Speaker: Vahid Moosavi, Postdoctoral Researcher at ETH
https://vahidmoosavi.com/about/

Topic:
Machine Learning Across Disciplines

Schedule:
6:00pm - 6:30pm - ODSC Intro
6:30pm - 7:20pm - Talk
7:20pm - 7:30pm - Q&A
7:30pm - 8:00pm - Networking and Apero

Bio:
Dr. Vahid Moosavi is a systems engineer with long-standing experience in machine learning techniques and their applications in architectural design and urban and spatial modeling. Parallel to researching and teaching at ETH, he has been conducting several applied machine-learning projects on a wide range of topics including structural design, urban traffic, air pollution, real estate market and urban flood risk. Currently, he is also working on his book "Data-Driven Modeling for Engineering Applications: An Orthogonal View to Classical Scientific Modeling.

Abstract:
Machine Learning and Big Data together offer a universal way of looking at the world phenomena, which is radically different than the classical expert based disciplinary research. This new approach of computational modeling has inverted the classical notion of expertise from “having the answers to the known questions” to “learning to ask good questions”, where the answers can always be found with an appropriate level of modeling skills. While trying to categorize machine learning applications, in this talk I will show the results of some of our ongoing data-driven projects in different domains such as structural design and from finding, planetary analysis of urban form and density, real estate market, urban air
pollution and urban flood risk estimations using deep learning as surrogate to computational fluid dynamics.

ODSC Links:
• Get free access to more talks like this at LearnAI:
https://learnai.odsc.com/
• Facebook: https://www.facebook.com/OPENDATASCI/
• Twitter: https://twitter.com/odsc & @odsc
• LinkedIn: https://www.linkedin.com/company/open-data-science/
• West Conference Oct 29 - Nov 1: https://odsc.com/california
• Europe Conference Nov 19 - 22: https://odsc.com/london

>>> Register here <<<