Gelareh Vakili | Data Science and Analytics | Innovative Research Award

Posted on by Innovator Awards

 

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/

Mr. Rouhollah Ahmadian – Artificial Intelligence – Best Researcher Award

Posted on by Innovator Awards

Mr. Rouhollah Ahmadian - Artificial Intelligence - Best Researcher Award

Amirkabir university of technology - Iran

Author Profile 

SCOPUS 

ORCID 

🎓 Early academic pursuits

Rouhollah Ahmadian’s journey into computer science began with a strong academic foundation at the university of tabriz, where he earned a bachelor’s degree in computer science. graduating in 2015 with a commendable gpa of 3.3/4 (16.77/20), he ranked among the top 1% of his cohort. his academic curiosity deepened at amirkabir university of technology, where he pursued both his master’s and ph.d. degrees in computer science. excelling in advanced topics like data mining, machine learning, and data analytics, he maintained exceptional gpas, earning recognition as a top performer in his graduate studies.

💼 Professional endeavors

Rouhollah’s career is marked by a rich blend of academic and industry experiences. as a data scientist at norc, amirkabir university of technology, he contributed to impactful projects like license plate recognition. his entrepreneurial spirit shone through his work as a freelance android developer, creating innovative applications across various domains, including municipal automation, real estate, messaging, and green iot systems. his roles at organizations like noor islamic sciences research center and al-zahra society allowed him to develop apps for religious, educational, and journalistic purposes, demonstrating his versatility in android development.

🔍 Contributions and research focus

Rouhollah’s research focuses on leveraging machine learning to solve real-world problems. his projects, such as driver identification using imu data, involved advanced techniques like data augmentation with gans, discrete wavelet transformations, and probabilistic classification. he also integrated sliding window Artificial Intelligence segmentation and probabilistic fusion methods to enhance the accuracy of classification models. his work highlights his dedication to innovation and advancing the field of machine learning for practical applications.

🏆 Accolades and recognition

Throughout his academic journey, rouhollah consistently stood out as a top-performing student. he ranked in the top 1% of his cohorts during both his bachelor’s and master’s programs, earning accolades for his outstanding gpa. his excellence extended beyond academics, as his contributions to diverse projects gained recognition Artificial Intelligence within both academic and professional circles.

🌍 Impact and influence

Rouhollah’s multifaceted work has had a significant impact on various sectors, from education and journalism to real estate and automation. his android applications have improved accessibility, efficiency, and user experiences in these fields. his academic contributions in machine learning have also influenced peers and Artificial Intelligence researchers, enriching the body of knowledge in data science and artificial intelligence.

🛠️ Legacy and future contributions

With a robust foundation in computer science, extensive professional experience, and a passion for innovation, rouhollah ahmadian is poised to leave a lasting legacy in the field of data science. his work in machine learning and android development demonstrates a commitment to creating practical solutions that address pressing challenges. as he continues his research and development efforts, he is set to make further strides in advancing technology and inspiring future scientists.

Notable Publications 

  • Title: Enhancing user identification through batch averaging of independent window subsequences using smartphone and wearable data
    Authors: Rouhollah Ahmadian, Mehdi Ghatee, Johan Wahlström
    Journal: Computers & Security
  • Title: Improved User Identification through Calibrated Monte-Carlo Dropout
    Authors: Rouhollah Ahmadian, Mehdi Ghatee, Johan Wahlström
    Journal: Knowledge-Based Systems
  • Title: Uncertainty Quantification to Enhance Probabilistic-Fusion-Based User Identification Using Smartphones
    Authors: Rouhollah Ahmadian, Mehdi Ghatee, Johan Wahlström, Hadi Zare
    Journal: IEEE Internet of Things Journal
  • Title: Driver Identification by an Ensemble of CNNs Obtained from Majority-Voting Model Selection
    Authors: Rouhollah Ahmadian, Mehdi Ghatee, Johan Wahlström
    Journal: [Book Chapter Title Unknown]
  • Title: Probabilistic Fusion on Sliding Windows of Neural Networks for Spatiotemporal Data Classification
    Authors: Rouhollah Ahmadian, Mehdi Ghatee, Johan Wahlström
    Journal: SSRN