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.
I finished my PhD thesis at the Chair for Computer Aided Architectural Design (CAAD), ETH Zurich by the end of April 2015. Here, you can take a look at my final PhD presentation. link
From November 2011-April 2015 I worked as a researcher in Singapore-ETH lab, Future Cities Laboratory.
Previously, I studied in Iran and received my B.S and M.S degrees in industrial and systems engineering from Isfahan University of Technology and Tehran Polytechnic respectively.
From May 2015, I am a senior researcher at the chair for Computer Aided Architectural Design (CAAD), ETH Zurich.
Research Interest: The intersection of machine learning, data and engineering systems:
Urban and spatial systems
- Learning physics: Fast and scalable urban water and wind flow emulations
- Urban air pollution modeling and monitoring systems at different scales
- Transportation networks dynamics
- Urban economy and real estate market dynamics
- Urban morphology analysis at the global scale
- City exploration with unconventional data sources
- Multidimensional geo-visualizations
- Remote sensing applications
Other application areas
- Economic networks and systemic risk
- Financial time series forecasting
- Structural engineering and design space exploration
- Manufacturing and supply chain systems
- Natural language modeling applications
- Atmospheric science
Projects portfolio: https://vahidmoosavi.com/projects/
Generic system modeling problems
- Function approximation and manifold learning methods
- Meta-modeling and surrogate models
- Dynamical systems
- Representation learning
- Dimensionality reduction and topology preserving space transformation
Computational modeling and machine learning techniques
- Self-organizing maps
- Markov chains
- Recurrent neural networks
- Convolutional neural networks
- Ensemble methods
History and philosophy of computational urban modeling and simulation techniques
- Life and death of great computational modeling concepts
Research projects for graduate students or those who are interested to collaborate with me:
If you are interested in machine learning and data driven modeling applications, you can contact me. Distance does not matter even if you are not based in Zurich. At the moment I am working on the following topics:
- Air Pollution Modeling at the Global Scale using data from more than 8000 locations
- Multidimensional Urban Exploration: combination of different modes of data including structured data such as census data in US and other unstructured data sets such as satellite images and graphical data from Open Street Map (OSM)
- Learning Physics: How to speed up slow physics-based simulation models (e.g. CFD models in urban wind or water flow models) via techniques such as Recurrent Neural Networks and Convolutional Neural Nets.
- Architectural Indexing of Millions of Buildings from Open Street Map
- Remote Sensing Imagery, Deep Learning and Informal Settlements
- Data Driven Traffic Simulation
- Systemic Risk in Energy Networks