Molla Hafizur Rahman
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Lecturer (Assistant Professor)
Faculty Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Molla Hafizur Rahman's research focuses on computational approaches to understanding and predicting human behavior in design processes. His work utilizes advanced machine learning techniques, including recurrent neural networks and reinforcement learning, to model sequential design decisions and cognitive competencies. Rahman investigates the transferability of design knowledge and explores methods for representing design thinking through embedding techniques to cluster design behaviors. He has published on topics such as predicting design actions using data-driven reward formulations and modeling student designers' cognitive abilities in computer-aided design environments. His scholarship metrics include an h-index of 6, with 10 total publications and 132 citations. Rahman collaborates with Darya L. Zabelina at the University of Arkansas at Fayetteville, with whom he has co-authored one publication.
Metrics
- h-index: 6
- Publications: 10
- Citations: 138
Selected Publications
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Empirical evidence and computational assessment on design knowledge transferability (2024)
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A Reinforcement Learning Approach to Predicting Human Design Actions Using a Data-Driven Reward Formulation (2022)
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Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors (2021)
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MODELLING AND PROFILING STUDENT DESIGNERS’ COGNITIVE COMPETENCIES IN COMPUTER-AIDED DESIGN (2021)
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Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks (2021)
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Predicting human design decisions with deep recurrent neural network combining static and dynamic data (2020)
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A Deep Learning Based Approach to Predict Sequential Design Decisions (2019)
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A Computer-Aided Design Based Research Platform for Design Thinking Studies (2019)
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Automatic Clustering of Sequential Design Behaviors (2018)
Collaboration Network
Top Collaborators
- Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks
- Empirical evidence and computational assessment on design knowledge transferability
- MODELLING AND PROFILING STUDENT DESIGNERS’ COGNITIVE COMPETENCIES IN COMPUTER-AIDED DESIGN
- A Reinforcement Learning Approach to Predicting Human Design Actions Using a Data-Driven Reward Formulation
- Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors
- Predicting Sequential Design Decisions Using the Function-Behavior-Structure Design Process Model and Recurrent Neural Networks
- MODELLING AND PROFILING STUDENT DESIGNERS’ COGNITIVE COMPETENCIES IN COMPUTER-AIDED DESIGN
- Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors
- Empirical evidence and computational assessment on design knowledge transferability
- A Reinforcement Learning Approach to Predicting Human Design Actions Using a Data-Driven Reward Formulation
- MODELLING AND PROFILING STUDENT DESIGNERS’ COGNITIVE COMPETENCIES IN COMPUTER-AIDED DESIGN
- MODELLING AND PROFILING STUDENT DESIGNERS’ COGNITIVE COMPETENCIES IN COMPUTER-AIDED DESIGN
- MODELLING AND PROFILING STUDENT DESIGNERS’ COGNITIVE COMPETENCIES IN COMPUTER-AIDED DESIGN
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