Academic Profile : Faculty
Asst Prof Binyang Song
Assistant Professor, School of Mechanical & Aerospace Engineering
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Dr. Song Binyang joined the School of Mechanical and Aerospace Engineering (MAE) at Nanyang Technological University (NTU) in October 2024 as an Assistant Professor in the Robotics & Design Cluster. She earned her bachelor’s degree and master’s degree in Automotive Engineering from Tsinghua University in Beijing, China, in 2011 and 2014, respectively. She completed her Ph.D. in Engineering Product Development at Singapore University of Technology and Design in 2019. Before joining us, Dr. Song Binyang gained postdoctoral experience at the Pennsylvania State University (2019-2021) and Massachusetts Institute of Technology (2021-2023), respectively. From August 2023 to August 2024, she served as an Assistant Professor at Virginia Tech.
Her research focuses on applied artificial intelligence (AI) and human-AI collaboration for engineering design, with particular interests in "AI for Design" - using data-driven design techniques, multimodal learning, and generative modeling to create innovative designs across various representation formats (e.g., text, sketches, images, and 3D models), and "Design for AI" – which involves human factor design of AI to enhance AI's collaborative potential in design settings.
Prior to joining MAE, Dr. Song gained extensive experience in data-driven design and human-AI collaboration for engineering design. Her research in data-driven design spans areas such as network analysis for extracting design knowledge, multimodal learning for design evaluation, and generative modeling for design creation and optimization. In human-AI collaboration, her work focuses on forming human-AI hybrid teams and exploring the impact of AI on human behavior and team effectiveness. Her team’s work intersects mechanical systems, engineering design, applied AI and machine learning, and human-computer interaction. Currently, her research aims to develop generalizable AI-based design methods and advance human-AI teaming in designing complex systems, leveraging AI to transform engineering design.
Her research focuses on applied artificial intelligence (AI) and human-AI collaboration for engineering design, with particular interests in "AI for Design" - using data-driven design techniques, multimodal learning, and generative modeling to create innovative designs across various representation formats (e.g., text, sketches, images, and 3D models), and "Design for AI" – which involves human factor design of AI to enhance AI's collaborative potential in design settings.
Prior to joining MAE, Dr. Song gained extensive experience in data-driven design and human-AI collaboration for engineering design. Her research in data-driven design spans areas such as network analysis for extracting design knowledge, multimodal learning for design evaluation, and generative modeling for design creation and optimization. In human-AI collaboration, her work focuses on forming human-AI hybrid teams and exploring the impact of AI on human behavior and team effectiveness. Her team’s work intersects mechanical systems, engineering design, applied AI and machine learning, and human-computer interaction. Currently, her research aims to develop generalizable AI-based design methods and advance human-AI teaming in designing complex systems, leveraging AI to transform engineering design.
Dr. Song's research centers on applied artificial intelligence (AI) and human-AI hybrid teaming in engineering design, with particular emphasis on:
Data-Driven Design
Multimodal Learning
Generative Modeling
Human-AI Design
Large Language Models
Network Analysis and Graph Theory
Design Representation
Data Analytics
Complex System
Data-Driven Design
Multimodal Learning
Generative Modeling
Human-AI Design
Large Language Models
Network Analysis and Graph Theory
Design Representation
Data Analytics
Complex System
Awards
• Paper of Distinction, 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE): “Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings”
• Paper of Distinction, 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE): “ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development”
• Reviewers’ Favorite, 2021 International Conference on Engineering Design: “The Effects of Artificial Intelligence Agents on Team Communication During Solving an Interdisciplinary Drone Design Problem”
• Paper of Distinction, 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE): “ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development”
• Reviewers’ Favorite, 2021 International Conference on Engineering Design: “The Effects of Artificial Intelligence Agents on Team Communication During Solving an Interdisciplinary Drone Design Problem”