Binyang Song
1145 Perry Street
(MC 0118)
Blacksburg, VA 24061
For Prospective PhD Students: I have funded GRA positions for students who have strong background in Artificial Intelligence and Machine Learning, Data-Driven Design, or other related fields, and are interested in Multimodal Learning, Generative Design,Human-AI Collaboration, and their applications in engineering design, optimization, and complex problem solving. If you are interested in this position, please email me your resume and (unofficial) transcripts.
Short Bio: My research lies at the intersection of design methodology and theory, machine learning, and human-AI collaboration to boost engineering design and innovation and complex problem solving. Particularly, I focus on leveraging multimodal learning and generative modeling to foster the generation, evaluation, and optimization of engineering designs represented by different data modalities, such as text, images, sketches, voxels, and point clouds. In terms of human-AI collaboration, I study if, how, and why collaborating with AI reshapes the human problem-solving process. On this basis, my research aims to shed light on the design of AI systems to enhance the interactive attributes and trust enablers of AI, in order to promote human-AI collaboration. My lab is dedicated to leveraging multi-modal data for enhancing data-driven human-AI collaboration in the realms of engineering design and innovation.
Research Areas:
Main area: Data-Driven Human-AI Collaboration for Engineering Design and Complex Problem Solving
- Artificial Intelligence and Machine Learning
- Multimodal Learning
- Generative Design
- Design Optimization
- Human-AI Collaboration
- AI for Design and Design for AI
- Ph.D., Engineering Product Development, Singapore University of Technology and Design, 2019
- M.S., Automotive Engineering, Tsinghua University, 2014
- B.Tech., Automotive Engineering, Tsinghua University, 2011
- Assistant Professor, Industrial and Systems Engineering, Virginia Tech, 2023 - present
- Postdoctoral Associate, Mechanical Engineering, Massachusetts Institute of Technology, 2021 - 2023
- Postdoctoral Researcher, Engineering Design, Penn State, 2019 - 2021
- ISE 5984: Artificial Intelligence for Systems Engineering
Binyang Song, Chenyang Yuan, Frank Permenter, Nikos Arechiga, Faez Ahmed. Data-driven Car Drag Coefficient Prediction with Depth and Normal Renderings. Journal of Mechanical Design. (Under review)
Binyang Song, Qihao Zhu, Jianxi Luo. Human-AI Collaborative Innovation in Design. 18th International Design Conference, 2024. (Under Review)
Premith Kumar Chilukuri, Binyang Song, Sungku Kang, Ran Jin. Generating Optimized 3D Designs for Manufacturing Using a Guided Voxel Diffusion Model. International Manufacturing Science and Engineering Conference (MSEC2024), 2024. (Under Review)
Nikos Arechiga, Frank Permenter, Chenyang Yuan, Binyang Song. Drag-guided Diffusion Models for Vehicle Image Generation. Generative Design Workshop, NeurIPS, 2023. (Accepted)
Binyang Song, Hanqi Su, Faez Ahmed. Multi-modal Machine Learning for Vehicle Rating Predictions using Image, Text, and Tabular Data. Journal of Computing and Information Science in Engineering. (Under review)
Kristen Edwards, Binyang Song (Co-first), Jaron Porciello, Mark Engelbert, Carolyn Huang, Faez Ahmed. ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development. Journal of Mechanical Design. (Accepted)
Binyang Song, Rui Zhou, Faez Ahmed. Multi-modal Learning in Engineering Design: a Review and Future Directions. Journal of Computing and Information Science in Engineering, 2023. (Accepted)
Binyang Song, Scarlett Miller, Faez Ahmed. AEML: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluation. Journal of Mechanical Design. Journal of Mechanical Design, 145(4) (2023): 0414105.
Binyang Song, Joshua Gyory, Guanglu Zhang, Gary Stump, Nicolas Soria Zurita, Corey Balon, Simon Miller, Michael Yukish, Jonathan Cagan, Christopher McComb. Decoding the Agility of Human-Artificial Intelligence Hybrid Teams on Complex Problem Solving. Design Studies, 79(3) (2022).
Shuo Jiang, Serhad Sarica, Binyang Song, Jie Hu, Jianxi Luo. Patent Data for Engineering Design: A Critical Review and Future Directions. Journal of Computing and Information Science in Engineering. 22(6) (2022).
Binyang Song, Nicolas Soria Zurita, Hannah Nolte, Harshika Singh, Jonathan Cagan, Christopher McComb. When Faced with Increasing Complexity: The Effectiveness of AI Assistance for Drone Design. Journal of Mechanical Design, 144(2) (2021): 021701.
Serhad Sarica, Binyang Song, Jianxi Luo, Kristin Wood. Idea Generation with Technology Semantic Network. Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM), 35(3) (2021): pp265-283.
Zhang, G., Zurita, N.F.S., Stump, G., Song, B., Cagan, J. and McComb, C., 2021. Data on the design and operation of drones by both individuals and teams. Data in Brief, 36, p.107008.
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”