REMINDER: RI PhD Thesis Defense - Ceci Morales

[Artur Dubrawski]

This happens tomorrow!

On Wed, Oct 8, 2025 at 5:15 PM Artur Dubrawski <awd at cs.cmu.edu> wrote:
>
> Dear Autonians,
>
> Please mark your calendars and join Ceci in celebration of her
> doctoral thesis defense on Tuesday next week!
>
> Cheers,
> Artur
>
> ---------- Forwarded message ---------
> From: RI PhD Program Manager <ri-phd-manager at andrew.cmu.edu>
> Date: Wed, Oct 8, 2025 at 4:19 PM
> Subject: RI PhD Thesis Defense - Ceci Morales
> To: RI People <ri-people at andrew.cmu.edu>
>
>
> RI EVENT CALENDAR
>
> Who: Ceci Morales
>
> Date: Tuesday, October 14th
>
> Time: 2:00PM ET
>
> Location: NSH 4305
>
> Zoom Link: https://cmu.zoom.us/j/8982402813
>
> Title: Embodied Artificial Intelligence for Emergency Care in
> Unstructured Environments
>
> Abstract:
>
> In mass casualty events and resource-constrained scenarios, limited
> responder capacity leads to preventable deaths. Time is of the essence
> particularly in severe trauma: the sooner individuals receive care,
> the higher their chances of survival. Yet a single responder can only
> manage a few patients simultaneously, leaving others unattended. This
> thesis addresses this capacity constraint by developing intelligent
> robotic systems that serve as medical force multipliers, enabling
> effective emergency response when casualties outnumber available help.
>
> This work presents two embodied Artificial Intelligence (AI) platforms
> for emergency medical response in unstructured field environments. The
> first performs multipatient automated assessment, which uses
> contactless multimodal sensors to identify qualitative (e.g., wounds,
> amputations, hemorrhage, respiratory distress) and quantitative vital
> signs (e.g., heart rate) to rapidly assess and prioritize the most
> critically injured. The second automates fluid resuscitation,
> targeting hemorrhage, the leading cause of preventable death in
> trauma. The pipeline comprises multiple stages: vessel localization
> and segmentation, visualization and uncertainty quantification for
> safe decision-making, bifurcation detection for anatomically-informed
> needle placement, and real-time needle tracking.
>
> To address the scarcity of training data in emergency medicine
> robotics, this research embeds expert clinical knowledge and applies
> weak supervision techniques, enabling robust performance with limited
> labeled examples. All algorithms execute in real time on
> resource-constrained platforms, with key components designed to adapt
> to changing environmental conditions.
>
> This thesis contributes to autonomous medical systems and offers new
> methodologies for developing AI solutions for life-critical
> applications in unstructured environments where traditional
> data-driven approaches may fail. By augmenting human responders, we
> show how robotic systems can expand treatment capacity when it matters
> most, potentially saving lives that would otherwise be lost.
>
> Thesis Committee:
>
> Artur Dubrawski, Chair
>
> Jean Oh
>
> Fernando de la Torre Frade
>
> Daniel McDuff, Google
>
> Laura Brattain, University of Central Florida
>
>
> Document Link