchoalveolar lavage fluid of patients with advanced non-small cell lung cancer
    Authors: W. Naumnik; I. Płońska; M. Ossolińska; J. Nikliński; B. Naumnik
    Year: 2018
    Citation: Advances in Experimental Medicine and Biology, vol 1021: 47–56. DOI: 10.1007/5584_2017_111

  • Title: The effect of nephrectomy on Klotho, FGF-23 and bone metabolism
    Authors: Katarzyna Kakareko; Alicja Rydzewska-Rosolowska; S. Brzosko; J. Gozdzikiewicz-Lapinska; E. Koc-Zorawska; P. Samocik; R. Kozlowski; M. Mysliwiec; B. Naumnik; T. Hryszko
    Year: 2017
    Citation: International Urology and Nephrology, 49(9): 1653–1661. DOI: 10.1007/s11255-017-1519-9

  • Title: Unexpected and striking effect of heparin-free dialysis on cytokine release
    Authors: A. Rydzewska-Rosolowska; J. Gozdzikiewicz-Lapinska; J. Borawski; E. Koc-Zorawska; M. Mysliwiec; B. Naumnik
    Year: 2017
    Citation: International Urology and Nephrology, 49(6): 1117–1125. DOI: 10.1007/s11255-017-1589-8

  • Title: Different effects of enoxaparin, nadroparin, and dalteparin on plasma TFPI during hemodialysis: A prospective crossover randomized study
    Authors: Beata Naumnik; Alicja Rydzewska-Rosolowska; M. Myśliwiec
    Year: 2011
    Citation: Clinical and Applied Thrombosis/Hemostasis, 17(6): 612–618. DOI: 10.1177/1076029610376936

Conclusion

Prof. Beata Naumnik’s career stands as a model of excellence in medical research, education, and leadership. Her education, extensive experience, and pioneering research have advanced the understanding of kidney disease, dialysis, and transplantation, making a lasting impact on patient care and scientific development. Recognized with national and international honors, she has consistently demonstrated her ability to translate research into meaningful clinical outcomes. Her strong record of publications, editorial contributions, and leadership within professional societies underscores her influence on the global nephrology community. With a proven history of innovation, collaboration, and mentorship, she exemplifies the qualities of a world-class researcher. Prof. Naumnik’s ongoing dedication ensures her continued contributions to scientific progress, medical education, and patient well-being, making her a truly deserving candidate for recognition at the highest level.

 

Iliyas Khan | Statistics | Best Researcher Award

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Dr. Iliyas Khan | Statistics | Best Researcher Award

Teaching Assistance at Universiti Teknologi PETRONAS, Malaysia

Dr. Iliyas Karim Khan is a dedicated researcher specializing in machine learning, statistical modeling, forecasting, and big data analysis. He holds a Ph.D. from Universiti Teknologi PETRONAS (UTP), Malaysia, along with advanced degrees in statistics from Peshawar University, Pakistan. With a strong publication record in Q1 journals, his research focuses on optimizing clustering algorithms, particularly in K-means clustering and data science applications. His expertise extends to programming and statistical tools such as Python, SPSS, Stata, and EViews. Dr. Khan has gained extensive teaching experience, serving as a Teaching Assistant at UTP and a Subject Specialist in Pakistan. He was recognized with a Publication Recognition Achievement (2024) at UTP, underscoring his research contributions. His work is instrumental in improving data-driven decision-making and computational efficiency, making him a valuable asset in the academic and research community. His commitment to innovation and education positions him as a promising leader in his field.

Professional Profile 

Google Scholar
Scopus Profile

Education

Dr. Iliyas Karim Khan has a strong academic background in statistics and data science. He earned his Ph.D. in 2024 from Universiti Teknologi PETRONAS (UTP), Malaysia, focusing on optimizing machine learning algorithms. Prior to that, he completed an M.Phil. (2016) and M.Sc. (2014) in Statistics from Peshawar University, Pakistan, and a B.Sc. in Statistics (2012) from SBBU Sheringhal, Upper Dir, Pakistan. His foundational studies in science and engineering were completed at BISE Peshawar, along with a B.Ed. (2015) for pedagogical training. His educational journey reflects his commitment to advanced research in big data analysis, forecasting, and statistical modeling. Throughout his studies, he has demonstrated exceptional analytical skills, contributing to high-impact research in data clustering and computational efficiency. His academic achievements have positioned him as a promising researcher in the field of machine learning and statistical analytics, making significant contributions to data science methodologies.

Professional Experience

Dr. Iliyas Karim Khan has gained extensive teaching and research experience across multiple institutions. He worked as a Teaching Assistant at Universiti Teknologi PETRONAS (UTP), Malaysia, where he was actively involved in data science research and statistical analysis. In Pakistan, he served as a Subject Specialist at GHSS Bang Chitral for four years, strengthening his expertise in applied statistics and data analysis. His teaching career also includes one year at Abbottabad University of Science and Technology, where he mentored students in statistical modeling and machine learning techniques. Additionally, he completed an internship at the University of Peshawar and taught at Darban Degree College, Chitral. His experience encompasses curriculum development, student mentoring, and advanced research in statistical computing, making him an accomplished educator and researcher. His hands-on expertise in Python, SPSS, Stata, and EViews further enhances his ability to train future data scientists and statisticians.

Research Interest

Dr. Iliyas Karim Khan’s research revolves around machine learning, big data analytics, statistical modeling, and forecasting. His primary focus is on clustering algorithms, particularly enhancing the K-means clustering method for improved efficiency and accuracy. His work addresses key challenges in data science, such as non-spherical data, outlier handling, and optimal cluster selection, which have significant implications for AI-driven decision-making. Additionally, he explores computational time optimization and statistical accuracy in clustering techniques, contributing to the advancement of data-driven methodologies. His studies extend to forecasting models, applied statistical techniques, and real-world big data applications. Through numerous Q1 journal publications, he has proposed innovative solutions for refining data science models. His research is highly relevant in industries reliant on machine learning, artificial intelligence, and predictive analytics, positioning him as a key contributor to the evolution of data-centric technologies and computational intelligence.

Awards and Honors

Dr. Iliyas Karim Khan’s academic excellence and research contributions have been recognized through prestigious accolades. In 2024, he received the “Publication Recognition Achievement” award from Universiti Teknologi PETRONAS (UTP), Malaysia, honoring his impactful research in machine learning and statistical analysis. His Q1 journal publications in high-impact journals demonstrate his ability to produce innovative and widely acknowledged research. Additionally, his contributions to data clustering and computational efficiency have been well-received within the academic and research communities. His work on addressing K-means clustering limitations and enhancing algorithmic efficiency has been cited extensively, highlighting its significance in data science and artificial intelligence. His growing recognition in machine learning and big data analytics marks him as a promising scholar in his field. Through his research, he continues to make valuable contributions that shape the future of data-driven decision-making and statistical computing.

Conclusion

Dr. Iliyas Karim Khan is an exceptional researcher, educator, and innovator in the field of machine learning, statistical modeling, and big data analysis. His strong academic background, extensive teaching experience, and high-impact research make him a significant contributor to computational intelligence and data science advancements. His studies on enhancing K-means clustering efficiency, outlier handling, and computational time optimization have added valuable insights to the field. Through prestigious publications and academic honors, he has demonstrated his expertise in refining statistical methodologies for real-world applications. His proficiency in Python, SPSS, Stata, and advanced statistical techniques enables him to bridge the gap between theoretical advancements and practical applications in data science. As he continues to explore new frontiers in machine learning and predictive analytics, his work is expected to have a lasting impact on data-driven industries and AI-based decision-making systems.

Publications Top Noted

  • Title: Determining the Optimal Number of Clusters by Enhanced Gap Statistic in K-Mean Algorithm
    Authors: Iliyas Karim Khan, HB Daud, NB Zainuddin, R Sokkalingam, M Farooq, ME Baig, …
    Year: 2024
    Citations: 7
    Source: Egyptian Informatics Journal, Volume 27, Article 100504

  • Title: Numerical Solution of Heat Equation Using Modified Cubic B-Spline Collocation Method
    Authors: M Iqbal, N Zainuddin, H Daud, R Kanan, R Jusoh, A Ullah, Iliyas Karim Khan
    Year: 2024
    Citations: 3
    Source: Journal of Advanced Research in Numerical Heat Transfer, Volume 20, Pages 23-35

  • Title: Numerical Solution by Kernelized Rank Order Distance (KROD) for Non-Spherical Data Conversion to Spherical Data
    Authors: Iliyas Karim Khan, HB Daud, R Sokkalingam, NB Zainuddin, A Abdussamad, …
    Year: 2024
    Citations: 2
    Source: AIP Conference Proceedings, Volume 3123 (1)

  • Title: A Modified Basis of Cubic B-Spline with Free Parameter for Linear Second Order Boundary Value Problems: Application to Engineering Problems
    Authors: M Iqbal, N Zainuddin, H Daud, R Kanan, H Soomro, R Jusoh, A Ullah, …
    Year: 2024
    Citations: 1
    Source: Journal of King Saud University-Science, Volume 36 (9), Article 103397

  • Title: Standardizing Reference Data in Gap Statistic for Selection of Optimal Number of Clusters in K-Means Algorithm
    Authors: Iliyas Karim Khan, H Daud, N Zainuddin, R Sokkalingam
    Year: 2025
    Source: Alexandria Engineering Journal, Volume 118, Pages 246-260

  • Title: A Hybrid Stacked Sparse Autoencoder (HSSAE) Model for Predicting Type 2 Diabetes
    Authors: A Abdussamad, H Daud, R Sokkalingam, M Zubair, Iliyas Karim Khan, Z Mahmood
    Year: 2025
    Source: To be published

  • Title: A Mini Review of the State-of-the-Art Development in Oil Recovery Under the Influence of Geometries in Nanoflood
    Authors: M Zafar, H Sakidin, A Hussain, M Sheremet, I Dzulkarnain, R Safdar, …
    Year: 2024
    Source: Journal of Advanced Research in Micro and Nano Engineering, Volume 26 (1), Pages 83-101

  • Title: Exploring K-Means Clustering Efficiency: Accuracy and Computational Time Across Multiple Datasets
    Authors: Iliyas Karim Khan, H Daud, N Zainuddin, R Sokkalingam, A Abdussamad, AS Azad, …
    Year: 2024
    Source: Journal of Advanced Research in Applied Sciences and Engineering Technology

  • Title: Forecasting the Southeast Asian Currencies Against the British Pound Sterling Using Probability Distributions
    Authors: Iliyas Karim Khan, Ahmad Abubakar Suleiman, Hanita Daud, Mahmod Othman, Abdullah …
    Year: 2023
    Source: Data Science Insights, Volume 1 (1), Pages 31-51

  • Title: Addressing Limitations of the K-Means Clustering Algorithm: Outliers, Non-Spherical Data, and Optimal Cluster Selection
    Authors: Iliyas Karim Khan, Abdussamad, Abdul Museeb, Inayat Agha
    Year: 2024
    Citations: 4
    Source: AIMS Mathematics, Volume 9, Pages 25070-25097