College of Engineering Paul E. Torgersen Award Winners

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  • Patient-Centered Usability Evaluation of LymphaVibe: A Novel Device for Upper-Extremity Lymphedema Management
    Thomas, Leah Rebecca (Virginia Tech, 2025-04-14)
    I co-invented the LymphaVibe in 2021 as an undergrad and have continued leading the research as a graduate student. It started as a sketch on a napkin and I have designed multiple prototypes, the software controlling the device, the circuity, and have led the patient-centered design efforts in our research.
  • Systems to Transform Interdisciplinary Graduate Education: An Ecological Systems Analysis of STEM Graduate Students’ Longitudinal Interdisciplinary Identity-Based Motivation
    Webb, Margaret (Virginia Tech, 2025-04-14)
    This research reveals how academic systems shape STEM graduate students' interdisciplinary scholarly development, crucial for addressing complex societal challenges like climate change and disaster resilience. Integrating ecological systems and identity theories, it identifies developmental trajectories and system interaction patterns that explain barriers to developing the interdisciplinary scholars society urgently needs.
  • Optimizing Natural Disaster Recovery: Dynamic Modeling of the Evolution of Habitation
    Montilla-Peña, Héctor (Virginia Tech, 2025-04-14)
    This research expands the Disaster Habitation Model by introducing a classification framework for Critical Internal Infrastructure Systems (CIIS) and developing a custom database to calibrate the three key procedural delays. This work also enhances model accuracy and looks to support data-driven mitigation policy development for post-disaster habitation recovery.
  • Enhancing AI-Clinician Interaction: Building Trust to Improve Patient Outcomes
    Nassarian, Elham (Virginia Tech, 2025-04-14)
    This research systematically reviews interpretable machine learning (IML) and explainable AI (XAI) in healthcare, proposing a Responsible Clinician-AI Collaboration Framework. It introduces a novel three-level interpretability process and step-by-step roadmap to enhance AI-clinician communication, fostering trust and improving decision-making in clinical decision support systems (CDSS).
  • Experimental Evaluation of Combat Helmet Performance in Mitigating Blast Loading on the Head
    Nelson, Allison (Virginia Tech, 2025-04-14)
    This study is one of few to investigate the underwash effect experimentally and the first to evaluate the effect of transverse axis rotation on under-helmet blast loading. This work will improve the understanding of current protective equipment efficacy during blast and inform the development of future combat helmet designs.
  • Frequency Interception and Manipulation Vulnerabilities in Myoelectric-Computer Interface Signal Transmission
    Szczesniak, Emma (Virginia Tech, 2025-04-14)
    Despite extensive research on neural interfaces, limited studies address whether vulnerabilities exist within their framework and how these devices are secured. This work presents a cybersecurity outlook on neural interface signal transmission to encourage safety and data protection while assessing the impact of neural interface integration on the human condition.
  • Preventing Unintended Data Access: Information Flow Control in eBPF
    Dimobi, Chinecherem (Virginia Tech, 2025-04-14)
    We: 1) Identify the balance between allowing access to sensitive data and preventing leakages in kernel extensions. 2) Design techniques to assign sensitivity labels and policies to inputs/outputs of kernel extensions, propagating them during execution. 3) Implement a system that distinguishes between safe and malicious kernel extensions that access sensitive data.
  • Design and Control of a Structurally Elastic Humanoid Robot
    Herron, Connor (Virginia Tech, 2025-04-14)
    This research focuses on uncovering and addressing control and state estimation challenges of a full-sized humanoid robot with 3D-printed structural components. These structural components are designed to be elastic, meaning the parts provide a certain natural human-like compliance, but presents with whole-body stability and estimation challenges for dynamic behaviors.