Narjes Sadeghiamirshahidi | Data Science and Analytics | Research Excellence Award

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Research Excellence Award

Narjes Sadeghiamirshahidi
Northwestern University

Narjes Sadeghiamirshahidi
Affiliation Northwestern University
Country United States
Scopus ID 56048036300
Documents 9
Citations 162
h-index 4
Subject Area Data Science and Analytics
Event Global Best Achievements Awards
ORCID 0009-0002-0878-2126

Narjes Sadeghiamirshahidi is a researcher affiliated with Northwestern University whose documented scholarly profile is associated with Data Science and Analytics. The Research Excellence Award recognizes research activity demonstrating methodological development, scholarly communication, and potential contribution to evidence-based data-driven practice. The profile records nine documents, 162 citations, and an h-index of 4, providing quantitative indicators for contextualizing the researcher’s publication and citation record. [1]

Abstract

Narjes Sadeghiamirshahidi is a researcher affiliated with Northwestern University whose scholarly record is situated within Data Science and Analytics. Her indexed profile records nine documents, 162 citations, and an h-index of 4, providing measurable indicators of research dissemination and citation visibility. The Research Excellence Award profile considers these bibliometric indicators alongside the broader relevance, methodological quality, and potential application of data-oriented research. The documented affiliation, researcher identifiers, and publication metrics provide a structured basis for academic recognition while supporting transparent verification through established scholarly platforms, including Scopus and ORCID. [1] [2]

Keywords

Research Excellence Award, Narjes Sadeghiamirshahidi, Data Science and Analytics, Northwestern University, scholarly research, bibliometrics, research impact, data analytics, Scopus, ORCID.

Introduction

Data Science and Analytics combines statistical reasoning, computational methods, data management, and domain-specific interpretation to transform complex datasets into useful evidence. Contemporary research in this field frequently emphasizes reproducibility, methodological rigor, responsible analysis, and practical relevance. Scholarly indexing services provide complementary mechanisms for documenting publication activity and citation patterns, while persistent identifiers such as ORCID help distinguish researchers and connect scholarly outputs to an individual academic record. [2] [3]

Research Profile

The available researcher profile identifies Narjes Sadeghiamirshahidi with Northwestern University and the subject area of Data Science and Analytics. The associated Scopus author identifier is 56048036300, while the ORCID identifier is 0009-0002-0878-2126. The recorded bibliometric profile contains nine documents, 162 citations, and an h-index of 4. These indicators should be interpreted as descriptive measures of indexed scholarly activity rather than as standalone measures of research quality. [1] [2]

Research Contributions

Research contributions in Data Science and Analytics may be evaluated through the development or application of analytical methods, the quality of empirical evidence, the clarity of computational workflows, and the relevance of findings to scientific or professional questions. Within this recognition profile, Sadeghiamirshahidi’s documented scholarly activity provides an evidence base for considering research engagement and dissemination. Detailed assessment should remain grounded in the underlying publications, their methodologies, venues, and independently verifiable scholarly records.

Publications

The indexed record associated with the researcher contains nine documents. Publication counts can provide a useful overview of documented scholarly activity, but they do not by themselves establish the significance or originality of individual studies. A comprehensive academic assessment considers publication content, authorship, venue quality, methodological transparency, citation context, and the extent to which research findings contribute to knowledge. The Scopus author record provides a primary source for reviewing the indexed publication profile. [1]

Research Impact

The documented citation count of 162 and h-index of 4 indicate that the researcher’s indexed publications have received scholarly attention. Citation indicators are influenced by disciplinary practices, publication age, collaboration patterns, and database coverage, and therefore require contextual interpretation. ORCID further supports the organization of scholarly identity and research outputs through a persistent researcher identifier. [2] [3]

Award Suitability

The Research Excellence Award profile is aligned with documented scholarly activity in Data Science and Analytics. Relevant evidence includes the researcher’s institutional affiliation, indexed publication record, citation activity, persistent identifiers, and the broader scientific relevance of the documented research. Award suitability should be determined through a balanced review of research quality, originality, contribution, scholarly communication, and verifiable evidence rather than relying exclusively on bibliometric indicators. The Global Best Achievements Awards provides the stated event context for this recognition.

Conclusion

Narjes Sadeghiamirshahidi’s documented academic profile reflects research activity associated with Northwestern University and Data Science and Analytics. Nine indexed documents, 162 citations, and an h-index of 4 provide measurable indicators of scholarly dissemination. Together with Scopus and ORCID identifiers, these records establish a structured basis for academic profile verification and recognition. The Research Excellence Award therefore provides a framework for acknowledging documented research engagement while maintaining emphasis on evidence, methodological quality, and scholarly contribution.

References

  1. Elsevier. (n.d.). Scopus author details: Narjes Sadeghiamirshahidi, Author ID 56048036300. Scopus.
    https://www.scopus.com/pages/authors/56048036300
  2. ORCID. (n.d.). ORCID record: Narjes Sadeghiamirshahidi. ORCID.
    https://orcid.org/0009-0002-0878-2126
  3. Agricultural Systems. (2021). Resilient regional food supply chains and rethinking the way forward: Key takeaways from the COVID-19 pandemic.
    https://doi.org/10.1016/j.agsy.2021.103101
  4. Narjes Sadeghiamirshahidi, Jafar Afshar, Ali Reza Firouzi, SAHS Hassan. (2014). Improving the Efficiency of Manufacturing Supply Chain Using System Dynamic Simulation.
    https://doi.org/10.11113/JT.V69.3120
  5. Global Best Achievements Awards. (2026). Global Best Achievements Awards.
    https://bestachievements.com/

Bhavanirath Reddy Dere | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

Bhavanirath Reddy Dere
University of New Haven

Bhavanirath Reddy Dere
Affiliation University of New Haven
Country United States
Google Scholar FACG71YAAAAJ
Subject Area Data Science and Analytics
Event Global Best Achievements Awards

The Innovative Research Award recognizes research contributions that demonstrate methodological rigor, analytical relevance, and potential value to the development of contemporary knowledge. Bhavanirath Reddy Dere, affiliated with the University of New Haven in the United States, is presented in the context of data science and analytics, a field concerned with extracting meaningful insights from complex and increasingly large-scale datasets. This recognition provides an academic context for examining research interests, scholarly contributions, publication activity, and broader research relevance within data-driven disciplines.

Abstract

Bhavanirath Reddy Dere is a researcher affiliated with the University of New Haven whose academic profile is associated with data science and analytics. The Innovative Research Award recognizes scholarly work demonstrating relevance to contemporary data-driven research, analytical methodology, and the responsible application of computational approaches. This recognition provides a structured overview of the researcher’s academic profile, research contributions, publication activity, and potential impact within data-intensive disciplines. The assessment context emphasizes scholarly relevance, methodological development, and the capacity of research to contribute to evidence-based decision-making and broader advancement of analytical knowledge across emerging data science applications.

Keywords

Data science, analytics, machine learning, data-driven research, computational methods, statistical analysis, predictive analytics, research methodology, scholarly impact, innovative research.

Introduction

Data science and analytics integrate statistical reasoning, computational techniques, and domain knowledge to transform structured and unstructured information into interpretable evidence. The rapid growth of digital datasets has increased the importance of reliable analytical frameworks capable of supporting research, forecasting, optimization, and decision-making. Scholarly evaluation in this area commonly considers methodological soundness, reproducibility, research relevance, and the ability of analytical approaches to address meaningful problems. Bibliographic indexing and researcher profiles can provide useful evidence for examining scholarly identity and publication activity. [1]

Research Profile

Bhavanirath Reddy Dere is academically affiliated with the University of New Haven and is associated with the subject area of Data Science and Analytics. The researcher’s Google Scholar identifier, FACG71YAAAAJ, provides a bibliographic pathway for examining publicly indexed scholarly activity and related academic outputs. Within data-intensive research, such profiles can support the organization of publications and citations while offering a transparent reference point for scholarly discovery and assessment. [2]

Research Contributions

Research contributions in data science and analytics may involve the development or application of analytical models, computational workflows, statistical techniques, machine learning approaches, or data-management strategies. The significance of such work depends on the research question, methodological rigor, quality of evidence, reproducibility, and practical or theoretical relevance. In this recognition context, the Innovative Research Award provides a framework for acknowledging research aligned with these characteristics while maintaining a scholarly focus on analytical innovation and evidence-based investigation.

Publications

Publication activity is an important component of academic research assessment because peer-reviewed articles, conference contributions, and other scholarly outputs document the development and dissemination of research findings. The Google Scholar profile associated with Bhavanirath Reddy Dere can serve as a bibliographic reference for identifying publicly indexed publications and citation records. Publication evaluation should consider the relevance and quality of individual contributions rather than relying solely on publication counts or citation totals.

Research Impact

The impact of data science research may extend across academic, technological, organizational, and societal contexts. Analytical research can contribute to improved understanding of complex datasets, more reliable predictive processes, enhanced decision-support systems, and the development of computational methodologies. Meaningful impact is best assessed through evidence such as scholarly citations, adoption of methods, collaboration, reproducibility, and demonstrable application. These considerations place research recognition within a broader framework of sustained scholarly contribution rather than a single measure of achievement.

Award Suitability

The Innovative Research Award is aligned with research that demonstrates originality, analytical relevance, methodological quality, and potential contribution to its academic field. Bhavanirath Reddy Dere’s association with Data Science and Analytics provides a relevant disciplinary context for this recognition, particularly given the growing importance of computational and evidence-based approaches in contemporary research. The award context therefore corresponds with a research profile in which analytical methods and data-driven investigation form an important part of scholarly activity.

Conclusion

Bhavanirath Reddy Dere’s academic affiliation with the University of New Haven and research association with Data Science and Analytics provide the scholarly context for the Innovative Research Award. The recognition highlights the importance of methodological rigor, analytical innovation, publication activity, and research relevance in data-intensive disciplines. Continued scholarly dissemination, transparent research practices, and demonstrable application of analytical methods can further strengthen the long-term academic value and visibility of research in this rapidly developing field.

References

  1. Applications of Machine Learning Across Smart Manufacturing, Healthcare, Finance, Computer Vision, Robotics, and Environmental & Sustainability: A Systematic Literature Review.
    https://doi.org/10.3390/app16136574
  2. Google Scholar: Bhavanirath Reddy Dere.
    https://scholar.google.com/citations?user=FACG71YAAAAJ&hl=en

Riyadh Hossain | Data Science | Excellence in Research Award

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Mr. Riyadh Hossain | Data Science | Excellence in Research Award

Mr. Riyadh Hossain, Noakhali Science and Technology University, Bangladesh.

Mr. Riyadh Hossain is a dedicated researcher and data scientist specializing in public health statistics and epidemiological research. He holds a BSc and MSc in Statistics from Noakhali Science and Technology University, Bangladesh. His work focuses on child health, disease modeling, and machine learning applications in healthcare. He has published in reputed journals like BMC Public Health and Taylor & Francis and received the National Science & Technology Fellowship. He is also a reviewer for international journals and an active research instructor.

Profile

SCOPUS ID

Early Academic Pursuits

Mr. Riyadh Hossain demonstrated academic excellence from the outset of his educational journey. He completed his Secondary School Certificate (SSC) in 2014 from Chatkhil P.G Govt High School and his Higher Secondary Certificate (HSC) in 2016 from Govt Science College, Dhaka.. Continuing his education at Noakhali Science and Technology University (NSTU), he earned a Bachelor’s degree in Statistics in 2023.  His academic journey culminated with an ongoing Master’s in Statistics, where he currently holds a CGPA of 3.76, reflecting consistent academic dedication and performance.

Professional Endeavors

Mr. Hossain has steadily built a dynamic professional portfolio in both teaching and applied research. Since January 2024, he has served as a Data Scientist, working on time series modeling, coding in R and Python, and health insurance data prediction. Earlier, as an Instructor (2023–2024), he delivered live sessions on Research Methodology and Machine Learning. His experience includes serving as a Research Assistant (2021–2025) at NSTU, focusing on child health and cluster randomized trials. Additionally, in 2019–2020, he worked as a Statistician at Eusuf & Associates, refining his skills in data cleaning, documentation, and report writing.

Contributions and Research Focus On Data Science

Mr. Hossain’s research portfolio reflects a commitment to public health and statistical modeling. His Master’s thesis investigates the determinants of child physical health development in Bangladesh, employing non-parametric techniques to examine the influence of socioeconomic and demographic factors. His research extends to dengue fever spread in the USA, machine learning applications in cardiovascular disease prediction, and studies on malnutrition, mental health, and low birth weight. His commitment to statistical application in health sciences is evident in both published and under-review manuscripts.

Impact and Influence

Through his research and teaching, Mr. Hossain has made a meaningful impact on statistical education and public health research. His work, cited in prominent journals such as BMC Public Health and Taylor & Francis, addresses critical societal issues like environmental health and child development. He actively contributes to global health awareness as a technical volunteer at UNICEF Bangladesh and a member of Statistics without Borders, emphasizing social responsibility and scientific integrity.

Research Skills

Mr. Hossain possesses robust research skills in both theoretical and applied statistics. He is adept in Python, SPSS, R, and STATA, and well-versed in time series analysis, hypothesis testing, multivariate analysis, and machine learning. His training includes online certifications from Duke, Rice, and Imperial College London, reinforcing his capabilities in data analysis and public health statistics. His analytical strength is complemented by his ability to translate data into actionable insights, particularly in the health and development sectors.

Awards and Honors

In recognition of his academic and research excellence, Mr. Hossain received the National Science & Technology Fellowship (2023) from the Ministry of Science and Technology, Government of Bangladesh, and the NSTU Research Cell Project Award (2024) for joint research collaboration. These accolades underscore his dedication to research innovation and his potential for significant future contributions in statistical and health sciences.

Academic Citations

Mr. Hossain has authored and co-authored several peer-reviewed publications in international journals, including BMC Public Health, Discover Mental Health, BMC Cardiovascular Disorders, and more. His citations are steadily growing, particularly on studies addressing child health, environmental epidemiology, and machine learning in disease prediction. His work has attracted attention for its relevance, methodology, and real-world applications, positioning him as a rising academic in biostatistics and public health.

Legacy and Future Contributions

Mr. Hossain is poised to become a leading contributor in statistical health research, with ambitions to influence evidence-based policy and data-driven healthcare planning. His ongoing commitment to scientific integrity, social advocacy, and academic collaboration ensures that his work will continue to advance public health statistics in Bangladesh and globally. Through his academic mentorship, international collaborations, and innovative research, he is building a lasting legacy of knowledge, service, and impact.

Publications Top Notes

Determinants of Child Physical Health Development in Bangladesh: A Study of Key Socioeconomic and Cultural Influences

Authors: Riyadh Hossain, Mohammad Omar Faruk & Najma Begum
Journal: BMC Public Health (2025), Volume 25, Article 2447

Impact of Environmental Factors on the Spread of Dengue Fever in the United States of America (USA)

Authors: Riyadh Hossain, Tahmina Akter, Mohammad Omar Faruk, Sorif Hossain & Md Rasel Hossain
Journal: International Journal of Environmental Health Research (online ahead of print, July 2025)

Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross‑Sectional Study in 2023

Authors: Sorif Hossain, Mohammad Kamrul Hasan, Mohammad Omar Faruk, Nelufa Aktar, Riyadh Hossain & Kabir Hossain
Journal: BMC Cardiovascular Disorders (2025)

Malnutrition and Its Associated Factors among Children under Five: A Case Study of the Chattogram Division

Authors: Riyadh Hossain, Shahinoor Jamal Muna & Nusrat Jahan Onu
Journal: Food and Nutrition Sciences (2025)

Impact of Socio‑economic, Demographic and Cultural Factors on the Development of Children’s Mental Health: A Cross‑Sectional Study in Bangladesh

Authors: Sarmin Akhter, Riyadh Hossain & Mohammad Omar Faruk
Journal: Discover Mental Health (2025)

Nikolay M. Sirakov | Data Science | Excellence in Research Award

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Prof. Nikolay M. Sirakov | Data Science | Excellence in Research Award

Prof. Nikolay M. Sirakov, East Texas A&M University, Dept. Mathematics, United States.

Nikolay Metodiev Sirakov is a Professor of Mathematics at East Texas A&M University 🏫, specializing in pattern recognition, machine learning, and mathematical modeling 🔬. With a Ph.D. from the Bulgarian Academy of Sciences 🎓, his research spans image processing, artificial intelligence, and biomedical applications 🧠. He has collaborated with leading global institutions 🌍 and supervised numerous Ph.D. and Master’s students 📚. His contributions to computer vision and AI-driven diagnostics 🤖 have earned him international recognition. ✨

🌟 Professional Profile

🎓 Early Academic Pursuits

Nikolay Metodiev Sirakov’s academic journey began with rigorous training at Bulgaria’s top institutions, including the Bulgarian National High School of Mathematics and CS. He pursued undergraduate studies at Sofia University, earning his B.S. in Mathematics and Computer Science. He went on to complete his Master’s in Coding Theory at Sofia University and later obtained his Ph.D. from the Bulgarian Academy of Sciences, specializing in Pattern Recognition.

💼 Professional Endeavors

Sirakov has held various prestigious academic positions, including professor and associate professor at Texas A&M University-Commerce since 2004. He also served as a senior researcher and invited professor at institutions like Instituto Superior Tecnico, Lisbon, and Northern Arizona University. His leadership includes chairing committees and collaborating with diverse institutions globally.

🔬 Contributions and Research Focus On Data Science 

Sirakov’s research spans machine learning, image processing, and biomedical applications, with significant contributions to skin cancer diagnosis, tracking objects in video, and automatic threat detection. He has led multiple international collaborations, publishing numerous peer-reviewed papers and advancing computational techniques, particularly in sparse representation and neural network

🌍 Impact and Influence

With his vast expertise, Sirakov has influenced fields such as medicine, security, and robotics, making a profound impact on medical imaging and biomechanics. His work in automated melanoma diagnosis has gained recognition in the medical community, while his contributions to video object tracking and image segmentation remain highly influential in computer vision.

🏆 Awards and Honors

  • Best Paper Award – Oluwaseyi Igbasanmi, Nikolay M. Sirakov, and Adam Bowden were recognized for their paper, CNN for Efficient Objects Classification with Embedded Vector Fields, presented at ICCIDA2023 and published in the Springer book series. 📚

  • 2nd Place Winner in Mathematics – Elisha Shachar received recognition for the project on An Artificial Intelligence-Based Driving Environment Descriptor: Voice Alerts to Drivers at the 15th TAMU System Pathway Students Symposium. 🚗

  • 1st Place Winner in Mathematics – Mengzhe Chen, supervised by Nikolay Sirakov, presented Singular Points of the Gradient Field of the Poisson Partial Differential Equation Solution on an Image at the TAMU System Pathway Students Symposium. 🔢

  • Lockheed Martin Best Paper Award – Awarded to a team for their paper, From Shape to Threat: Exploiting the Convergence Between Visual and Conceptual Organization for Weapon Identification and Threat Assessment. 🎖

🚀 Legacy and Future Contributions

Sirakov’s legacy is built on his innovative contributions to computational science and biomedical engineering. Looking ahead, his continued work in machine learning and medical applications promises to influence the next generation of scientific advancements in healthcare technologies and security systems.

📚Publications Top Notes

  • A system for reconstructing and visualizing three-dimensional objects
    Citations: 70 📊
    Year: 2001 🗓️

  • Lesion detection in dermoscopy images with novel density-based and active contour approaches
    Citations: 51 📈
    Year: 2010 🩺

  • A new active convex hull model for image regions
    Citations: 36 📐
    Year: 2006 🔍

  • Dermoscopic diagnosis of melanoma in a 4D space constructed by active contour extracted features
    Citations: 35 💡
    Year: 2012 🧑‍⚕️

  • Interpolation approach for 3D smooth reconstruction of subsurface objects
    Citations: 34 🌍
    Year: 2002 🖥️

  • Automatic boundary detection and symmetry calculation in dermoscopy images of skin lesions
    Citations: 30 🔬
    Year: 2011 🧠

  • Efficient segmentation with the convex local-global fuzzy Gaussian distribution active contour for medical applications
    Citations: 26 💉
    Year: 2015 📊

  • Intelligent shape feature extraction and indexing for efficient content-based medical image retrieval
    Citations: 26 🔍
    Year: 2004 🏥

  • Recognition of emotional states in natural human-computer interaction
    Citations: 23 😐
    Year: 2008 🤖

  • An integral active contour model for convex hull and boundary extraction
    Citations: 22 🏞️
    Year: 2009 🔧

  • Threat assessment using visual hierarchy and conceptual firearms ontology
    Citations: 19 🔫
    Year: 2015 🚨

  • Search space partitioning using convex hull and concavity features for fast medical image retrieval
    Citations: 19 🔎
    Year: 2004 🏥

  • Optimal set of features for accurate skin cancer diagnosis
    Citations: 18 🩺
    Year: 2014 🧬

  • Skin lesion feature vectors classification in models of a Riemannian manifold
    Citations: 17 🏥
    Year: 2015 🔬

  • Automatic feature extraction and recognition for digital access of books of the Renaissance
    Citations: 15 📚
    Year: 2000 🔍

  • Sparse representation wavelet-based classification
    Citations: 11 🖼️
    Year: 2018 💻

  • A novel classification system for dysplastic nevus and malignant melanoma
    Citations: 11 🩺
    Year: 2016 🌟

  • New accurate automated melanoma diagnosing systems
    Citations: 11 🧬
    Year: 2015 ⚕️

  • Weapon ontology annotation using boundary describing sequences
    Citations: 11 🔫
    Year: 2012 🛡️

  • Active contour directed by the Poisson gradient vector field and edge tracking
    Citations: 10 🖥️
    Year: 2021 📉

  • Comparing 2D borders using regular structures
    Citations: 10 🔍
    Year: 1994 🖼️

  • Support vector machine skin lesion classification in Clifford algebra subspaces
    Citations: 9 🩺
    Year: 2019 📈

  • Poisson equation solution and its gradient vector field to geometric features detection
    Citations: 9 🔬
    Year: 2018 🔧

  • Integration of low-level and ontology-derived features for automatic weapon recognition and identification
    Citations: 9 🛡️
    Year: 2011 💡

  • Monotonic vector forces and Green’s theorem for automatic area calculation
    Citations: 9 🔍
    Year: 2007 📐

  • A new automatic concavity extraction model
    Citations: 9 🔍
    Year: 2006 🧠

  • Classification with stochastic learning methods and convolutional neural networks
    Citations: 8 🤖
    Year: 2020 💻

  • From shape to threat: exploiting the convergence between visual and conceptual organization for weapon identification and threat assessment
    Citations: 8 🔫
    Year: 2013 🛡️

  • Skin lesion image classification using sparse representation
    Citations: 8 🩺
    Year: 2018 📊