Dr. Menglin Dai | Resources | Best Researcher Award

Dr. Menglin Dai, Resources at Peking University, China

Menglin Dai is a researcher specializing in applied computer vision for stock modeling, currently working as a postdoctoral researcher at Peking University. Menglin earned a PhD from the University of Sheffield, focusing on characterizing English residential housing using street view imagery and deep learning techniques. Their doctoral research involved developing deep learning models to automatically recognize building components and generate detailed building models, contributing to large-scale urban environment datasets. Menglin also holds a Master of Engineering (Hons.) degree in Civil Engineering from the University of Sheffield, where they graduated with distinction. Their academic and professional experience spans urban built environment modeling, structural engineering research, and hydraulic modeling. Menglin has contributed to several peer-reviewed journal articles and conferences, and serves as a reviewer for multiple scientific journals. They possess strong technical skills in programming and data analysis, including Python, Matlab, and various machine learning libraries.

Professional Profile:

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Summary of Suitability for Best Researcher Award

Dr. Menglin Dai has demonstrated exceptional academic and research capabilities in the field of applied computer vision and sustainable urban infrastructure modeling. His doctoral work at the University of Sheffield focused on leveraging deep learning techniques to characterize English residential housing stock using street-view imagery, a novel and impactful contribution to the fields of civil engineering, data science, and urban planning. His postdoctoral research at Peking University further extends this work, incorporating advanced machine learning techniques to develop high-resolution models of built environment material stock, with international applications such as the Danish Built Environment Stock Model and contributions to the Global Secondary Resource Stocks Report.

🎓 Education

• PhD in Applied Computer Vision for Stock Modelling
  University of Sheffield, UK (Sept 2018 – Mar 2023)
  Thesis: Characterising English Residential Housing Using Street View Capture and Deep Learning Techniques
  (Supervisors: Dr. Danielle Densley Tingley & Prof. Martin Mayfield-Tulip)

• Master of Engineering (Hons.) in Civil Engineering (Distinction)
  University of Sheffield, UK (Sept 2013 – July 2017)
  Dissertation: The Effect of Connectors Behaviour on Cold-formed Steel Structural Elements
  (Supervised by Dr. Jurgen Becque)
  Awarded: Faculty Undergraduate Scholarship, Sheffield Undergraduate Research Experience (SURE) bursary, Sheffield Graduate Award

• Foundation Year in Science and Engineering (Distinction)
  Sheffield International College, UK (Dec 2012 – Aug 2013)

💼 Work Experience & Research

• Postdoctoral Researcher – High-Resolution Built Environment Stock Modelling
  Peking University, China (Mar 2023 – Present)
  • Developed ML methods for high-resolution building material stock characterization
  • Contributed to ‘Global Secondary Resource Stocks Report’ and Danish Built Environment Stock Model
  • Session chair, ISIE SEM/AP 2024 conference

• PhD Researcher – Residential Building Feature Characterisation
  University of Sheffield, UK (Sept 2018 – Mar 2023)
  • Developed deep learning models for building component recognition from urban images
  • Automated LoD3-level building model generation from point cloud data

• Researcher – Connector Behaviour on Structural Elements
  University of Sheffield, UK (June 2016 – May 2017)
  • Experimental and finite element modeling of cold-formed steel structural connectors
  • Developed post-processing algorithms for digital image correlation (DIC)

• Graduate Teaching Assistant
  University of Sheffield, UK (Oct 2018 – June 2022)
  • Assisted tutorials and exam marking for engineering sustainability courses

• Hydraulic Modeller Internship/Graduate Program
  RPS Group, UK (2016 & 2017)
  • Built hydraulic city models, site surveys, and data communication

🏆 Achievements & Awards

• 🏅 Sheffield Graduate Award

• 🎓 Faculty Undergraduate Scholarship for Academic Achievement

• 🔬 Sheffield Undergraduate Research Experience (SURE) bursary

• 🥇 Gold Duke of Edinburgh Award

• 🎸 Level 10 Amateur Guitar Certificate

• 🏔 Mount Kilimanjaro Certificate of Completion

• 🚗 Driving License (China)

Publication Top Notes:

Residential building facade segmentation in the urban environment Cited: 66

A scalable data collection, characterization, and accounting framework for urban material stocks Cited: 51

Estimating energy consumption of residential buildings at scale with drive-by image capture Cited: 24

Component-level residential building material stock characterization using computer vision techniques Cited: 9

Scalable residential building geometry characterisation using vehicle-mounted camera system Cited: 7

Conclusion

Dr. Menglin Dai is an outstanding candidate for the Best Researcher Award, reflecting academic distinction, technical innovation, and global relevance. His pioneering work in AI-driven urban stock modeling, his high-impact publications, and his commitment to advancing research on sustainable built environments make him highly deserving of this recognition. Awarding Dr. Dai would honor not only his individual excellence but also highlight the future of data-driven sustainable development.