The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Mani Golparvar-Fard sits on this spectrum.
This scientist: 183 publications — 40th percentile
40% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Mani Golparvar-Fard sits on this spectrum.
This scientist: 49 D-Index — 60th percentile
60% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Mani Golparvar-Fard is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research is situated primarily within the field of Engineering, with a particular focus on Civil and Structural Engineering, as well as Building and Construction. Additional subfields in their work include Geology, Radiological and Ultrasound Technology, and Artificial Intelligence.
The primary topics covered in their research include Infrastructure Maintenance and Monitoring, BIM and Construction Integration, 3D Surveying and Cultural Heritage, Occupational Health and Safety Research, Construction Project Management and Performance, Robotics and Sensor-Based Localization, and Manufacturing Process and Optimization.
Recent notable publications by Mani Golparvar-Fard include:
Mani Golparvar-Fard has published frequently in several key venues that include:
Their collaborative work often involves a number of frequent co-authors, such as:
The scientist's work spans several interconnected domains related to the application of advanced technologies and methodologies in construction and civil engineering, with strong emphases on automation, safety, and digital integration within construction processes. Through multiple publications and collaboration, their contributions reflect active engagement with areas such as activity recognition, site safety inspection, 3D mapping, and deep learning for building information modeling.
Youngjib Ham;Kevin K. Han;Jacob J Lin;Mani Golparvar-Fard
Mani Golparvar-Fard;Jeffrey Bohn;Jochen Teizer;Silvio Savarese
Mani Golparvar-Fard;Feniosky Peña-Mora;Silvio Savarese
Mani Golparvar-Fard;Feniosky Peña-Mora;Silvio Savarese
Mani Golparvar-Fard;Feniosky Peña-Mora;Carlos A. Arboleda;SangHyun Lee
Jun Yang;Man-Woo Park;Patricio A. Vela;Mani Golparvar-Fard
Mani Golparvar-Fard;Arsalan Heydarian;Juan Carlos Niebles
Milad Memarzadeh;Mani Golparvar-Fard;Juan Carlos Niebles
Hyungchul Yoon;Hazem Elanwar;Hazem Elanwar;Hajin Choi;Mani Golparvar-Fard
Alex Albert;Matthew R. Hallowell;Brian Kleiner;Ao Chen
Andrey Dimitrov;Mani Golparvar-Fard
Kevin K. Han;Mani Golparvar-Fard
Kevin K. Han;Mani Golparvar-Fard
Mani Golparvar-Fard;Mani Golparvar-Fard;Mani Golparvar-Fard;Feniosky Peña-Mora;Feniosky Peña-Mora;Feniosky Peña-Mora;Silvio Savarese;Silvio Savarese;Silvio Savarese
Andrey Dimitrov;Mani Golparvar-Fard
Matthew M. Torok;Mani Golparvar-Fard;Kevin B. Kochersberger
Youngjib Ham;Mani Golparvar-Fard
Behnam Sherafat;Changbum R. Ahn;Reza Akhavian;Amir H. Behzadan
Hyojoon Bae;Mani Golparvar-Fard;Jules White
Dominic Roberts;Mani Golparvar-Fard
Mani Golparvar-Fard;Feniosky A. Peña-Mora;Silvio Savarese
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