Daniel Emaasit

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I am a PhD student of Transportation Engineering doing research in Urban Mobility & Planning, Travel Demand Forecasting & Modeling, Urban Data Analytics, Highway Safety and Traffic Operations from the Transportation Research Center (TRC) at the University of Nevada Las Vegas (UNLV). Broadly, my research interests involve the development of probabilistic/Bayesian machine learning methods for high-dimensional data, with applications to Urban Mobility, Transport Planning, Highway Safety, & Traffic Operations.

machine learning bayesian data science big data transport planning united nations africa

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Daniel Emaasit

Apache® Spark™ MLlib: From Quick Start to Scikit-Learn

Building Robust, Adaptive Streaming Apps with Spark Streaming

Structuring Spark: DataFrames, Datasets, and Streaming

H2O Machine Learning and Kalman Filters for Machine Prognostics

The Joys of Clean Data with Matt Dowle

Data mining with caret package

H2O World - Generalized Low Rank Models - Madeleine Udell

H2O World - Migrating from Proprietary Analytics Software - Fonda Ingram

Intro to Apache Spark

H2O 3 REST API Overview

H2O World - Top 10 Deep Learning Tips & Tricks - Arno Candel

H2O World - Sparkling Water - Michal Malohlava

H2O World - GLM - Tomas Nykodym

H2O World - Machine Learning for non-data scientists

H2O World - PAAS: Predictive Analytics offered as a Service - Prateem Mandal

SparkR + Zeppelin

H2O World - What's New in H2O with Cliff Click

H2O World - Ensembles with Erin LeDell

Building a REST Job Server for Interactive Spark as a Service

Applying the R Language to BI and Real Time Applications