activity
20112022
most citedSmartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains

63 citations · 128 across the 6 of their papers we have counts for

collaborators

6 papers

stat.AP2022★ 3 cited

The Content of Statistics and Data Science Collaborations: the QQQ Framework

Ilana M. Trumble, Jessica L. Alzen, Leanna L. House +1

For today's applied statisticians and data scientists, collaboration is a reality. Statisticians (and data scientists) may collaborate with domain experts across academic fields, i…

cs.CY2020

Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study

Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha +2

The paper develops models for modeling the availability of bikes in the San Francisco Bay Area Bike Share System applying machine learning at two levels: network and station. Inves…

cs.CY2020★ 44 cited

Modeling bike availability in a bike-sharing system using machine learning

Huthaifa I. Ashqar, Mohammed Elhenawy, Mohammed H. Almannaa +3

This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost)…

cs.LG2020★ 63 cited

Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains

Huthaifa I. Ashqar, Mohammed H. Almannaa, Mohammed Elhenawy +2

This paper develops a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. This paper also enhances cla…

stat.AP2019

The Value of Summary Statistics for Anomaly Detection in Temporally-Evolving Networks: A Performance Evaluation Study

Lata Kodali, Srijan Sengupta, Leanna House +1

Network data has emerged as an active research area in statistics. Much of the focus of ongoing research has been on static networks that represent a single snapshot or aggregated…

stat.AP2011★ 18 cited

Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy

Leanna L. House, Merlise A. Clyde, Robert L. Wolpert

We present a novel nonparametric Bayesian approach based on Lévy Adaptive Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix Assisted Laser Desorption…