Best Researcher Award
Md Sadman Haque
United International University, Bangladesh
Md Sadman Haque is a computer science researcher affiliated with United International University, Bangladesh. His documented research output includes work spanning interpretable machine learning, computer vision datasets, laboratory equipment recognition, and human–computer interaction involving large language models. His publication record reflects an applied orientation toward machine learning methods and datasets designed for practical research and educational contexts. Current bibliometric information records five documents, 11 citations, and an h-index of 2. [1]
Abstract
Md Sadman Haque’s research profile is characterized by applied computer science studies using machine learning and data-driven approaches. Recent publications address interpretable prediction of financial market stress, large-scale image datasets for physics laboratory equipment, chemistry laboratory apparatus recognition, and student perspectives on artificial intelligence-assisted academic search. These works collectively indicate engagement with machine learning, computer vision, dataset development, and human-centered artificial intelligence. [2] [3]
Keywords
Computer Science; Machine Learning; Explainable AI; Deep Learning; Computer Vision; Image Datasets; Artificial Intelligence; Human–Computer Interaction; Financial Analytics.
Introduction
The research record associated with Haque demonstrates the application of contemporary computational techniques to problems in finance, laboratory education, visual recognition, and information-seeking behavior. Rather than being limited to a single application domain, the publications show a common methodological emphasis on machine learning and structured data. Such cross-domain applications are relevant to the broader development of practical and interpretable artificial intelligence systems. [4]
Research Profile
A principal feature of the profile is the use of machine learning for prediction and recognition tasks. The 2026 study on firm-level market stress in the Dhaka Stock Exchange emphasizes interpretability, connecting predictive modeling with the need to understand model outputs. [2] Related research develops image datasets for identifying laboratory equipment, supporting computer vision research and potentially automated educational environments. [3]
Research Contributions
- Development of an interpretable machine learning framework for next-day firm-level market-stress prediction.
- Contribution to large-scale image dataset development for physics laboratory equipment detection.
- Creation of a real-world chemistry laboratory image dataset covering 25 apparatus categories.
- Investigation of university students’ use of LLMs and traditional search engines for academic problem solving.
Publications
“An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange” was published in Discover Artificial Intelligence on 6 September 2026. [2] Other recent works include the Scientific Data article on physics laboratory equipment detection, the chemistry laboratory image dataset study, and the HCI+NLP conference paper examining LLMs and traditional search. [3] [4] [5]
Research Impact
The available bibliometric record lists five documents, 11 citations, and an h-index of 2. [1] The broader significance of the research can also be considered through its application-oriented scope, particularly the development of reusable datasets and interpretable computational methods. Publications in Scientific Data further position dataset construction as a research contribution in its own right. [3]
Award Suitability
The profile is relevant to the Best Researcher Award within the International Forensic Scientist Awards based on its documented publication activity and demonstrated engagement with contemporary computer science research. The combination of explainable machine learning, computer vision, dataset development, and human-centered AI provides a substantive basis for academic recognition, while the assessment remains grounded in the documented research record and bibliometric indicators.
Conclusion
Md Sadman Haque presents an emerging computer science research profile centered on practical applications of machine learning and artificial intelligence. His recent publications demonstrate methodological breadth across financial prediction, computer vision datasets, laboratory equipment recognition, and academic information-seeking. The available record supports consideration for research recognition based on these documented contributions.
External Links
References
- Elsevier. (n.d.). Google Scholar author details: Md Sadman Haque, Author ID 3fMbSl8AAAAJ.
https://scholar.google.com/citations?user=3fMbSl8AAAAJ&hl=en
- Haque, M. S., et al. (2026). An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange. Discover Artificial Intelligence.
https://doi.org/10.1007/s44163-026-02065-7
- Haque, M. S., et al. (2026). Large-Scale Image Dataset for Physics Laboratory Equipment Detection Using Deep Learning Models. Scientific Data.
https://doi.org/10.1038/s41597-026-07839-3
- Haque, M. S., et al. (2025). Real-world chemistry lab image dataset for equipment recognition across 25 apparatus categories. Scientific Data.
https://doi.org/10.1038/s41597-025-05952-3
- Haque, M. S., et al. (2025). Rethinking Search: A Study of University Students’ Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving. Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing.
https://doi.org/10.18653/v1/2025.hcinlp-1.5
- ORCID. (n.d.). Md Sadman Haque — ORCID record.
https://orcid.org/0009-0001-0471-8197
