Marco Cirillo | Interactive Analytics | Best Researcher Award

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Dr. Marco Cirillo | Interactive Analytics | Best Researcher Award

Non-Profit Polyambulance Foundation | Italy

Dr. Marco Cirillo is a distinguished cardiac surgeon whose career seamlessly bridges clinical excellence and experimental research. After completing classical studies, he graduated in Medicine and Surgery with highest honors  from the University of Bologna and specialized in Cardiac and Great Vessels Surgery. His career has encompassed leadership roles as Head of the Cardiac Surgery Unit in Bologna for six years and the Heart Failure Surgery Unit in Brescia for twelve years. With expertise spanning the full range of traditional cardiac surgery-including mitral repair, coronary bypass, valve replacement, and aortic arch procedures-Dr. Cirillo has performed over 8,000 surgeries as first surgeon. His innovative contributions include the KISS procedure for physiological left ventricular reconstruction, the “Arterial-source No-touch Aorta” technique for off-pump coronary revascularization, and the NINFEA method for annular stabilization in endocarditis. Renowned for his commitment to quality and risk management (JCI accreditation), he continues to advance the field through research on bioprostheses and ventricular assist systems, recognized internationally through publications and awards. In 2025, he earned a Master’s degree in Echocardiography from the University of Verona. Beyond medicine, his intellectual pursuits extend to writing, photography, and cosmology, where he recently published a paper exploring Darwinian natural evolution as applied to the development of the Universe.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Achilles and the Tortoise: Rethinking Evidence Generation in Cardiovascular Surgery and Interventional Cardiology", Marco Cirillo, Hearts, 2025.

"Abiotrophia defectiva and Granulicatella: A Literature Review on Prosthetic Joint Infection and a Case Report on defectiva PJI and Concurrent Native Valve Endocarditis", MCristina Seguiti; Edda Piacentini; Angelica Fraghì; Mattia Zappa; Elia Croce; Angelo Meloni; Marco Cirillo; Clarissa Ferrari; Chiara Zani; David Belli et al., Microorganisms, 2025.

"Finger ischemia in a young lady: an unusual presentation of papillary fibroelastoma with intraventricular location", Matteo Pernigo; Elisabetta Dinatolo; Marco Cirillo; Zean Mhagna; Alida Filippini; Fabiana Cozza; Marco Berti; Roberto Bazzani; Tony Sabatini; Claudio Cuccia et al., Monaldi Archives for Chest Disease, 2023.

"Exploring Personal Protection During High-Risk PCI in a COVID-19 Patient", Marco Cirillo, JACC: Case Reports, 2020.

Farhad Hossain Sojib | Data Science | Best Researcher Award

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Mr. Farhad Hossain Sojib | Data Science | Best Researcher Award 

University of Hull | Bangladesh

Mr. Farhad Hossain Sojib is an engineer with a strong foundation in electronics and communication engineering and a growing specialization in data science and artificial intelligence. He is currently pursuing his M.Sc. in Artificial Intelligence and Data Science at the University of Hull, United Kingdom, following the completion of his B.Sc. in Engineering from Hajee Mohammad Danesh Science and Technology University, Bangladesh, where he conducted notable research on explainable AI in educational data mining and machine learning applications in 5G antenna optimization. With professional experience as an IELTS Instructor at Lexicon Plus, he has trained over 50 students, developed course materials, and mentored junior instructors. His leadership and organizational skills were further demonstrated through his role as a Program Committee Member at the IEEE Student Branch, HSTU, where he managed events, seminars, and competitions. Additionally, his internship at BRACNet Limited provided hands-on experience in ISP operations, ICT technologies, and server management. Farhad combines his technical expertise, research acumen, and collaborative mindset to contribute meaningfully to the fields of machine learning and data-driven innovation.

Profiles: Orcid 

Featured Publications

"The integration of explainable AI in Educational Data Mining for student academic performance prediction and support systems", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman, Telematics and Informatics Reports, 2025.

"A Bioinformatics Approach to Uncover Hub Genes and Potential Drug Targets of Stroke, Heart-Disease, Hyperglycemia, and Hypertension", Md. Emran Biswas; M D. Fazle Hasan Mihad; Farhad Hossain Sojib; Mohammad Jubair Ahmmed; M D Galib Hasan; Md. Jobare Hossain; Md. Abul Basar; Md. Mehedi Islam; Md. Delowar Hossain; Md. Selim Hossain et al., 27th International Conference on Computer and Information Technology (ICCIT), 2024.

"An Explainable Educational Data Mining System for Predicting Student Academic Performance", Md. Mahmudul Islam; Farhad Hossain Sojib; Md. Fazle Hasan Mihad; Mahmudul Hasan; Mahfujur Rahman; FARHAD HOSSAIN SOJIB, 2024 IEEE International Conference on Signal Processing, Information, Communication and Systems, 2024.

Iliyas Karim Khan | Statistics | Best Researcher Award

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Mr. Iliyas Karim Khan | Statistics | Best Researcher Award

Teaching Assistance at Universiti Teknologi Petronas Malaysia, Malaysia📖

Iliyas Karim Khan is a dedicated researcher and educator with a strong background in statistics and data science. He is currently pursuing his Ph.D. at Universiti Teknologi PETRONAS, Malaysia, focusing on advanced statistical modeling and machine learning applications. With extensive teaching experience spanning over 8 years in various academic institutions, he has contributed significantly to the field through research and publications. His work primarily revolves around clustering algorithms, data analysis, and predictive modeling.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Statistics (2024), Universiti Teknologi PETRONAS, Malaysia
  • M.Phil. in Statistics (2016), Peshawar University, KPK, Pakistan
  • M.Sc. in Statistics (2014), Peshawar University, KPK, Pakistan
  • B.Sc. in Statistics (2012), SBBU Sheringhal, Upper Dir, Pakistan
  • B.Ed. (2015), SBBU Sheringhal, Upper Dir, Pakistan
  • F.Sc. in Engineering (2010), BISE Peshawar, Pakistan
  • S.S.C. in Science (2008), BISE KPK, Peshawar, Pakistan

Professional Experience🌱

Iliyas has accumulated diverse teaching and research experience in both national and international institutions. He has served as a lecturer and subject specialist at GHSS Bang Chitral, Pakistan, and Abbottabad University of Science and Technology, contributing to curriculum development and student mentorship. Additionally, he has gained international teaching experience as a Teaching Assistant at Universiti Teknologi PETRONAS, Malaysia. His professional expertise extends to statistical analysis, machine learning, and forecasting, with hands-on experience in tools such as Python, SPSS, and Minitab

Research Interests🔬
  • Machine Learning
  • Statistical Modeling
  • Forecasting
  • Big Data Analysis
  • Cluster Optimization Algorithms

Author Metrics

Iliyas has published several high-impact journal articles in Q1 journals, including Egyptian Informatics Journal and AIMS Mathematics, with notable contributions to the advancement of clustering algorithms and data science techniques. His research work has garnered significant recognition within the academic community.

Awards and Honors
  • Publication Recognition Achievement 2024, Universiti Teknologi PETRONAS, Malaysia
  • Acknowledged for outstanding contributions to statistical analysis and machine learning applications
Publications Top Notes 📄

1. Determining the Optimal Number of Clusters by Enhanced Gap Statistic in K-mean Algorithm

  • Authors: I.K. Khan, H.B. Daud, N.B. Zainuddin, R. Sokkalingam, M. Farooq, M.E. Baig, et al.
  • Journal: Egyptian Informatics Journal
  • Volume: 27, Article 100504
  • Year: 2024
  • Citations: 3
  • Abstract: This study introduces an enhanced gap statistic method to determine the optimal number of clusters in the K-means clustering algorithm. The approach addresses common challenges in cluster analysis, improving the reliability and efficiency of the algorithm.
  • Impact: Provides an effective method to enhance clustering performance in various data-driven applications.

2. 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, I.K. Khan
  • Journal: Journal of Advanced Research in Numerical Heat Transfer
  • Volume: 20, Issue 1, Pages 23-35
  • Year: 2024
  • Citations: 2
  • Abstract: The paper presents a numerical solution to the heat equation using a modified cubic B-spline collocation method. The proposed method enhances accuracy and computational efficiency compared to conventional techniques.
  • Impact: Contributes to the advancement of numerical modeling in heat transfer applications.

3. 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
  • Journal: AIMS Mathematics
  • Volume: 9, Pages 25070-25097
  • Year: 2024
  • Citations: 2
  • Abstract: This paper critically examines the limitations of the K-means clustering algorithm, proposing novel solutions to handle outliers, non-spherical data, and optimal cluster determination.
  • Impact: Enhances the applicability of clustering techniques in complex real-world datasets.

4. Numerical Solution by Kernelized Rank Order Distance (KROD) for Non-Spherical Data Conversion to Spherical Data

  • Authors: I.K. Khan, H.B. Daud, R. Sokkalingam, N.B. Zainuddin, A. Abdussamad, et al.
  • Journal: AIP Conference Proceedings
  • Volume: 3123, Issue 1
  • Year: 2024
  • Citations: 1
  • Abstract: The study introduces the Kernelized Rank Order Distance (KROD) method to convert non-spherical data to spherical data, improving the performance of traditional clustering algorithms.
  • Impact: Provides a novel solution for handling data distribution challenges in clustering applications.

5. 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, et al.
  • Journal: Journal of Advanced Research in Micro and Nano Engineering
  • Volume: 26, Issue 1, Pages 83-101
  • Year: 2024
  • Abstract: This review paper explores recent advancements in oil recovery techniques using nanotechnology, emphasizing the influence of geometries on the efficiency of nanoflooding processes.
  • Impact: Provides critical insights for improving oil recovery processes using nanomaterials.

Conclusion

Iliyas Karim Khan is a highly deserving candidate for the Best Researcher Award due to his impressive academic credentials, impactful research contributions, and dedication to the field of statistics and data science. His work on clustering algorithms and machine learning applications offers innovative solutions to critical challenges in data analysis.

To further strengthen his profile, he should focus on expanding his research network, leading high-value projects, and enhancing his presence in industry-oriented applications. With continued efforts, Iliyas is poised to make even greater contributions to the field and emerge as a thought leader in statistical modeling and data science.