Laura Barnes headshot
LB

Laura Barnes

Associate Professor
Unit: School of Engineering and Applied Science
Department: Department of Systems and Information Engineering
Office location and address
Olsson 101b
151 Engineer's Way
Charlottesville, Virginia 22903
Education
B.S. Computer Science, Texas Tech University, 2003
M.S. Computer Science, University of South Florida, ​2007
Ph.D. Computer Science, University of South Florida, 2008
Biography

Laura Barnes is an assistant professor in Systems and Information Engineering. She directs the Sensing Systems for Health Lab which focuses on designing impactful, technology-enabled solutions for improving health and well-being. She received her Ph.D. degree in computer science from the University of South Florida. Laura’s work has been funded by the National Institutes of Health, National Institute of Aerospace, US Army, and private foundations.

SCH: INT: Collaborative Research: Multiscale Modeling and Intervention for Improving Long-Term Medication Adherence in Context
Source: U.S. NIH Cancer Institute
September 01, 2019 – August 31, 2023
Effectiveness of interpretation training to reduce anxiety: Evaluating technology-based delivery models and methods to reduce attrition
Source: U.S. NIH Institute of Mental Health
July 01, 2017 – April 30, 2022
EN-SE-1483- Phase II - REaDI Sense
Source: Lockheed Martin Corporation
September 27, 2019 – February 28, 2022
REaDI Sense
Source: Lockheed Martin Corporation
March 07, 2018 – February 28, 2022
Real-time Monitoring and Modeling of Symptoms and Adverse Events in Lung Cancer Patients Receiving Oral Targeted Therapies for Tumors with EGFR mutations or ALK Rearrangements
Source: Pfizer Inc.
December 20, 2019 – December 31, 2021
A Novel Computational Framework for Prediction of Severe and Response to Therapy
Source: Jeffress Memorial Trust
June 30, 2016 – August 30, 2018
EN-SE Big Data Analytics Approaches to Real-Time Monitoring and Assessment of Pilot Cognitive State
Source: National Institute of Aerospace Associates, Inc.
October 01, 2016 – May 31, 2018
EN-SE Feasibility of Virtual Agent Cervical Cancer Education for Hispanic Farmworkers-YR2
Source: San Diego State University Research Foundation
May 01, 2014 – April 30, 2017
EN-SE Feasibility of Virtual Agent Cervical Cancer Education for Hispanic Farmworkers-YR2
Source: San Diego State University Research Foundation
May 01, 2014 – April 30, 2017
EN-SE Big Data Analytics Approaches to Real-Time Monitoring and Assessment of Pilot Cognitive State
Source: National Institute of Aerospace Associates, Inc.
July 01, 2015 – November 09, 2016
EN-SE Expanding Virtual Machine Introspection to Achieve Debugging Transparency
Source: Massachusetts Institute of Technology
January 01, 2014 – September 30, 2016
EN-SYS Predictive Multi-Sensor Data Fusion Techniques for Advanced Coordination-of-Care in the ICU - Fellowship for Nicholas Napoli
Source: Virginia Space Grant Consortium
June 01, 2014 – May 31, 2016
EN-SE The Analysis and Documentation of Experimental Data from the "CAER Experiment 2014"
Source: Center for Advanced Engineering and Research
March 09, 2015 – May 17, 2015
EN-SE Feasibility of Virtual Agent Cervical Cancer Education for Hispanic Farmworkers
Source: San Diego State University Research Foundation
August 01, 2013 – April 30, 2014
EN-SE Feasibility of Virtual Agent Cervical Cancer Education for Hispanic Farmworkers
Source: San Diego State University Research Foundation
March 01, 2013 – April 30, 2014
SYS 4021: Linear Statistical Models
Credits: 4
This course shows how to use linear statistical models for analysis in engineering and science. The course emphasizes the use of regression models for description, prediction, and control in a variety of applications. Building on multiple regression, the course also covers principal component analysis, analysis of variance and covariance, logistic regression, time series methods, and clustering. Course lectures concentrate on the theory and practice of model construction while laboratories provide a series of open-ended problem solving situations that illustrate the applicability of the models. Prerequisite: SYS 3060, APMA 3120, and major in systems engineering.
SYS 4053: Systems Design I
Credits: 3
A design project extending throughout the fall semester. Involves the study of an actual open-ended situation, including problem formulation, data collection, analysis and interpretation, model building for the purpose of evaluating design options, model analysis, and generation of solutions. Includes an appropriate computer laboratory experience. Prerequisite: SYS 3021, 3060, and fourth-year standing in the Systems Engineering major.
SYS 4054: Systems Design II
Credits: 3
A design project extending throughout the spring semester. Involves the study of an actual open-ended situation, including problem formulation, data collection, analysis and interpretation, model building for the purpose of evaluating design options, model analysis, and generation of solutions. Includes an appropriate computer laboratory experience. SYS 4053 and fourth-year standing in Systems Engineering major.
SYS 4995: Supervised Projects in Systems Engineering
Credits: 1–6
Independent study or project research under the guidance of a faculty member. Offered as required. Prerequisite: As specified for each offering.
SYS 6021: Statistical Modeling I
Credits: 3
This course shows how to use linear statistical models for analysis in engineering and science. The course emphasizes the use of regression models for description, prediction, and control in a variety of applications. Building on multiple regression, the course also covers principal component analysis, analysis of variance and covariance, logistic regression, time series methods, and clustering. Course lectures concentrate on theory and practice.
SYS 6097: Graduate Teaching Instruction
Credits: 1–12
For master's students.
APMA 6430: Statistics for Engineers and Scientists
Credits: 3
Analyzes the role of statistics in science; hypothesis tests of significance; confidence intervals; design of experiments; regression; correlation analysis; analysis of variance; and introduction to statistical computing with statistical software libraries. Prerequisite: Admission to graduate studies.
SYS 6582: Selected Topics in Systems Engineering
Credits: 1–3
Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.
SYS 6993: Independent Study
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
SYS 6995: Supervised Project Research
Credits: 1–12
Formal record of student commitment to project research under the guidance of a faculty advisor. Registration may be repeated as necessary.
DS 6999: Independent Study
Credits: 1–12
Graduate-level independent study conducted under the supervision of a specific instructor(s)
CPE 7993: Independent Study
Credits: 1–3
Detailed study of graduate course material on an independent basis under the guidance of a faculty member
SYS 8995: Supervised Project Research
Credits: 1–12
Formal record of student commitment to project research for Master of Engineering degree under the guidance of a faculty advisor. Registration may be repeated as necessary.
SYS 8999: Non-Topical Research, Masters
Credits: 1–12
Formal record of student commitment to master's research under the guidance of a faculty advisor. Registration may be repeated as necessary.
SYS 9997: Graduate Teaching Instruction
Credits: 1–12
For doctoral students.
SYS 9999: Dissertation
Credits: 1–12
For doctoral students.
CPE 9999: Non-Topical Research, Doctoral Dissertation
Credits: 1–12
Formal record of student commitment to doctoral research under the guidance of a faculty adviser. May be repeated as necessary.