Gelareh Vakili | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

Gelareh Vakili
University of Maryland, Baltimore County
Gelareh Vakili
Affiliation University of Maryland, Baltimore County
Country United States
Scopus ID 60765665400
Documents 1
Subject Area Data Science and Analytics
Event International Innovator Awards
ORCID 0000-0003-3384-3840

Gelareh Vakili is a researcher affiliated with the University of Maryland, Baltimore County in the United States, with a stated subject area of Data Science and Analytics. The researcher is associated with Scopus Author ID 60765665400 and an ORCID identifier that provides a persistent digital identifier for scholarly activities. The supplied researcher record lists one document; citation and h-index values were not provided in the source information for this article.[2]

Abstract

Innovative Research Award profiles the academic and research record of Gelareh Vakili, affiliated with the University of Maryland, Baltimore County, United States. The supplied information associates the researcher with Data Science and Analytics and identifies a Scopus Author ID and ORCID record. Scopus is a major bibliographic and citation database used to index scholarly literature and provide author-level publication information. ORCID provides persistent identifiers intended to distinguish researchers and connect them with their scholarly contributions. On the basis of the supplied record, the profile documents one indexed publication and provides a foundation for describing the researcher’s academic activity without making unsupported claims about citation impact or research excellence.[1]

Keywords

Data Science and Analytics; Data Science; Analytics; Research Innovation; Academic Research; Scholarly Communication; Bibliometrics; Researcher Profile; University of Maryland, Baltimore County; International Innovator Awards; ORCID; Scopus.

Introduction

Data science and analytics encompass methodological approaches for collecting, processing, interpreting, and communicating information derived from data. Contemporary research in these areas commonly integrates statistical reasoning, computational methods, data management, and analytical techniques to address questions across scientific and applied domains. A researcher’s scholarly profile can therefore be considered through several complementary dimensions, including documented publications, subject-area classification, persistent identifiers, institutional affiliation, and measurable bibliographic indicators. [1]

Research Profile

The supplied academic profile places Gelareh Vakili at the University of Maryland, Baltimore County and identifies Data Science and Analytics as the principal subject area. The available bibliographic information lists one document in the supplied Scopus record. Because no citation count or h-index value was supplied, those indicators are intentionally reported as unavailable rather than estimated. This distinction is important because bibliometric measures can vary according to database coverage, publication type, indexing status, and the date on which the record is examined. [1]

Research Contributions

The available information supports describing Gelareh Vakili’s research profile in relation to Data Science and Analytics. However, a detailed assessment of specific methodological contributions requires access to the researcher’s publications, abstracts, datasets, software, or other scholarly outputs. Accordingly, the contribution profile below is limited to areas that can be reasonably connected with the supplied subject classification and bibliographic record. [2]

Publications

The supplied record specifies 1 document associated with the Scopus profile. No publication title, journal, year, authorship details, pages, or DOI were included in the input data. Consequently, no publication-specific bibliographic information or DOI has been inferred for this article. [3]

Research Impact

Research impact may be evaluated using multiple forms of evidence, including citations, scholarly dissemination, adoption of research outputs, collaboration, policy or industry use, and contributions to subsequent research. Bibliometric indicators such as citation counts and h-index values can provide useful quantitative signals, but they should be interpreted in relation to disciplinary norms and the coverage of the database being used. [1]

Award Suitability

The International Innovator Awards provides the stated recognition context for this profile. Based on the supplied information, Gelareh Vakili has a documented affiliation with the University of Maryland, Baltimore County, a research classification in Data Science and Analytics, an indexed Scopus record, and persistent scholarly identifiers. These elements provide verifiable components for an academic recognition profile. [1]

Conclusion

Gelareh Vakili is presented in the supplied academic record as a researcher affiliated with the University of Maryland, Baltimore County and associated with Data Science and Analytics. The available information includes a Scopus Author ID, an ORCID identifier, and one reported Scopus document. These identifiers and bibliographic details provide a basis for documenting the researcher’s scholarly profile.[2]

References

  1. Elsevier. (2026). Scopus author details: Gelareh Vakili, Author ID 60765665400. Scopus.https://www.scopus.com/authid/detail.uri?authorId=60765665400
  2. ORCID. (2026). ORCID record: Gelareh Vakili, ORCID iD 0000-0003-3384-3840. ORCID.https://orcid.org/0000-0003-3384-3840
  3. International Innovator Awards. (2026). Official website of the International Innovator Awards.https://innovatorawards.org/

Dr. Lamine Mbarki – Stochastic and probability – Best Researcher Award

Posted on by Innovator Awards

Dr. Lamine Mbarki - Stochastic and probability - Best Researcher Award

University of Tunis El Manar - Tunisia

Author Profile 

SCOPUS 

Early academic pursuits 🎓

Lamine Mbarki's academic journey began with a strong foundation in mathematics, earning his baccalaureate diploma in 2006. his passion for the subject led him to pursue a diplomate in mathematics and computer sciences from the faculty of sciences of monastir, tunisia, in 2008. he furthered his expertise with a master's degree in mathematics in 2010, followed by a master's of research in harmonic analysis in 2012. his research, supervised by mohamed sifi and dominique bakry, explored markovian operators and hypergroup properties, setting the stage for his ph.d. work. he completed his ph.d. at the faculty of sciences of tunis, focusing on the p(x)-laplacian operator and its associated operators, a significant contribution to the field of differential equations.

Professional endeavors 👨‍🏫

Currently an assistant professor in the mathematics department at the faculty of sciences of tunis, lamine mbarki has been dedicated to academia, imparting knowledge and advancing mathematical research. his teaching and mentorship have influenced many students, particularly in areas such as partial differential equations, fractional calculus, and stochastic analysis. his institutional affiliation with the university of tunis el manar reflects his commitment to academic excellence and research development.

Contributions and research focus 🔬

Dr. Mbarki's research focuses on the mathematical study of partial differential equations, fractional calculus, probability, and dynamical systems. his significant contributions include his work on the hypergroup property and the study of the p(x)-laplacian operator. this research has advanced the understanding of complex mathematical structures and their applications in various scientific fields. his interdisciplinary approach bridges theoretical mathematics with practical applications,  Stochastic and probability particularly in stochastic processes and dynamic systems.

Accolades and recognition 🏅

Throughout his academic career, dr. mbarki has received recognition for his pioneering research in mathematics. his collaborations with esteemed supervisors such as dominique bakry and mounir bezzarga have cemented his reputation as a notable researcher in harmonic analysis and operator theory. his work continues to be  Stochastic and probability cited and utilized by researchers across the globe, particularly in the areas of partial differential equations and probability theory.

Impact and influence 🌍

Dr. Mbarki’s research has had a wide-reaching impact, particularly in advancing the understanding of complex mathematical systems. his work on fractional calculus and stochastic analysis has applications in physics, engineering, and economics, influencing how mathematical models are employed in real-world scenarios. as a  Stochastic and probability teacher and researcher, his contributions have shaped the careers of many students and young researchers, fostering a new generation of mathematical minds.

Legacy and future contributions đź”®

As a dedicated mathematician, dr. mbarki's legacy will be defined by his innovative work in partial differential equations and stochastic processes. his research continues to evolve, and he is expected to contribute further to the fields of fractional calculus and dynamical systems. his future work will likely continue to impact both theoretical mathematics and its applied fields, ensuring that his contributions remain influential for years to come.

Notable Publications 

  1. Title: "A degenerate Kirchhoff-type problem involving variable s(·)-order fractional p(·)-Laplacian with weights"
    Authors: Allaoui, M., Hamdani, M.K., Mbarki, L.
    Journal: Periodica Mathematica Hungarica, 2024, 88(2), pp. 396–411
  2. Title: "Existence and Multiplicity of Solutions for a Class of Kirchhoff–Boussinesq-Type Problems with Logarithmic Growth"
    Authors: Carlos, R.D., Mbarki, L., Yang, S.
    Journal: Mediterranean Journal of Mathematics, 2024, 21(3), 108
  3. Title: "Existence of Multiple Solution for a Singular p(x)-Laplacian Problem"
    Authors: Ghanmi, A., Mbarki, L., Choudhuri, D.
    Journal: Complex Analysis and Operator Theory, 2024, 18(2), 26
  4. Title: "Unified approach to nonlinear Caputo fractional derivative boundary value problems: extending the upper and lower solutions method"
    Authors: Talib, I., Batool, A., Sousa, J.V.D.C., Lamine, M.
    Journal: Journal of Mathematics and Computer Science, 2024, 37(1), pp. 20–31
  5. Title: "Solutions for a Nonlocal Elliptic System with General Growth"
    Authors: Mbarki, L., Tavares, L.S., Sousa, J.V.C.
    Journal: Complex Analysis and Operator Theory, 2023, 17(8), 134

Mr. md Ali Akber – Natural Language Processing – Excellence in Innovation

Posted on by Innovator Awards

Mr. md Ali Akber - Natural Language Processing - Excellence in Innovation

AHSANULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY - Bangladesh

Author Profile

GOOGLE SCHOLAR

🌱 Early academic pursuits

md Ali Akber's academic journey began with a strong foundation in computer science and engineering. in 2023, he earned his bachelor's degree from ahsanullah university of science and technology, bangladesh, with a remarkable gpa of 3.592. his academic curiosity was demonstrated through a thesis focused on natural language processing (nlp), a cutting-edge field at the intersection of linguistics and machine learning. this research solidified his expertise and passion for exploring language technology and computational algorithms, positioning him for a future in academia and research.

đź’Ľ Professional endeavors

since february 2024, md Ali Akber has been serving as a lecturer at daffodil international university (diu) in the department of software engineering. his role encompasses a wide range of academic responsibilities, from teaching theory courses to conducting lab sessions and mentoring students. his teaching portfolio includes key courses like "introduction to software engineering," "data structure lab," and "object-oriented concepts," highlighting his versatility and expertise in guiding the next generation of software engineers.

đź“š Contributions and research focus

md Ali Akber’s research contributions are making waves in the field of natural language processing and personality analysis. his publication on "personality and emotion – a comprehensive analysis using contextual text embeddings" breaks new ground by using machine learning to explore the complex relationship between Natural Language Processing personality traits and emotions through social media analysis. another significant work, "personality prediction based on contextual feature embedding sbert," focuses on automating personality predictions using sentence transformers and machine learning models. his research, blending advanced algorithms with human behavioral studies, showcases his commitment to impactful and innovative research.

🏆 Accolades and recognition

through his research publications in reputable journals and conferences, md ali akber has garnered recognition in the academic community. his work on personality prediction and the explainability of machine learning models in news category classification has been particularly well-received, with his model achieving outstanding Natural Language Processing accuracy, far surpassing contemporary benchmarks. his contributions to machine learning and artificial intelligence are setting a high bar in the field, as seen in his successful publications in prestigious platforms such as the ieee region 10 symposium and the natural language processing journal.

🌍 Impact and influence

md Ali Akber’s work has had a broad-reaching impact, particularly in the application of machine learning to personality prediction and nlp. his research not only contributes to academic knowledge but also offers practical applications, especially in understanding human behavior through digital footprints. by exploring the Natural Language Processing intersection of artificial intelligence and psychology, he is paving the way for more intuitive and responsive human-computer interactions. his teachings and mentorship at diu further amplify his influence by shaping the minds of young engineers.

đź”® Legacy and future contributions

looking ahead, md Ali Akber is poised to continue his work at the frontier of natural language processing and artificial intelligence. his dedication to the explainability of machine learning models reflects a broader commitment to ethical ai development, ensuring that future systems are both transparent and user-friendly. as he progresses in his career, he will likely contribute more groundbreaking research, further advancing the understanding of machine learning's role in human-computer interaction and behavioral analysis.

Notable Publications 

  1. Title: Personality Prediction Based on Contextual Feature Embedding SBERT
    Authors: MA Akber, T Ferdousi, R Ahmed, R Asfara, R Rab
    Journal: 2023 IEEE Region 10 Symposium (TENSYMP), 1-5 (2023)
  2. Title: Personality and Emotion—A Comprehensive Analysis Using Contextual Text Embeddings
    Authors: A Akber, T Ferdousi, R Ahmed, R Asfara, R Rab, U Zakia
    Journal: Natural Language Processing Journal, 100105 (2024)
  3. Title: Interpretability of Machine Learning Algorithms for News Category Classification Using XAI
    Authors: N Tabassoum, MA Akber
    Journal: 2024 6th International Conference on Electrical Engineering and Information (2024)

Dr. S. Pourmohammad Azizi – Time series analysis – Excellence in Innovation

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Dr. S. Pourmohammad Azizi - Time series analysis - Excellence in Innovation

National Taiwan Ocean University - Taiwan

AUTHOR PROFILE 

SCOPUS

GOOGLE SCHOLAR

EARLY ACADEMIC PURSUITS

Seyed Mohammad Esmaeil Pourmohammad Azizi began his academic journey in mathematics, earning a Bachelor's degree from the University of Mazandaran in 2015 with a GPA of 16.64/20. He continued his studies with a Master’s degree at Allameh Tabataba’i University, Tehran, where he graduated in 2018 with a GPA of 17.69/20. Currently, he is pursuing a Ph.D. in Artificial Intelligence at the Department of Electrical Engineering and Computer Sciences at National Taiwan Ocean University, focusing on Smart Tele-Communication.

PROFESSIONAL ENDEAVORS

Pourmohammad Azizi has gained significant professional experience as an algorithm designer for a Fin-tech company in 2020 and 2021. Additionally, he has been actively involved in Forex Trading since 2018. His professional growth is further enriched by academic visits to Lisbon University in Portugal and Guangzhou University in China.

CONTRIBUTIONS AND RESEARCH FOCUS ON TIME SERIES ANALYSIS

Pourmohammad Azizi’s research contributions are extensive and cover a range of topics in machine learning and financial mathematics. His work includes publications on Dynamical System Decomposition of Signal Learning, Deep Learning Detection for Massive MIMO Systems, Time series analysis and Volatility Forecasting using Artificial Neural Networks. He has also explored Neutrosophic Fuzzy Decision-Making and the application of machine learning in financial decision-making.

IMPACT AND INFLUENCE

His research has made significant impacts in the fields of financial mathematics, machine learning, and artificial intelligence. His contributions to Deep Learning Detection with Probabilistic Tabu Search for Massive MIMO Systems Time series analysis and the integration of machine learning in financial decision-making are widely recognized.

ACADEMIC CITATIONS

Pourmohammad Azizi's work is well-cited in the academic community, reflecting the interdisciplinary influence and relevance of his research. His Google Scholar profile indicates a robust citation record, underscoring his academic impact.

LEGACY AND FUTURE CONTRIBUTIONS

Pourmohammad Azizi aims to continue advancing the fields of smart communications, machine learning, and computational mathematics. His ongoing projects and future research endeavors, such as new models for time series forecasting and exploring dynamical systems in machine learning, promise to contribute significantly to these domains.

NOTABLE PUBLICATIONS

Bitcoin volatility forecasting: An artificial differential equation neural network  2023(1)

An Optimized Method for Solving Membership-based Neutrosophic Linear Programming Problems  2022(2)

Hybrid Approach for Forecasting Stock Exchange Index Combining Statistical Methods and Artificial Neural Network  2021(1)

Neutrosophic Fuzzy Decision-Making Using TOPSIS and Autocratic Methodology for Machine Selection in an Industrial Factory  2024

DeepEigen-Tabu: Deep Eigen Network Assisted Probabilistic Tabu Search for Massive MIMO Detection  2024