Mohammad Emtiyaz Khan

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Bilinear models of count data with Poisson distribution are popular in applications such as matrix factorization for recommendation systems, modeling of receptive fields of sensory neurons, and modeling of neural-spike trains. Bayesian inference in such mo ...

In this paper we present a novel emulator of a building simulator for the simulation-assisted design of high performance buildings. Our emulator is based on Gaussian-Process (GP) regression models. Such non-linear models are better suited than linear model ...

Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution. In such situations, existing methods usually resort to a diagonal approxi ...

We consider online predictions of vote results, where regions across a country vote on an issue under discussion. Such online predictions before and during the day of the vote are useful to media agencies, polling institutes, and political parties, e.g., t ...

Simulation is an informative tool to plan the design and renovation of engineering systems, but its use requires accurate inputs from the users. Inputs such as the unknown future operating conditions might be expected in a system’s lifetime, e.g., the futu ...

Mohammad Emtiyaz Khan

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Variational Gaussian Inference for Bilinear Models of Count Data

Gaussian-Process-Based Emulators for Building Performance Simulation

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Online Collaborative Prediction of Regional Vote Results

Planning for a Changing Climate without Accurate Predictions