Keynotes

We had the priviledge of hosting a diverse group of engaging speakers:

Noah Snavely

Dr. Noah Snavely is an associate professor in the Computer Science Department at Cornell Tech interested in computer vision and computer graphics, and a member of the Cornell Graphics and Vision Group. He also works at Google Research in NYC. His research interests are in computer vision and graphics, particularly in 3D understanding and depicting scenes from images. He is the recipient of a PECASE, a Microsoft New Faculty Fellowship, an Alfred P. Sloan Fellowship, and the SIGGRAPH Significant New Researcher Award.

Daniel Aliaga

Daniel Aliaga holds BS in Computer Science, honors thesis (with Andy van Dam) and magna cum laude, from Brown University. Subsequently, obtained a MS (with Henry Fuchs) and a PhD degree (with Anselmo Lastra, Fred Brooks, and Dinesh Manocha) in Computer Science from UNC Chapel Hill. Worked at Nokia/Lucent/AT\&&T Bell Labs (with Ingrid Carlbom) and at Princeton University as a researcher (with Tom Funkhouser). He joined Purdue in 2003, co-founding the Computer Graphics and Visualization Laboratory (CGVLAB). Dr. Aliaga has held visiting professor positions at ETH Zurich Information Architecture and also ETH Computer Science, INRIA Sophia-Antipolis, and KAUST in Saudi Arabia. After finishing high school (Colegio Santa Maria), Daniel immigrated from Lima, Peru and is the first in his family and relatives to hold a PhD.

Francis Engelmann

Dr. Francis Engelmann is a PostDoctoral Researcher at ETH Zurich with Prof. Marc Pollefeys, and a visiting researcher at Google Zurich. Francis’ research focuses on large-scale 3D scene understanding for modeling interactive spaces and generating novel environments. Previously, he completed his PhD in computer science under the supervision of Prof. Bastian Leibe at the computer vision group of RWTH Aachen University. His research has been recognized through the ETH Zurich Career Seed Award and the ETH AI Center Postdoctoral Fellowship.

Matthias Strandfest

Matthias Standfest is an architect with main interests directed towards understanding the geometric impact on architecture performance models using machine learning methods. He received his Ph.D. from ETH Zurich with Dr. Gerhard Schmitt and as a guest at FCL Singapore with Prof. Dr. Ludger Hovestadt, he has balanced method development for mesh-based deep learning techniques with the application of these tools to predict urban simulation results in real-time. He is the curator of Swiss Dwellings — the World’s largest dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality, and geometric analysis.