Iro Armeni is completing her PhD at Stanford University, conducting interdisciplinary research between Civil Engineering and Machine Vision. Her area of focus is on automated semantic and operational understanding of buildings throughout their life cycle using visual data, toward establishing a seamless interaction between the physical world and its digital representation. Prior to enrolling in the PhD program, Iro received an MSc in Computer Science (Ionian University-2013), an MEng in Architecture and Digital Design (University of Tokyo-2011), and a Diploma in Architectural Engineering (National Technical University of Athens-2009). She is the recipient of the 2019 ETHZ PostDoctoral Fellowship, the 2017 Google PhD Fellowship on Machine Perception, and the 2009 Japanese Government (MEXT) scholarship.

Dr. Angela Dai is a Junior Research Group Leader at the Technical University of Munich where she leads the 3D Understanding group. Dr. Dai's research focuses on understanding how the 3D world around us can be modeled and semantically understood. Previously, she received her PhD in computer science from Stanford in 2018 and her BSE in computer science from Princeton in 2013. Her research has been recognized through a ZDB Junior Research Group Award, an ACM SIGGRAPH Outstanding Doctoral Dissertation Honorable Mention, as well as a Stanford Graduate Fellowship.

Nathan Jacobs earned a Ph.D. in Computer Science at Washington University in St. Louis (2010). Since then, he has been a Professor of Computer Science at the University of Kentucky. Dr. Jacobs' research area is computer vision; his specialty is developing learning- based algorithms and systems for processing large-scale image collections. His current focus is on developing techniques for mining information about people and the natural world from geotagged imagery, including images from social networks, publicly available outdoor webcams, and satellites. His research has been funded by NSF, NIH, DARPA, IARPA, NGA, ARL, AFRL, and Google.

Yubin Kuang's a co-founder of Mapillary, the street-level imagery platform that scales and automates mapping with computer vision and machine learning. He has been focusing on research and development of large-scale and robust algorithms for generating accurate map data from images. His main research interests are optimization and deep learning, and their applications in object recognition, large-scale structure from motion, visual relocalization, and sensor localization problems. He received his BSc degree from Nanjing University, China. And he holds a MSc degree in Computer Science and a PhD degree in mathematics from Lund University, Sweden.