Selected Publication

A nonparametric Bayesian approach is proposed to estimate an integrated travel demand model of work duration and commute mode. The proposed nonparametric model, a generalized additive Gaussian process model, precludes the need to prespecify a functional form. The additive structure of the model enables computational tractability in high dimensions of covariates. Bayesian estimation was adopted for inference to quantify uncertainty using probability distributions.
In Mobil.TUM

Recent Publication

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  • An Integrated Travel Demand Model of Work Duration and Commute-mode Choices: A Flexible Modeling Approach

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Recent & Upcoming Talks

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Recent Post

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Incase you missed it, here is a recording of my talk on Introduction to Probabilisitic Machine Learning at the Las Vegas R & Data Science Meetup groups. I introduced probabilistic machine learning and probabilistic programming with Stan. I discussed the basics of machine learning from a probabilistic/Bayesian perspective and contrasted it with traditional/algorithmic machine learning

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Projects

Automating Hyperparameter tuning

Bayesian optimization provides a principled approach to automate hyperparameter tuning.

Integrated Travel Demand Modeling

Currently, there’s growing research interest in integrated travel demand models.

Latent Travel Patterns from Mobile Phone Data

Currently, there’s growing research interest in integrated travel demand models.

Contact

  • daniel.emaasit@gmail.com
  • 702 895 3701
  • Science and Engineering Building (SEB) Room 240, University of Nevada, Las Vegas, Nevada, USA
  • Monday - Friday 9:00 to 17:00 or email for appointment