Data-Driven Decision-Making DEPARTMENT OF ENGINEERING MANAGEMENT AND SYSTEMS ENGINEERING Data-Driven Decision-Making Use Decision Science to develop human-centered approaches for big data and autonomous systems. Grid Modernization, Model and Simulate Socio-technical Systems Perform risk, cost-benefit, uncertainty, sensitivity analysis Predict impacts of behavioral interventions on system Quantify Human Components of Complex Systems Measure attitudes, risk perceptions, and perceptual abilities in the context of new technology and data access Test Decision-Making Interventions Design and assess randomized control trials to test human- computer interfaces, risk communications, and trainings in the lab and field Evaluate Program Impacts Use quasi-experimental techniques to evaluate the impact of government and industry programs to facilitate evidence- based decision-making PoC: Casey Canfield, PhD Assistant Professor, Engineering Management & Systems Eng. canfieldci@mst.edu; https://www.caseycanfield.com/ BS Engineering: Systems, Franklin W. Olin College of Engineering PhD Engineering & Public Policy, Carnegie Mellon University Autonomous Vehicles, Human-Machine Teams Microgrid Operation Technology Process People Smart Communities, Data Governance Keywords Human Systems Integration, Evidence-Based Decision-Making, Randomized Control Trials, Smart Cities, Grid Modernization, Automation, Risk Analysis, Policy Analysis, Stakeholder Engagement Recognitions NSF Graduate Research Fellowship, 2013-2016 Society for Risk Analysis Security & Defense Student Merit Award, 2015 Best Proposal, DOE Better Buildings Case Competition, 2014 CEC Research