Fwd: RI PhD Thesis Defense - Ceci Morales

[Artur Dubrawski]

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