Sogand Dehghan | Social Network Analysis | Best Researcher Award

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Ms. Sogand Dehghan | Social Network Analysis | Best Researcher Award

University Lecturer | K. N. Toosi University of Technology | Iran

Ms. Sogand Dehghan is a dedicated Data Analyst and IT Specialist with strong expertise in data mining, machine learning, text mining, social network analysis, and software development. She specializes in collecting, cleaning, and analyzing large-scale datasets to create insightful reports and dashboards that support organizational decision-making. Her work bridges academia and industry, with contributions spanning research, teaching, and practical implementation of data-driven solutions. She is particularly passionate about analyzing social media networks to align with organizational objectives and developing innovative tools that integrate analytics with business strategies.

Professional Profile

Scopus | Orcid | Googlescholar

Education

Ms. Dehghan earned her Master’s degree in Information Technology from K. N. Toosi University of Technology, where she conducted advanced research on social media user evaluation using machine learning techniques. She also holds a Bachelor’s degree in Information Technology from Payame Noor University. Her academic training has equipped her with a strong foundation in data visualization, machine learning, text mining, and database management, supported by proficiency in multiple programming and analytical tools.

Experience

Ms. Dehghan has served in both academic and industrial roles. In academia, she has worked as a Lecturer at the National Skills University, teaching advanced programming and mentoring students in data analytics. In the industrial sector, she has held positions as Data Analyst and Software Developer at GAM Arak Industry and Kherad Sanat Arvand, where she utilized tools such as Power BI, Python, SQL Server, and SSRS to develop interactive dashboards, predictive models, and enterprise applications. Her professional background demonstrates her ability to translate research concepts into scalable industry solutions.

Research Interests

Her research interests lie in credibility assessment of social network users, big data analytics, and text mining. She focuses on integrating heterogeneous data sources, such as social media profiles and scholarly databases, to assess user trustworthiness for organizational purposes. She also investigates emerging research trends in library and information science using text mining approaches, contributing to the identification of new and impactful academic domains.

Honors

Ms. Dehghan has been recognized for her innovative contributions to machine learning-based social media analysis and her ability to apply academic research in real-world industrial projects. Her research has been published in well-regarded, peer-reviewed journals and indexed in major databases such as Scopus, reflecting the impact and relevance of her work.

Top Noted Publications

The credibility assessment of Twitter/X users based on organizational objectives by heterogeneous resources in big data life cycle – 2 citations, Scopus index, 2025

The text mining approach to investigate active areas of library and information science and discover emerging topics – 1 citation, Scopus index, 2024

The main components of evaluating the credibility of users according to organizational goals in the life cycle of big data – 1 citation, Scopus index, 2023

Main components of user credibility assessment according to organizational goals in the big data – Scopus index

Conclusion

Ms. Sogand Dehghan exemplifies a professional who seamlessly integrates academic research excellence with industry-driven solutions. Her expertise in data analytics, software development, and social network analysis positions her as a valuable contributor to both research and practical innovation. Through her work, she has advanced the understanding of social media credibility assessment, big data life cycle management, and emerging trends in information science. With her continuous pursuit of impactful research and her ability to bridge theory with application, she is poised to make further significant contributions to the fields of data science and information technology.

Sarah Marzen | Data Science | Best Researcher Award

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Prof. Sarah Marzen | Data Science | Best Researcher Award

Associate Professor | Keck Graduate Institute, Claremont | United States

Professor Sarah Marzen is a prominent physicist and interdisciplinary researcher based at the W. M. Keck Science Department, representing Pitzer, Scripps, and Claremont McKenna Colleges in California. With a strong foundation in theoretical physics and complex systems, she is widely recognized for her research at the intersection of information theory, neuroscience, and machine learning. Her work explores how biological and artificial systems perceive, predict, and adapt to their environments. Through academic excellence and a commitment to scientific inquiry, she has established herself as a respected voice in computational neuroscience and resource-rational modeling

Professional Profile

Scopus | Google scholar

Education

Professor Marzen earned her Ph.D. in Physics from the University of California, Berkeley, where she conducted pioneering research on “Bio-inspired problems in rate-distortion theory” under the mentorship of Professor Michael R. DeWeese. Prior to her doctoral studies, she completed a Bachelor of Science degree in Physics at the California Institute of Technology (Caltech), reflecting an early and consistent commitment to scientific excellence. She has also participated in several prestigious summer schools and professional development programs, including the Santa Fe Institute’s Complex Systems School and the MIT Kauffman Teaching Certificate Program.

Experience

Dr. Marzen currently serves as Associate Professor of Physics at the W. M. Keck Science Department. Prior to this, she served as an Assistant Professor at the same institution . Her earlier career includes a postdoctoral fellowship at the Massachusetts Institute of Technology, where she collaborated with Professors Nikta Fakhri and Jeremy England. Her teaching experience is complemented by her role as a Seminar XL/LE Facilitator at MIT, underscoring her dedication to student engagement and mentorship.

Research Interests

Professor Marzen’s research focuses on sensory prediction, reinforcement learning, resource rationality, and the integration of information theory with biological systems. She investigates how both living and artificial neural systems process and respond to information in complex, dynamic environments. Her interdisciplinary approach spans computational modeling, machine learning theory, and theoretical neuroscience. She is currently involved in major research initiatives, including an Army Research Laboratory MURI project centered on hybrid biological-artificial neural networks and a series of workshops supported by the Sloan Foundation and Carnegie Institute

Honors

Dr. Marzen has received numerous recognitions for her academic contributions, including serving as Principal Investigator (PI) or Co-PI on several major research grants. Within her institution, she has held key service roles such as membership on the Executive Committee, DEI Committee, and Data Science Curriculum Coherence Committee, reflecting her leadership in fostering academic inclusivity and interdisciplinary learning.

Top Noted Publications

Title: Statistical mechanics of Monod–Wyman–Changeux (MWC) models
Citation: 128
Year of Publication: 2013

Title: On the role of theory and modeling in neuroscience
Citation: 100
Year of Publication: 2023

Title: The evolution of lossy compression
Citation: 65
Year of Publication: 2017

Title: Informational and causal architecture of discrete-time renewal processes
Citation: 46
Year of Publication: 2015

Title: Predictive rate-distortion for infinite-order Markov processes
Citation: 45
Year of Publication: 2016

Conclusion

Professor Sarah Marzen is a highly accomplished academic whose innovative research bridges physics, neuroscience, and artificial intelligence. Her work advances our understanding of how systems learn, adapt, and make decisions under constraints, with implications for both scientific theory and technological development. Through her leadership, mentorship, and scholarly impact, she continues to shape the future of interdisciplinary research and education. Her academic rigor, commitment to collaboration, and visionary research make her a key contributor to the global scientific community.

Assoc. Prof. Dr Sadullah Çelik | Quantitative Decision Methods | Best Academic Researcher Award |

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Assoc. Prof. Dr. Sadullah Çelik | Quantitative Decision Methods | Best Academic Researcher Award

Aydın Adnan Menders University,Turkey

Assoc. Prof. Dr. Sadullah Çelik is a dynamic academician specializing in international trade, finance, and quantitative decision-making. With a strong foundation in mathematics and econometrics, he brings a multidisciplinary perspective to business and economic sciences. He is currently serving as a faculty member at Aydın Adnan Menderes University, Faculty of Economics and Administrative Sciences, Department of International Trade and Finance. Known for his dedication to teaching, research, and academic leadership, Dr. Çelik has contributed significantly to the development of innovative curricula and the advancement of data-driven decision-making in international business contexts.

profile

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Orcid

Google scholar

Education

Dr. Çelik earned his Bachelor’s degree in Mathematics from Celal Bayar University (2007–2011), followed by a Master’s degree in Geometry from Ege University, Institute of Science (2011–2013). He later pursued a Ph.D. in Econometrics from Uludağ University, Institute of Social Sciences (2013–2018), solidifying his expertise in statistical and econometric analysis. Demonstrating intellectual versatility, he also completed an associate degree in Opticianry from Ege University’s Atatürk Vocational School of Health Services.

Experience

Dr. Çelik began his academic career as a Research Assistant in the Department of Econometrics at Adnan Menderes University (2014–2021). He later joined the Department of International Trade and Finance, where he was promoted to Doctor Lecturer (2022) and subsequently to Associate Professor (2023). He has also served as the Vice Chair of the Department, showcasing his leadership abilities in academic management and curriculum development. His extensive teaching portfolio includes undergraduate and graduate-level courses such as Econometrics, Research Methods, E-commerce, International Business, and Innovation Management.

Research interest

Dr. Çelik’s primary research interests lie in Quantitative Decision Methods, Data Analytics, Business Statistics, and International Trade and Finance. His work bridges the analytical rigor of mathematics and econometrics with practical applications in global business environments. He is especially interested in how data-driven strategies can enhance international business operations, risk analysis, and financial decision-making processes.

Awards

Dr. Çelik has achieved notable academic milestones, including his appointment as Associate Professor in 2023, marking a significant recognition of his scholarly contributions. His continuous academic promotions—from Research Assistant to Associate Professorship—reflect his commitment to excellence in research, teaching, and service. While specific national or international awards are not detailed, his academic progression itself is a testament to merit and recognition within the Turkish higher education system.

Publications

1. Big Data and Data Visualization by S. Çelik and E. Akdamar, published in Academic Perspective International Refereed Journal of Social Sciences, Issue 65, pages 253–264, in 2018, has received 32 citations.

2. Analyzing Shakespeare’s Corpus with Text Mining by S. Steel, published in MANAS Journal of Social Studies, Volume 9, Issue 3, pages 1343–1357, in 2020, has been cited 17 times.

3. The Importance of Big Data Technologies for Businesses by S. Steel, published in Social Sciences Studies Journal, Volume 3, Issue 6, pages 873–883, in 2017, has accumulated 14 citations.

4. Big Data by S. Steel, published by Night Library, publication number 25931, page 176, in 2018, has received 13 citations.

5. High-frequency Words Have Higher Frequencies in Turkish Social Sciences Articles by N. Gursakal, S. Çelik, and S. Özdemir, published in Quality & Quantity, Volume 57, pages 1865–1887, in 2023, has received 7 citations.

Conclusion

In summary, Assoc. Prof. Dr. Sadullah Çelik is a well-rounded academic with a robust educational background, a strong teaching record, and a research portfolio focused on the intersection of data analytics and international business. His career reflects both depth and breadth in the social sciences, with an emphasis on analytical precision and practical application. With further expansion into international publications and collaborative research, Dr. Çelik is poised to make even greater contributions to academia and the global business research community.

Dr Ahmed Ramses El-Saeed | Mathematical Statistics | Best Researcher Award |

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Dr. Ahmed Ramses El-Saeed | Mathematical Statistics | Best Researcher Award | 

Faculty of Science, at Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Dr. Ahmed Ramses El-Saeed is an accomplished Assistant Professor of Statistics at Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. With a deep expertise in Bayesian and non-Bayesian inference, lifetime data analysis, and statistical modeling, he has made significant contributions to the field of mathematical statistics. His research focuses on the development of advanced statistical methodologies for real-world applications, including reliability engineering, data science, and econometrics. Over the years, Dr. El-Saeed has built a strong academic and research career, publishing impactful studies and actively engaging in statistical education and training.

Professional Profile

Scopus

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Google Scholar

Education 🎓

Dr. El-Saeed earned his Ph.D. in Statistics from Cairo University, Egypt (2020), with a thesis titled “Bayesian and Non-Bayesian Inference for Some Inverted Lifetime Distributions under Progressive Censoring Schemes.” Prior to that, he completed his M.Sc. in Statistics (2015) at Cairo University, focusing on life testing sampling plans. His academic journey began with a B.A. in Commerce (Statistics and Insurance) from Zagazig University (2010), where he graduated with distinction. He has also completed specialized certifications in Deep Learning, R Programming, SPSS, and Structural Equation Modeling, enhancing his expertise in data analytics and computational statistics.

Professional Experience 💼

With over a decade of experience in academia, Dr. El-Saeed has held various teaching and research positions:

Assistant Professor of Statistics at IMSIU, Saudi Arabia (2023 – Present), where he teaches advanced statistical methodologies and supervises research projects.

Lecturer of Statistics at Al-Obour High Institute for Management and Informatics, Egypt (2020 – 2023), contributing to curriculum development and statistical training.

Assistant Lecturer and Demonstrator of Statistics (2012 – 2020), mentoring students and conducting research in mathematical statistics.

Awards & Honors 🏆

Dr. El-Saeed has been recognized for his academic excellence and contributions to statistical research. His work has been featured in reputable peer-reviewed journals, and he has received accolades for his dedication to statistical education and innovation. Additionally, his engagement in international research collaborations has positioned him as a respected scholar in the field.

Top Noted Publications 📚

Power Inverted Topp–Leone Distribution in Acceptance Sampling Plans
Authors: SGN Tahani A. Abushal, Amal S. Hassan, Ahmed R. El-Saeed
Journal: Computers, Materials & Continua
Citations: 26
Year: 2021

A New Distribution for Modeling Data with Increasing Hazard Rate: A Case of COVID-19 Pandemic and Vinyl Chloride Data
Authors: AH Tolba, CK Onyekwere, AR El-Saeed, N Alsadat, H Alohali, OJ Obulezi
Journal: Sustainability
Citations: 21
Year: 2023

Estimation of Entropy for Log-Logistic Distribution under Progressive Type II Censoring
Authors: ME M. Shrahili, Ahmed R. El-Saeed, Amal S. Hassan, Ibrahim Elbatal
Journal: Journal of Nanomaterials
Citations: 21
Year: 2022

Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type‐I Censoring Scheme
Authors: A Algarni, M Elgarhy, A M Almarashi, A Fayomi, A R El-Saeed
Journal: Advances in Civil Engineering
Citations: 21
Year: 2021

Bayesian and Non-Bayesian Estimation of the Nadarajah–Haghighi Distribution: Using Progressive Type-I Censoring Scheme
Authors: I Elbatal, N Alotaibi, SA Alyami, M Elgarhy, AR El-Saeed
Journal: Mathematics
Citations: 12
Year: 2022

Acceptance Sampling Plans for the Three-Parameter Inverted Topp–Leone Model
Authors: SG Nassr, AS Hassan, R Alsultan, AR El-Saeed
Journal: Mathematical Biosciences & Engineering
Citations: 12
Year: 2022

Conclusion

Dr. Ahmed R. El-Saeed is a strong candidate for a Best Researcher Award, particularly if the award criteria emphasize expertise in Bayesian inference, lifetime data analysis, and statistical modeling. To enhance his chances, he should increase high-impact journal publications, seek research funding, and highlight past recognitions.

Mr Ernesto Diaz | Data Scientist | Best Researcher Award

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Mr Ernesto Diaz |  Data Scientist | Best Researcher Award

Assistant Specialist at University of California, San Francisco – Radiology & Biomedical Imaging , United States.

Ernesto Diaz is an accomplished researcher and data scientist specializing in biomedical imaging and artificial intelligence applications in healthcare. With a strong background in medical imaging, deep learning, and data science, he has contributed significantly to Hyperpolarized Carbon-13 MRI research, cancer imaging, and radiation oncology. His work has been recognized through prestigious NIH awards, peer-reviewed publications, and multiple conference presentations. Passionate about advancing healthcare technology, Ernesto combines technical expertise with a commitment to mentorship and diversity in STEM.

Professional Profile

Orcid

Education 🎓

Ernesto earned his Bachelor of Science in Computer Science from San Francisco State University in 2022, graduating with Dean’s List honors (2020-2022). His education provided a strong foundation in programming, data analysis, and computational research, which he has applied extensively in biomedical imaging and artificial intelligence projects.

Professional Experience 💼

  • As a Data Scientist at UCSF’s Department of Radiology and Biomedical Imaging, Ernesto leads software development for medical imaging analysis, enhancing data processing and visualization tools. His previous research experience includes working on automated radiation treatment planning and bioinformatics coding for population health studies. His contributions have improved efficiency in clinical workflows and advanced AI applications in medical imaging.

Research Interests 🌍

His research revolves around Hyperpolarized Carbon-13 MRI, deep learning for medical image segmentation, and automation in radiation oncology. At UCSF, he developed a DICOM standardization tool for metabolic imaging and co-developed a U-Net deep learning model for prostate cancer segmentation. Additionally, he has explored health disparities in underserved communities, analyzing COVID-19’s impact on marginalized populations.

Awards & Honors 🏆

  • NIH Diversity Supplement Award (2022-2024) – Recognized for contributions to Hyperpolarized 13C MRI research.
  • NIH-SF BUILD Scholar (2021-2022) – Selected for leadership potential and commitment to diversity in research.
  • Dean’s List (2020-2022) – Awarded for academic excellence at San Francisco State University.

Top Noted Publications 📚

Title: Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts

Authors: Satvik Nayak, Henry Salkever, Ernesto Diaz, Avantika Sinha, Nikhil Deveshwar, Madeline Hess, Matthew Gibbons, Sule Sahin, Abhejit Rajagopal, Peder E. Z. Larson, et al.

Journal: Tomography

Publication Year: 2025

DOI: 10.3390/tomography11030021

Indexing: Indexed in major scientific databases.

Conclusion

Ernesto Diaz is a rising leader in medical imaging research, blending AI, data science, and biomedical imaging to drive innovation. With his technical skills, research excellence, and dedication to mentorship, he continues to push the boundaries of healthcare technology and scientific discovery. 🚀