Recording

Recordings are free for event attendees. If you did not attend the virtual event, access the
full series
or
individual session
for a fee.

Summary

Résumé de la présentation

As project expectations rise, thereby raising the level of complexity and interacting requirements, our processes to achieve these goals must adapt and meet these demands. Additionally, these demands sometimes now include performance guarantees during operations. To address these issues, we present a conceptual process for continuous improvement, that is explicitly goal-driven, so that computational methods can help measure progress and automatically recommend actions that maximize progress and minimize effort.

Who's Presenting

Qui présente

Azam Khan

Co-founder and CEO, Trax.GD

Co-founder and CEO, Trax.GD

Biographie

Azam Khan is the Co-founder and CEO of Trax.GD, developing an urban scale symbiotic simulation and visualization platform. He founded the Symposium on Simulation for Architecture and Urban Design (SimAUD) in 2010, is Adjunct Professor of Computer Science at the University of Toronto, and has been the Velux Guest Professor at The Royal Danish Academy of Fine Arts, School of Architecture, and a member of the Technical Advisory Committee of CIFE at Stanford University, a research center for Virtual Design and Construction. Azam received his B.Sc. and M.Sc. in Computer Science at the University of Toronto and his Ph.D. in Computer Science at the University of Copenhagen. Azam was Director of Complex Systems Research at Autodesk where he led a multidisciplinary team of 25 people including researchers, software developers, architects, and engineers.

As project expectations rise, thereby raising the level of complexity and interacting requirements, our processes to achieve these goals must adapt and meet these demands. Additionally, these demands sometimes now include performance guarantees during operations. To address these issues, we present a conceptual process for continuous improvement, that is explicitly goal-driven, so that computational methods can help measure progress and automatically recommend actions that maximize progress and minimize effort.

Gallery

No items found.

Related Articles

No items found.