Alfredo Garcia

About

Professor Alfredo Garcia is a faculty member in the Department of Industrial & Systems Engineering at Texas A&M University. He holds the Mike and Sugar Barnes Professorship III in Industrial & Systems Engineering. His research interests include game theory and dynamic optimization, with applications in electricity and communication networks. Dr. Garcia has contributed to the understanding of driver responses to automation failures, distributed networked learning with correlated data, incentive mechanisms for electricity markets, and distributed non-convex optimization, among other topics. His work focuses on developing advanced mathematical and computational methods to address complex problems in engineering systems and networks.

Research topics

• Computer Science
• Artificial Intelligence
• Reliability engineering
• Pure mathematics
• Geometry
• Mathematical analysis
• Medicine
• Combinatorics
• Biology
• Physics

Selected publications

• Disease spread coupled with evolutionary social distancing dynamics can lead to growing oscillations

  2021 60th IEEE Conference on Decision and Control (CDC) · 2021 · 22 citations

  If the public’s adherence to social distance measures remained steady during an outbreak, the number of cases would have a single peak followed by a sharp decline according to standard epidemiological models. Nonetheless, during COVID-19 the initial rise and fall in the number of cases followed new waves of cases in many localities. In this paper, we explore a standard susceptible-exposed-infected-recovered (SEIR) epidemiological model coupled with an individual be-havior response model that mod…

  DOI

• Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference

  Frontiers in Neurorobotics · 2024-03-21 · 18 citations

  articleOpen access

  Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adapt…

  PublisherOA PDFDOI

• Modeling Driver Responses to Automation Failures With Active Inference

  IEEE Transactions on Intelligent Transportation Systems · 2022 · 15 citations

  Automated vehicle (AV) technologies promise to improve traffic safety and reduce driver workload in the near future. However, most current implementations have limited capabilities and require transition of control between the vehicle and the human driver during automation failures. For this reason, models of driver behavior have been widely studied to assist the design and development of AV technologies. Recent works have shown that driver behavior models grounded in human cognitive information…

  DOI

• Decentralized Riemannian Gradient Descent on the Stiefel Manifold

  International Conference on Machine Learning · 2024 · 9 citations

  We consider a distributed non-convex optimization where a network of agents aims at minimizing a global function over the Stiefel manifold. The global function is represented as a finite sum of smooth local functions, where each local function is associated with one agent and agents communicate with each other over an undirected connected graph. The problem is non-convex as local functions are possibly non-convex (but smooth) and the Steifel manifold is a non-convex set. We present a decentraliz…

• Active Inference Models of AV Takeovers: Relating Model Parameters to Trust, Situation Awareness, and Fatigue

  Human Factors The Journal of the Human Factors and Ergonomics Society · 2024-11-01 · 5 citations

  articleSenior author

  ObjectiveOur objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness.BackgroundControl transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems…

  PublisherDOI

Recent grants

• Collaborative Research: Unlocking Capacity for Wireless Networks Using Cooperative and Cognitive Techniques

  NSF · $40k · 2010–2012

• Smart Markets for Black-box Capacity Allocation

  NSF · $218k · 2018–2020

• CIF: Small: Dynamic Pricing of Interference in Cognitive Radio Networks

  NSF · $436k · 2010–2014

Frequent coauthors

• Mingyi Hong

  28 shared

• Natalia Fabra

  9 shared

• Jorge Barrera

  University of Virginia

  9 shared

• Shi Pu

  8 shared

• Ceyhun Eksin

  8 shared

• Anthony D. McDonald

  University of Wisconsin–Madison

  7 shared

• Robert L. Smith

  University of Michigan–Ann Arbor

  7 shared

• Lingzhou Hong

  7 shared

Labs

• Wm Michael Barnes '64 Department of Industrial & Systems Engineering at Texas A&M UniversityPI

Education

• D.E.A Automatique et Informatique Industrielle, Laboratoire d'analyse et d'architecture des systèmes

  Université Paul Sabatier

  1992

• Ingeniero Eléctrico, Ingenieria Electrica

  Universidad de los Andes

  1990

Similar researchers at Texas A&M University

• Satish Bukkapatnamh-40
• Alaa Elwanyh-37
• Sergiy Butenkoh-35