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Data Wrangling: Management, Sharing, and Use of Design and Manufacturing Data


The digitalization of manufacturing and the technologies associated with Industry 4.0 has led to an explosion in unstructured data across the entire product lifecycle, including engineering design and manufacturing activities, which are embodied in the emerging “digital thread” and corresponding “digital twin” of the product. These technologies expose rich information that can be used to achieve data-driven (re)design of products and engineering, support continuous improvement of manufacturing operations, and enhance product development practices. 

This webinar is conferred to begin the conversation for data sharing, interoperability, and use cases toward collaborative data-driven engineering design and manufacturing research. In this webinar we invite data stakeholders to present on topics of interest, announce a data wrangling JCISE special issue, and form a community of interest around data topics.

Local Organizing Committee 

  • Vincenzo Ferrero – NIST, 
  • Chris McComb – CMU, 
  • William Bernstein – AFRL, 
  • Binil Starly – NCSU, 
  • Nicholas Alexander – PSU, 
  • Timothy W. Simpson - PSU


Time (Eastern Standard, UTC-4) Topic  Speaker
11:00 - 11:10 AM Introduction Moderator
11:10 -11:25 AM

The Creation of TechNet

Jianxi Luo, SUTD

11:25 - 11:40 AM


Robert Juhanson, GrabCAD

11:40 - 11:55 AM 

The ABC Dataset and Related Research

Daniele Panozzo, NYU

11:55 AM -12:10 PM

Fusion 360 and Related Research

Daniele Grandi, Autodesk

12:10 - 12:25 PM

Data Sensitivity Detection and Tagging

Dan Berrigan, AFRL

12:25 - 12:40 PM

FAIR Data Principles

Yan Lu, NIST

12:40 PM - 1:00 PM

Panel Session / Outro


Speaker Bios 

Additional speakers biographies to be added. 


Jianxi Luo


Jianxi Luo is a tenured Associate Professor with SUTD, Director of Data-Driven Innovation Lab. and former Director of SUTD Technology Entrepreneurship Programme. Prof. Luo holds a PhD in Engineering Systems (Technology Management and Policy track) and S.M. degree in Technology Policy from Massachusetts Institute of Technology, and M.S. in Automotive and B.E. in Thermal Engineering from Tsinghua University. He had been a faculty member at New York University (2011-2012), visiting scholar at Columbia University (2011-2012) and University of Cambridge (2005). He was Chair of INFORMS Technology Innovation Management & Entrepreneurship Section (2015-2016). He is currently Associate Editor of Design Science, Associate Editor of Artificial Intelligence for Engineering Design, Analysis and Manufacturing – AI EDAM, Department Editor (Emerging Technologies) of IEEE Transactions on Engineering Management, Guest Editor of Technovation, and editorial board member of Research in Engineering Design.

Robert Juhanson


Robert Juhanson, MSc in Mechanical Engineering with valuable experience in Software Development. Product Owner in GrabCAD Tallinn office together with development teams building and maintaining GrabCAD products including the Community.

Daniele Panozzo


Daniele Panozzo is an Associate Professor of Computer Science at the Courant Institute of Mathematical Sciences in New York University. Prior to joining NYU he was a postdoctoral researcher at ETH Zurich (2012-2015). Daniele earned his PhD in Computer Science from the University of Genova (2012) and his doctoral thesis received the EUROGRAPHICS Award for Best PhD Thesis (2013).  He received the EUROGRAPHICS Young Researcher Award in 2015, the NSF CAREER Award in 2017, and a Sloan Research Fellowship in 2020. Daniele’s research group is leading the development of libigl (, an award-winning (EUROGRAPHICS Symposium of Geometry Processing Software Award, 2015) open-source geometry processing library, polyfem , a simple C++ and Python finite element library, and wild meshing (, a 2D and 3D robust meshing library. Daniele initiated the Graphics Replicability Stamp, which is an initiative to promote reproducibility of research results and to allow scientists and practitioners to immediately benefit from state-of-the-art research results. His  research interests are in digital fabrication, geometry processing, geometric deep learning, and discrete differential geometry.

Daniele Grandi

Autodesk Research 

Daniele Grandi is a Sr. Research Engineer at Autodesk Research, focusing on design methods and working to further the machine understanding of mechanical design problems. Daniele is interested in finding ways to capture design knowledge, understand design intent, and support design exploration by leveraging machine learning methods to create the next generation of design tools.


Created October 27, 2021, Updated November 5, 2021