Madhiarasan Manoharan | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

Madhiarasan Manoharan
Independent Researcher, India

Madhiarasan Manoharan
Affiliation Independent Researcher
Country India
Scopus ID 56997519900
Documents 31
Citations 624
h-index 10
Subject Area Data Science and Analytics
Event International Soil Scientist Awards

Madhiarasan Manoharan is an independent researcher in the subject area of Data Science and Analytics. His research profile in the context of the Innovative Research Award, presented at the International Academic Achievements & Awards event [4].

Abstract

This article presents a scholarly overview of Madhiarasan Manoharan, an independent researcher working in Data Science and Analytics. It summarises his bibliometric indicators as recorded in Scopus, outlines the scope of his research activity, and considers his recognition through the Innovative Research Award [1][4].

Keywords

Data science; analytics; independent research; bibliometrics; Scopus; academic recognition; innovative research.

Introduction

Data science and analytics draw on statistics, computing and domain knowledge to extract useful information from data. Researchers in this field often contribute across several application areas. Madhiarasan Manoharan conducts his research as an independent researcher, and his publication record is indexed in the Scopus database [1][5].

Research Profile

The Scopus profile, identified by Author ID 56997519900, records the following indicators [1]:

  • Documents indexed: 31
  • Total citations: 624
  • h-index: 10
  • Subject area: Data Science and Analytics

The researcher is also identifiable through an ORCID record, a persistent digital identifier that links a researcher to his or her scholarly outputs [2].

Research Contributions

His work falls within Data Science and Analytics, with emphasis on the analytical and computational treatment of data. The indexed output shows sustained scholarly activity, and the citation count indicates that parts of this work have been referenced by other researchers [1]. Detailed descriptions of individual studies are available through the publication records listed on the Scopus profile and in the DOI-registered articles [3].

Publications

The Scopus database lists 31 documents under this author profile [1]. Representative publications can be verified through their Digital Object Identifiers (DOIs), which provide permanent links to the published versions [3]. Readers are encouraged to consult the full list on the profile for complete bibliographic details.

Research Impact

Citation-based indicators offer one measure of research visibility. A total of 624 citations and an h-index of 10 indicate that at least ten of his documents have each received ten or more citations [1]. Such metrics are descriptive and should be read alongside the quality and context of the work itself.

Award Suitability

The Innovative Research Award, presented at the International Academic Achievements & Awards event, recognises researchers whose work shows originality and scholarly merit [4]. His suitability may be considered on the following grounds:

  • A body of 31 indexed documents in a recognised database [1]
  • Citation evidence of 624 citations and an h-index of 10 [1]
  • Independent research activity within Data Science and Analytics

Conclusion

Madhiarasan Manoharan has built a documented research record in Data Science and Analytics as an independent researcher. His Scopus indicators support his consideration for the Innovative Research Award [1][4].

References

  1. Madhiarasan, M., & Louzazni, M. (2022). Analysis of artificial neural network: Architecture, types, and forecasting applications. Journal of Electrical and Computer Engineering, 2022(1), 5416722.
    https://doi.org/10.1155/2022/5416722
  2. Madhiarasan, M., & Deepa, S. N. (2016). Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting. Artificial Intelligence Review, 1–23.
    https://doi.org/10.1007/s10462-016-9506-6
  3. Al-Dahidi, S., Madhiarasan, M., Al-Ghussain, L., Abubaker, A. M., Ahmad, A. D., et al. (2024). Forecasting solar photovoltaic power production: A comprehensive review and innovative data-driven modeling framework. Energies, 17(16), 4145.
    https://doi.org/10.3390/en17164145
  4. Madhiarasan, M., & Roy, P. P. (2022). A comprehensive review of sign language recognition: Different types, modalities, and datasets. arXiv preprint arXiv:2204.03328.
    https://arxiv.org/abs/2204.03328
  5. Madhiarasan, M., & Deepa, S. N. (2016). A novel criterion to select hidden neuron numbers in improved back propagation networks for wind speed forecasting. Applied Intelligence, 44(4), 878–893.
    https://doi.org/10.1007/s10489-015-0737-z

Takeshi Tsuda | Data Science | International Soil Scientist Awards

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International Soil Scientist Awards

Takeshi Tsuda
Nemours Children’s Hospital Delaware

Takeshi Tsuda
Affiliation Nemours Children’s Hospital Delaware
Country United States
Scopus ID 7402162750
Documents 70
Citations 1,810
h-index 22
Subject Area Data Science and Analytics
Event International Soil Scientist Awards

Takeshi Tsuda is a physician-researcher affiliated with Nemours Children’s Hospital Delaware whose indexed scholarly record includes work in pediatric cardiology, cardiovascular physiology, exercise performance, cardiomyopathy, and related clinical research. Institutional and bibliographic sources identify his affiliation with Nemours Children’s Health Delaware and associate his publications with the ORCID identifier 0000-0001-8110-1388. [1] [2]

Abstract

This academic recognition profile presents the research record of Takeshi Tsuda in the context of the International Soil Scientist Awards. The supplied bibliometric profile records 70 documents, 1,810 citations, and an h-index of 22. Independent institutional and bibliographic sources document a broader research program centered on pediatric cardiovascular medicine and quantitative clinical investigation. [1] [3]

Keywords

  • Takeshi Tsuda
  • Research Analytics
  • Data Science and Analytics
  • Pediatric Cardiology
  • Cardiovascular Research
  • International Soil Scientist Awards

Introduction

Takeshi Tsuda’s documented academic activity spans clinical research and quantitative investigation in pediatric cardiovascular medicine. Nemours identifies him as a physician at its Delaware children’s hospital, while its researcher profile records research interests involving exercise physiology, cardiac rehabilitation, cardio-oncology, and dystrophic heart research. [2] These areas provide a substantial evidence base for examining how structured research data, clinical measurements, and analytical methods can contribute to biomedical investigation.

Research Profile

The supplied Scopus-based profile lists 70 documents, 1,810 citations, and an h-index of 22. Scopus-associated bibliographic information independently identifies author ID 7402162750 and reports publications across multiple biomedical journals. [1] The profile should therefore be interpreted as a bibliometric snapshot rather than a complete measure of research quality or significance.

Research Contributions

Published studies associated with Tsuda include investigations of cardiovascular abnormalities, exercise performance, cardiomyopathy, and pediatric cardiac conditions. For example, research has examined subclinical coronary abnormalities following arterial switch procedures, while later work addressed cardiovascular changes associated with Duchenne muscular dystrophy. [4] [5] Such publications illustrate the use of clinical observations, physiological measurements, and quantitative analysis in pediatric cardiovascular research.

Publications

  • Tsuda T. Transient Ischemic Attack and Ischemic Stroke in Danon Disease with Formation of Left Ventricular Apical Thrombus despite Normal Systolic Function. doi:10.1155/2017/6576382. [3]
  • Tsuda T. et al. Identifying Subclinical Coronary Abnormalities and Silent Myocardial Ischemia After Arterial Switch Operation. doi:10.1007/s00246-019-02085-4. [4]
  • Tsuda T. et al. Sinus Tachycardia and Unrelieved Wall Stress Precede Left Ventricular Systolic Dysfunction During Preclinical Cardiomyopathic Changes in Duchenne Muscular Dystrophy. doi:10.3390/jcdd12080280. [5]

Research Impact

The supplied citation metrics indicate a substantial indexed publication and citation record. Bibliographic sources also show continuing publications through 2026, including research on exercise performance and cardiovascular conditions. [1] [6] Citation counts and h-index values are useful indicators of scholarly visibility, but they do not independently establish the methodological quality, clinical importance, or originality of individual studies.

Award Suitability

For an award consideration process, the documented publication record, citation indicators, institutional affiliation, and identifiable research outputs provide material that can be evaluated against the award’s published criteria. The available evidence is primarily biomedical rather than soil-science-specific; consequently, any assessment should distinguish general research achievements from contributions directly relevant to soil science, environmental systems, or agricultural applications. The award committee may use the complete submitted record for its formal evaluation.

Conclusion

Takeshi Tsuda’s documented research profile combines an established bibliometric record with continuing scholarly activity in pediatric cardiovascular and quantitative clinical research. Institutional records and indexed publications provide verifiable evidence of his affiliation and research outputs, while the supplied Scopus metrics offer a concise representation of citation-based visibility. [1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Takeshi Tsuda, Author ID 7402162750. Scopus/ScienceDirect author record.
    https://www.sciencedirect.com/author/7402162750/takeshi-tsuda
  2. Nemours Children’s Health. (n.d.). Takeshi Tsuda, MD — Researcher Profile.
    https://www.nemours.org/find-a-researcher/4660-takeshi-tsuda-wilmington.html
  3. Tsuda, T. et al. (2017). Transient Ischemic Attack and Ischemic Stroke in Danon Disease with Formation of Left Ventricular Apical Thrombus despite Normal Systolic Function. Case Reports in Pediatrics. DOI: https://doi.org/10.1155/2017/6576382
  4. Tsuda, T. et al. (2019). Identifying Subclinical Coronary Abnormalities and Silent Myocardial Ischemia After Arterial Switch Operation. Pediatric Cardiology. DOI: https://doi.org/10.1007/s00246-019-02085-4
  5. Tsuda, T., Walczak, A., & O’Neil, K. (2025). Sinus Tachycardia and Unrelieved Wall Stress Precede Left Ventricular Systolic Dysfunction During Preclinical Cardiomyopathic Changes in Duchenne Muscular Dystrophy. Journal of Cardiovascular Development and Disease. DOI: https://doi.org/10.3390/jcdd12080280

DongHee Park | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

DongHee Park
DAVISS, South Korea

DongHee Park
Affiliation DAVISS
Country South Korea
Documents 8
Subject Area Data Science and Analytics
Event International Soil Scientist Awards
ORCID 0009-0008-0010-1756

DongHee Park is a researcher affiliated with DAVISS in South Korea whose documented scholarly work applies data science and machine-learning methods to industrial fault diagnosis. The available record contains eight publications addressing condition-index representations, diagnostic automation, convolutional neural networks and feature classification for machinery faults. [1]

Abstract

Park’s research focuses on computational approaches for identifying and classifying faults in industrial machinery. Recent work compares conventional feature-based representations with physics-guided condition-index representations for motor fault diagnosis, while related studies investigate automated elevator diagnosis, rotor-system classification and machine-learning methods for blade-rubbing defects. [1] [2] The research demonstrates a consistent interest in translating operational data into diagnostic information.

Keywords

Data science; machine learning; fault diagnosis; condition monitoring; industrial motors; diagnostic automation; convolutional neural networks; rotor systems; blade rubbing; feature classification; predictive maintenance.

Introduction

Modern industrial systems generate large quantities of operational data that can be analysed to detect abnormal behaviour. Machine-learning algorithms and engineered diagnostic features provide methods for distinguishing normal operation from specific fault conditions. Park’s publication record addresses these challenges across several machinery categories, linking data analytics with practical condition-monitoring problems. [3]

Research Profile

The supplied profile records eight documents in the subject area of Data Science and Analytics. Citation and h-index values were not provided and therefore are not inferred. The publication sequence from 2024 to 2026 indicates sustained investigation of machine-learning feature development, classification methods, automated diagnosis and physics-guided representations.

Research Contributions

  • Evaluation of conventional and physics-guided representations for industrial motor fault diagnosis. [1]
  • Development of rule-based automation technology for elevator fault diagnosis. [2]
  • Use of combination images of feature vectors with convolutional neural networks for rotor fault classification. [3]
  • Development and classification of machine-learning features for early blade-rubbing diagnosis. [4] [5]

Publications

Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis (2026), Machines. [1]

Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis (2025), Sensors. [2]

CNN-based fault classification using combination image of feature vectors in rotor systems (2024), Journal of Mechanical Science and Technology. [3]

A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing (2024), Sensors.[4]

Development of features for blade rubbing defect classification in machine learning (2024), Journal of Mechanical Science and Technology.[5]

Research Impact

Because citation and h-index values were not supplied, quantitative scholarly impact cannot be assessed from the available profile alone. The publication record nevertheless demonstrates application of data-driven techniques to multiple industrial systems, including motors, elevators, rotor systems and machinery affected by blade rubbing. Publication across Machines, Sensors and the Journal of Mechanical Science and Technology provides a documented scholarly context for these contributions. [1] [3]

Award Suitability

The Innovative Research Award profile is supported by a coherent body of research applying machine learning and analytical representations to industrial fault diagnosis. The progression from feature development and classification toward automated diagnostic systems and physics-guided condition indices indicates methodological continuity. [2] [5] Final recognition should be determined using the complete scholarly record, originality, methodological quality, practical relevance, reproducibility and broader research contributions.

Conclusion

DongHee Park’s documented research is centred on data science and analytics for industrial condition monitoring and fault diagnosis. Publications from 2024 through 2026 cover machine-learning features, convolutional neural networks, rule-based automation and physics-guided diagnostic representations. This focused research trajectory provides a substantive basis for consideration for the Innovative Research Award within the International Soil Scientist Awards framework.

References

  1. Park, DongHee. (2026). Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis. Machines.
    https://doi.org/10.3390/machines14091024
  2. Park, DongHee. (2025). Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis. Sensors.
    https://doi.org/10.3390/s26010223
  3. Park, DongHee. (2024). CNN-based fault classification using combination image of feature vectors in rotor systems. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-024-1006-z
  4. Park, DongHee. (2024). A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing. Sensors.
    https://doi.org/10.3390/s24186013
  5. Park, DongHee. (2024). Development of features for blade rubbing defect classification in machine learning. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-023-1201-3

Lyubomur Lyubenov | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

Lyubomur Lyubenov
Researcher Lyubomur Lyubenov
Affiliation University of Ruse “Angel Kanchev”
Country Bulgaria
Google Scholar ID OE5r13gAAAAJ
Documents 182
Citations 175
h-index 7
Subject Area Data Science and Analytics
Event International Soil Scientist Awards
ORCID 0000-0002-5500-0265

Lyubomur Lyubenov
University of Ruse “Angel Kanchev”, Bulgaria

Lyubomur Lyubenov is a researcher affiliated with the University of Ruse “Angel Kanchev”, Bulgaria. His scholarly activities span agricultural economics, agricultural markets, beekeeping economics, rural development, and analytical methods applied to market research. His publications demonstrate a sustained interest in understanding agricultural value chains, market structures, and the economic sustainability of beekeeping and agricultural production systems. His academic record, supported by numerous publications and citations, reflects continued contributions to applied research relevant to agriculture and data-driven decision making.[1]

Abstract

The Innovative Research Award recognizes researchers whose scholarly work demonstrates originality, practical significance, and measurable academic contribution. Lyubomur Lyubenov has developed research focused on agricultural economics, agricultural market organization, beekeeping economics, and market analysis. His studies provide valuable evidence regarding agricultural product commercialization, market development, and economic sustainability. Through publications, conference participation, and analytical investigations, his work contributes to understanding agricultural market dynamics while supporting evidence-based policy and management decisions.[1][3]

Keywords

Agricultural Economics, Agricultural Markets, Data Science and Analytics, Beekeeping Economics, Honey Markets, Market Analysis, SWOT Analysis, Rural Development, Economic Research, Sustainable Agriculture.

Introduction

Academic research in agricultural economics increasingly relies on multidisciplinary analytical approaches that integrate economics, market intelligence, policy analysis, and data interpretation. Lyubenov’s research reflects these characteristics through investigations of agricultural markets and the economic performance of beekeeping sectors. His publications examine both domestic and international market environments, providing practical perspectives relevant to producers, policymakers, researchers, and agricultural stakeholders.[2]

Research Profile

Lyubomur Lyubenov has produced a substantial body of academic work comprising approximately 182 documented scholarly outputs and an h-index of 7. His research interests primarily include agricultural economics, agricultural market structures, commercialization of agricultural products, beekeeping economics, rural economic development, and analytical evaluation of agricultural sectors. His publications have received over 175 citations, indicating continued academic engagement with his research.[1]

Research Contributions

  • Investigated international and domestic markets for bee products, evaluating commercial opportunities and market dynamics.[3]
  • Published research addressing agricultural product marketing and commercialization through comprehensive economic analysis.[4]
  • Conducted SWOT analyses of Bulgarian beekeeping to identify strategic development opportunities.[5]
  • Examined development potential within Bulgarian beekeeping industries and agricultural markets.
  • Studied market diversification opportunities beyond traditional honey production, broadening understanding of value-added bee products.

Publications

  1. International and national markets for bee products. Economic Thought Journal, 102–121 (2019).
  2. Пазари и реализация на земеделски продукти. Monograph. ISBN 978-619-207-039-7 (2016).
  3. SWOT Analysis of the Bulgarian Beekeeping (2018).
  4. Potential of Bulgarian Beekeeping. Round Table Proceedings (2017).
  5. Beekeeping Markets outside the Honey Category. Journal of Mountain Agriculture on the Balkans.

Research Impact

The research portfolio demonstrates continued engagement with economically important issues affecting agriculture and apiculture. By combining market analysis with applied economic investigation, the work contributes to understanding agricultural competitiveness, market diversification, and sustainable commercialization. The documented publication record and citation profile indicate scholarly visibility while supporting future research in agricultural economics and market analytics.[1]

Award Suitability

Lyubomur Lyubenov’s sustained scholarly activity, emphasis on analytical research, publication productivity, and contributions to agricultural market studies demonstrate characteristics aligned with the objectives of the Innovative Research Award. His work illustrates the application of research methodologies to practical agricultural challenges while supporting knowledge development in agricultural economics and related analytical disciplines.[1][4]

Conclusion

The academic record of Lyubomur Lyubenov demonstrates consistent engagement in agricultural economics and market-oriented research. His investigations into beekeeping economics, agricultural product commercialization, and market development contribute to scholarly understanding of agricultural systems. Collectively, these achievements provide a solid academic foundation supporting recognition through the Innovative Research Award.

References

  1. Google Scholar. (n.d.). Lyubomur Lyubenov Scholar Profile.
    https://scholar.google.com/citations?hl=bg&user=OE5r13gAAAAJ
  2. ORCID. (n.d.). ORCID Record: 0000-0002-5500-0265.
    https://orcid.org/0000-0002-5500-0265
  3. Lyubenov, L. (2019). International and National Markets for Bee Products. Economic Thought Journal.
    https://ideas.repec.org/a/bas/econth/y2019i1p102-121.html
  4. SWOT analysis of the Bulgarian beekeeping. ISBN 978-619-207-039-7.
  5. Lyubenov, L. (2018). SWOT Analysis of the Bulgarian Beekeeping.

Yung Lan Yeh | Data Science | Research Excellence Award

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Prof. Dr. Yung Lan Yeh | Data Science | Research Excellence Award

Academy of Circular Economy-Intelligent Technology Program | Taiwan

Prof. Yung-Lan Yeh is an accomplished aerospace engineer and academic leader with extensive expertise spanning aerospace engineering, experimental fluid mechanics, aerodynamics, computational fluid dynamics, smoke-flow visualization, jet flow control, and UAV systems. His professional portfolio integrates advanced UAV design and system integration with industrial and aerodynamic design, civil aviation systems, maintenance practices, and modern manufacturing approaches such as 3D printing. He has led and contributed to high-impact technological projects focused on aerodynamic property verification, bio-inspired wind turbine development, and the investigation and prediction of complex jet flow structures using artificial neural network methods. Beyond research and industry engagement, he has played a sustained role in academic service, including editorial responsibilities in an international aeronautics journal, and has supported education and research through teaching and technical assistance in aerospace and aeronautics departments. His technical competencies further include aircraft maintenance, ultrasonic testing, and carbon fiber composite material repair, reflecting a strong balance between theoretical knowledge and applied engineering practice. His scholarly output comprises 23 published documents, which have collectively received 157 citations across 150 citing documents, resulting in an h-index of 5, demonstrating consistent contributions to aerospace science, fluid mechanics, and applied engineering research.

Citation Metrics (Scopus)

160
120
80
40
0

Citations 157

Documents 23

h-index
5

Citations

Documents

h-index

On the Experimental Study of Active Atmosphere Control of 3D Printing via Nozzle Design

– Journal of Aeronautics, Astronautics and Aviation (Journal Article)

Preliminary Design and Application of Anti-Explosion Technology for UAV Based on 3D Printing

– Journal of Aerospace (Journal Article)

Modular High Lift Device for Fixed-Wing UAV

– Journal of Aeronautics, Astronautics and Aviation

Thermal Analysis of a Radial Heat Sink

– Journal of Aeronautics, Astronautics and Aviation

Performance Analysis of UAV Propellers with Variable Inclined Angles

– Journal of Aeronautics, Astronautics and Aviation (Journal Article)
 

Axelle Hego | Data Science | Best Researcher Award

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Dr. Axelle Hego | Data Science | Best Researcher Award

Dr. Axelle Hego, University of Lorraine, France

Dr. Axelle Hego is a research engineer at the University of Lorraine, France. She specializes in model analysis, sensitivity analysis, and environmental system modeling. Her doctoral research focused on hydric models for green roof structures, exploring the influence of soil and meteorological parameters using advanced control and analysis techniques. With a strong background in Control and Complex Systems Engineering, Dr. Hego has collaborated with institutions like Cerema and contributed to international conferences and journals in the field of environmental modeling and risk assessment.

Profile

Scopus

Orcid

Summary:

Dr. Axelle Hego’s research stands at the intersection of control systems and environmental modeling, with significant implications for urban ecological design. Her innovative work on dynamic hydric models for green roofs addresses key questions in soil science, water retention, and climate resilience. Though still early in her career, her technical strengths, research relevance, and international engagement make her a compelling candidate.

🎓 Education

Axelle HEGO holds an engineering degree complemented by a specialized master’s degree in Control and Complex Systems Engineering. Her academic training laid the foundation for her later doctoral research focused on hydric modeling, specifically applied to green roof structures. Her educational journey provided her with a deep understanding of control systems, dynamic models, and analytical methods suited to complex environmental systems.

💼Experience

Currently serving as a Research Engineer at CEA-Liten, Axelle brings both academic and applied research experience to her role. During her PhD, she worked closely with Cerema—a public institution under France’s Ministry for Ecological Transition and Territorial Cohesion—which provided technical expertise and experimental data crucial to her research. She is also affiliated with the CRAN (Research Center on Automatic Control of Nancy), within the department of Control, Identification, and Diagnostic. Her role has encompassed model analysis and sensitivity testing, contributing to advancements in hydric modeling for ecological infrastructure.

🔬Research Focus

Her primary research focus revolves around the analysis of hydric models adapted for green roof systems, particularly in understanding how soil properties and meteorological variables influence model behavior. Her work uniquely adapts sensitivity analysis methodologies to handle dynamic outputs, addressing uncertainties inherent in time-dependent environmental parameters. This research is of high relevance to urban sustainability and ecological engineering.

🛠️Skills

Axelle HEGO possesses specialized skills in model analysis, dynamic system modeling, and sensitivity analysis. She is proficient in adapting analytical methodologies to account for uncertainties in environmental parameters. Her expertise lies in integrating engineering control systems with environmental modeling, focusing on soil and meteorological interactions in urban green infrastructure.

🏆Awards

Axelle HEGO has been actively involved in presenting her research at international conferences. Her PhD work has resulted in two proceedings presented at conferences on Automatic Control and Modeling, as well as a dedicated presentation at a Sensitivity Analysis conference. Additionally, she has published a journal paper in the reputable Stochastic Environmental Research and Risk Assessment. She has applied for the prestigious Best Researcher Award under the International Soil Scientist Awards 2025.

📚 Publications

Title: Sensitivity analysis of a green roof model with uncertain time-varying parameter
Year: 2025
Author: Axelle Hego
Journal: Stochastic Environmental Research and Risk Assessment

Conclusion:

Dr. Axelle Hego is a suitable and promising candidate for the Best Researcher Award in the soil science domain. While her academic portfolio is still growing, her contributions demonstrate innovation, interdisciplinary insight, and a clear commitment to sustainability. With continued support and recognition, she is well-positioned to become a leading researcher in environmentally driven systems modeling.

Sophie Sarrassat | Data Science | Best Researcher Award

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Assist. Prof. Dr. Sophie Sarrassat | Data Science | Best Researcher Award

Assist. Prof. Dr. Sophie Sarrassat, London School of Hygiene and Tropical Medicine, United Kingdom

Dr. Sophie Sarrassat is an Assistant Professor in Public Health and Epidemiology at the London School of Hygiene & Tropical Medicine (LSHTM), United Kingdom. With a PhD in Public Health from Pierre & Marie Curie University, she specializes in the evaluation of public health interventions, particularly in maternal, neonatal, and child health across West Africa. Her expertise spans mixed-methods research, randomized controlled trials, and global health. Alongside her academic career, she is a qualified integrative counsellor and psychotherapist (MA, BACP registered), offering mental health support in both English and French. Dr. Sarrassat is actively building bridges between global public health and mental health research.

Profile

Scopus

Summary:

Dr. Sophie Sarrassat is a highly qualified and impactful public health researcher with a strong portfolio of interdisciplinary work in global health and mental health. Her research demonstrates scientific rigor, innovation, and real-world impact. She is well-positioned at an internationally respected institution and maintains a robust publication and teaching profile. Her ability to integrate epidemiology, psychology, and cultural sensitivity makes her research both technically sound and human-centered.

🎓 Education

Sophie Sarrassat holds an impressive array of postgraduate degrees that span both health sciences and psychology. She earned her PhD in Public Health & Epidemiology from Pierre & Marie Curie University in France, along with an MSc in the same field. She also holds a Postgraduate Diploma in Integrative Counselling & Psychotherapy and a Master of Arts in Applied Psychology Research from the University of East London. Additionally, she has undertaken advanced training in Internal Family Systems (IFS) therapy, completing both Level 1 and Level 2 certifications through the IFS Institute. Her foundational studies include a postgraduate diploma in Pharmacy from Rouen University and certificates in counselling skills from LC & CTA in London. Sophie also completed a Kundalini Yoga Level 1 Teacher Training program, highlighting her holistic and integrative approach to health and wellness.

💼Experience

Sophie brings over two decades of combined professional experience in public health research and psychotherapy. Since 2011, she has served as an Assistant Professor in Public Health and Epidemiology at the London School of Hygiene & Tropical Medicine (LSHTM), where she contributes to teaching and supervises MSc students in epidemiology. From 2010 to 2011, she held a research fellow position at Heidelberg University in Germany. Parallel to her academic career, she trained and worked as an Integrative Counsellor and Psychotherapist, beginning as a trainee at The Awareness Centre in London and becoming a registered BACP member in 2021. She currently offers psychotherapy sessions both online and in person, in English and French.

🔬Research Focus

Sophie’s research has primarily focused on the evaluation of public health interventions in maternal, neonatal, and child health across West African countries. Her work includes large-scale, randomized controlled trials and mixed-methods studies funded by leading global health organizations. Key projects include interventions on antenatal corticosteroids, malaria reduction, HIV prevention campaigns, and child survival strategies through mass media and health system strengthening. These studies have informed global health policy and practice, with her findings published in high-impact peer-reviewed journals. More recently, she is expanding her focus into Global Mental Health, combining her dual expertise in epidemiology and psychotherapy.

🛠️Skills

Sophie has developed a diverse skill set in both public health research and psychotherapeutic practice. In research, she is adept in designing mixed-methods surveys, creating quantitative and qualitative data collection tools, training interviewers, and conducting advanced data analysis using STATA. She also has experience with interpretative phenomenological analysis and scientific reporting. In psychotherapy, she applies an integrative, trauma-informed and collaborative approach, blending Internal Family Systems therapy, humanistic modalities, attachment theories, neuroscience-informed methods, and creative techniques such as drawing. Her practice is inclusive, culturally sensitive, and open-ended or time-limited based on client needs.

🏆Awards

Sophie’s work has been supported by esteemed institutions such as the Wellcome Trust, Bill & Melinda Gates Foundation, and UNITAID. While formal individual awards are not listed, her consistent involvement in large-scale, high-impact international research trials and her academic role at LSHTM reflect strong recognition of her contributions to public health and applied psychology.

📚 Publications

Title: The World Health Organization Antenatal CorTicosteroids for Improving Outcomes in preterm Newborns (ACTION-III) Trial
Author(s): WHO ACTION Trials Collaborators
Year: 2024
Journal: Trials

Title: Evaluating the Intensity of Exposure to MTV Shuga, an Edutainment Program for HIV Prevention: Cross-Sectional Study in Eastern Cape, South Africa
Author(s): Babalola, S., Shiferaw, S., Mkhize, N., Sarrassat, S., Farahani, M., Wouters, E.
Year: 2024
Journal: JMIR Formative Research

Conclusion:

Dr. Sarrassat exemplifies the qualities of a top-tier researcher through her evidence-based approach, leadership in global health interventions, and dedication to mentorship and equity. With modest increases in visibility and leadership in mental health research, her profile will only grow stronger. Her interdisciplinary work aligns well with current global health priorities and deserves recognition through this prestigious award.

Fatimah Hameed Naser | Data Science | Best Researcher Award

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Prof. Dr. Fatimah Hameed Naser | Data Science | Best Researcher Award

Prof. Dr. Fatimah Hameed Naser, Al-Qasim Green University, Iraq

Prof. Dr. Fatimah Hameed Naser is a distinguished professor of structural engineering at Al-Qasim Green University, Iraq. With a Ph.D. in Structural Engineering from Babylon University, her research specializes in advanced concrete technologies, fiber-reinforced polymers (FRP), finite element modeling, and sustainable construction materials. She has authored numerous peer-reviewed publications and contributed significantly to civil engineering education and innovation in Iraq.

Profile

Scopus

Orcid

Google Scholar

Summary:

Prof. Dr. Fatimah Hameed Naser is an accomplished structural engineering academic whose research is both technically rigorous and socially relevant. Her contributions to sustainable concrete materials, structural behavior modeling, and civil engineering education highlight her as a strong candidate for the Research for Best Researcher Award. She combines domain-specific knowledge with a commitment to innovation and education in Iraq’s engineering sector.

🎓 Education

Dr. Fatimah Hameed Naser Al-Mamoori holds a distinguished academic background in civil and structural engineering. She earned her Bachelor of Science in Civil Engineering in 2008 from Babylon University, Iraq. She continued her graduate studies at the same institution, completing a Master of Science in Structural Engineering in 2011. Her academic journey culminated in a Ph.D. in Structural Engineering from Babylon University in 2015, further solidifying her expertise in the field.

💼Experience

Dr. Al-Mamoori has served as a researcher and Assistant Professor in Structural Engineering at the College of Engineering, Al-Qasim Green University, Babylon, Iraq, since October 2015. Over the years, she has contributed significantly to academia through both teaching and research. Her academic role involves mentoring students, supervising research, and advancing structural engineering knowledge with a focus on innovative materials and modeling techniques.

🔬Research Focus

Her research primarily focuses on the structural behavior of concrete systems and the use of advanced materials to enhance performance. Key themes in her work include lightweight concrete reinforced with carbon fiber-reinforced polymer (CFRP) bars, the flexural and shear behavior of concrete elements, the incorporation of local materials in structural concrete, and the application of steel and carbon fibers to improve structural integrity. She also explores innovative solutions to issues like concrete behavior in hot climates, the role of industrial and waste fibers in construction, and finite element modeling for structural optimization.

🛠️Skills

Dr. Al-Mamoori possesses a strong portfolio of technical and analytical skills in structural engineering. Her expertise includes fiber-reinforced polymers (FRP), finite element analysis, structural analysis, construction techniques, and the use of finite element modeling. She is highly skilled in civil engineering materials, concrete technologies, reinforced concrete structures, and the evaluation of flexural and shear strength. Her applied knowledge spans both experimental and analytical research methodologies.

🏆Awards

Dr. Al-Mamoori is recognized for her prolific contributions to structural engineering through numerous research publications in peer-reviewed journals and international platforms. Her work has been indexed in Scopus, Publons, Google Scholar, and ResearchGate, reflecting both impact and global visibility. Although specific awards are not listed, her sustained academic and research excellence positions her as a leading researcher in structural engineering in Iraq.

📚 Publications

Title: Torsional Behavior of Concrete-Filled Steel Tubular Members: State of the Mini Art Review
Authors: Mohammed Jassim Soudi Al-Mangoshi (M.J.S.), Ali Hameed Naser Almamoori (A.H.N.), Fatimah H. Naser (F.H.)

Title: Load Capacity of RC Members Subjected to Marine Environment and Rehabilitated with FRP: State of the Art Review
Authors: Mohammed Karar Hadi (M.K.), Ali Hameed Naser Almamoori (A.H.N.), Fatimah H. Naser (F.H.)

Conclusion:

Prof. Dr. Fatimah Hameed Naser is suitable for the Research for Best Researcher Award. Her blend of academic leadership, applied research excellence, and technical innovation aligns well with the award’s objectives. With strategic enhancements in research impact visibility and international recognition, she can further solidify her standing as one of the leading researchers in her field.

Habte Tadesse LIKASSA | Data Science | Best Academic Researcher Award

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Dr. Habte Tadesse LIKASSA | Data Science | Best Academic Researcher Award

Dr. Habte Tadesse Likassa, Arizona State University, United States

Dr. Habte Tadesse Likassa is a versatile and accomplished academic researcher whose work bridges critical areas of biostatistics, medical imaging, and econometrics. He has made notable contributions to methodological innovation and has demonstrated leadership in education and research policy. His career is marked by a commitment to excellence, interdisciplinary collaboration, and applied impact.

Profile

Scopus

Google Scholar

Summary:

Dr. Habte Tadesse Likassa is an accomplished statistician and researcher with strong credentials in applied and theoretical domains. His work is interdisciplinary, spanning healthcare, economics, and statistical methodologies, and is marked by real-world relevance and academic rigor. His leadership roles, technical skills, and international collaborations reinforce his profile as a high-potential researcher. However, strategic focus on publishing in higher-impact journals and increasing citation metrics would strengthen his global scholarly presence.

🎓 Education

Habte T. Likassa holds a PhD in Statistics from the National Taiwan University of Science and Technology, Taiwan, awarded in July 2019. His doctoral research focused on developing new robust methods for outlier and heavy sparse noise detection in high-dimensional images, earning him an A+ grade. He also holds an MSc in Statistics from Addis Ababa University, Ethiopia, where his thesis involved spatial and count regression models for analyzing tuberculosis incidence in North Shoa Zone. He earned his BSc in Statistics from the University of Gondar, Ethiopia, with a research focus on time series modeling for traffic data forecasting. Additionally, he obtained a Higher Diploma in Teaching at Higher Education from Debre Birhan University in 2013.

💼Experience

Dr. Likassa is currently serving as a Postdoctoral Research Scholar at the Department of Biostatistics, College of Health Solutions, Arizona State University, USA, since 2024. His work involves both methodological advancements in biostatistics and applied analysis using high-dimensional datasets. Previously, he held academic appointments across several Ethiopian universities including Addis Ababa University, Ambo University, and Debre Birhan University, serving in various capacities such as Assistant Professor, Lecturer, and Graduate Assistant between 2008 and 2023.

🔬Research Focus

Dr. Likassa’s research interests are diverse and interdisciplinary. His work primarily focuses on the development of robust statistical methods for medical imaging and anomaly detection in high-dimensional data. He is also engaged in survival analysis for heart failure, COVID-19, and cancer patients, as well as econometric modeling of inflation volatility using multivariate GARCH techniques. His research interests include machine learning, deep robust methods, low-rank sparse decomposition such as RPCA and Tensor RPCA, optimization techniques, artificial intelligence, and applied statistical modeling including logistic regression, multivariate analysis, spatial analysis, medical image analysis, and time series forecasting.

🛠️Skills

Dr. Likassa has extensive expertise in data management and advanced statistical modeling. His skills span big data analytics, management of economic and survey claim data, spatial exploratory data analysis, and high-dimensional health and image data analysis. He is proficient in various statistical software including LaTeX, SPSS, SAS, Stata, R, GIS, MATLAB, and Python. He has also delivered numerous training sessions to researchers, postgraduate students, and government professionals on research ethics, statistical methods, and data analysis techniques.

🏆Awards

Dr. Likassa has received several awards for his contributions to research and presentations. In 2024, he delivered a talk at the Arizona Alzheimer’s Consortium. He has received multiple Best Presenter Certificates at international conferences, including a Best Presenter Award in Taiwan, Republic of China.

📚 Publications

  • Title: A Multivariate GARCH Model with Time-Varying Correlations: What Do Inflation Data Show in Ethiopia
    Authors: Likassa, Habte Tadesse; Chen, Dinggeng; Nadarajah, Saralees; Temesgen, Shibru; Gotu, Butte
    Year: 2025
    Journal: Computational Economics

  • Title: Building a Sustainable Future: Investigating the Role and Contributions of Higher Education Institutions Instructors in Promoting Social Sustainability—Empirical Evidence from Ethiopia
    Authors: Tafese, Mestawot Beyene; Kopp, Erika; Likassa, Habte Tadesse
    Year: 2024
    Journal: Education Sciences

Conclusion:

Dr. Likassa is highly suitable for the Best Academic Researcher Award. His breadth of expertise, research innovation, and dedication to academic service make him a commendable choice. Addressing the noted areas for improvement could further enhance his candidacy, but his current achievements already position him as an impactful and deserving researcher on an international scale

Zhiyong Zhang | Data Science | Best Researcher Award

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Dr. Zhiyong Zhang | Data Science | Best Researcher Award

Dr. Zhiyong Zhang, Tianjin university of traditional Chinese medicine, China

Dr. Zhiyong Zhang is a Ph.D. candidate at Tianjin University of Traditional Chinese Medicine, China. His research focuses on the integration of modern analytical techniques and artificial intelligence to enhance quality evaluation and control in Traditional Chinese Medicine (TCM). With expertise in LC/GC-MS, ICP-MS, NIR, and LIBS, he has contributed to over 20 peer-reviewed publications, including 12 SCI-indexed papers as first or co-first author. His work advances real-time, data-driven approaches for TCM manufacturing and herbal product authentication.

Profile

Scopus

Summary:

Dr. Zhiyong Zhang is a rising star in the field of Traditional Chinese Medicine research, demonstrating excellence in combining classical medical knowledge with state-of-the-art analytical and computational techniques. His research is not only scientifically rigorous but also highly relevant to real-world applications in medicine production, safety, and quality assurance. With a growing list of impactful publications and innovative methodologies, he exemplifies the type of interdisciplinary thinker the Research for Best Researcher Award aims to recognize.

🎓 Education

Zhiyong Zhang, born in September 1997 in Huaibei, Anhui, China, is currently a Ph.D. candidate in Traditional Chinese Medicine at the Tianjin University of Traditional Chinese Medicine, under the supervision of Researcher Wenlong Li. He began his doctoral studies in September 2022. Prior to this, he participated in a joint master’s training program at the First Institute of Oceanography, Ministry of Natural Resources, from September 2021 to June 2022, where he worked with Researcher Junhui Chen. Zhang completed his Master of Science in Traditional Chinese Medicine at the Tianjin University of Traditional Chinese Medicine from September 2020 to June 2021. His academic journey began with a Bachelor of Science in Traditional Chinese Medicine Pharmaceutical at the same university, completed between 2015 and 2019.

💼Experience

Dr. Zhiyong Zhang has extensive academic and research experience in the field of Traditional Chinese Medicine (TCM). He is currently pursuing his Ph.D. at Tianjin University of Traditional Chinese Medicine, where he focuses on quality assessment and control of TCM using advanced analytical technologies and artificial intelligence. He previously participated in a joint master’s training program at the First Institute of Oceanography, Ministry of Natural Resources, where he gained interdisciplinary experience in TCM analysis and data integration. He also completed both his Master’s and Bachelor’s degrees in Traditional Chinese Medicine and Pharmaceutical Sciences at Tianjin University of Traditional Chinese Medicine. Over the years, Dr. Zhang has developed strong expertise in applying chemometrics, metabolomics, and spectroscopy-based techniques to improve the reliability and efficiency of TCM research and manufacturing.

🔬Research Focus

Zhiyong Zhang’s research centers on advancing the quality evaluation and control of Traditional Chinese Medicine (TCM) through modern analytical and computational techniques. His interests include machine learning-based quality analysis of TCM using multimodal data, rapid on-site quality control of Chinese herbal materials and processed products, and the development of multidimensional quality evaluation systems integrating metabolomics, elementalomics, and chemometrics. He also focuses on the application of Process Analytical Technology (PAT) for real-time monitoring of critical quality attributes in TCM manufacturing.

🛠️Skills

He is proficient in a wide range of analytical techniques, including Liquid/Gas Chromatography-Mass Spectrometry (LC/GC-MS), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Near-Infrared Spectroscopy (NIR), and Laser-Induced Breakdown Spectroscopy (LIBS). His expertise spans both laboratory-based and field-deployable technologies, enabling high-throughput and real-time analysis of complex herbal matrices.

🏆Awards

Zhiyong Zhang has made significant contributions to the field with over 20 peer-reviewed publications, including 12 SCI-indexed papers where he served as the first author or co-first author. He has also filed three patent applications related to his research innovations in TCM quality control and evaluation technologies.

📚 Publications

1. Journal of Food Composition and Analysis (2025)

Title: Investigating the impact of geographical origin and harvesting season on the quality of Hypericum perforatum L. using LC-MS, GC-MS, and ICP-MS technologies in conjunction with random forest model
Authors: Zhang Zhiyong, Qian Jiahe, Fang Guangpu, Chen Jingchao, Li Wenlong

2. Phytochemical Analysis (2025)

Title: A Machine Learning-Based Approach for the Prediction of Anticoagulant Activity of Hypericum perforatum L. and Evaluation of Compound Activity
Authors: Zhang Zhiyong, Nie Wennan, Zhang Yijing, Zhang Shule, Li Wenlong

Conclusion:

Dr. Zhiyong Zhang is highly suitable for the Research for Best Researcher Award. His achievements to date already place him among the most promising young researchers in the field of TCM. With continued support and opportunities to expand his global and practical influence, he is poised to make even greater contributions to science and society.