CS&E Announces 2026-27 Doctoral Dissertation Fellowship (DDF) Award Winners

Three Ph.D. students working with CS&E professors have been named Doctoral Dissertation Fellows for the 2026-27 school year. The Doctoral Dissertation Fellowship is a highly competitive fellowship that gives the University’s most accomplished Ph.D. candidates an opportunity to devote full-time effort to an outstanding research project by providing time to finalize and write a dissertation during the fellowship year. The award includes a stipend of $25,000, tuition for up to 14 thesis credits each semester, and subsidized health insurance through the Graduate Assistant Health Plan.

CS&E congratulates the following students on this outstanding accomplishment:

• Nuredin Ali Abdelkadir (Advisor: Stevie Chancellor)
• Shelby Ziccardi (Advisor: Stephen Guy)
• Hunmin Lee (Advisor: Catherine Qi Zhao)
   

Nuredin Ali Abdelkadir

Advisor: Stevie Chancellor
Thesis title: Understanding the Impact of Social Media and Supporting the Mental Health of Digital Data Workers

Abstract: Content on social media has the potential to influence our peace, social stability, and mental health due to the extreme nature of what is sometimes posted on these digital platforms. While platforms are used to share and experience positive things, there is a dark side to them. My research reveals that platforms' neglect of their digital data workers, particularly content moderators, can lead to social instability in marginalized communities and result in associated secondary mental health trauma in these workers. My final chapter focuses on building interventions to alleviate the mental trauma of these essential, yet “hidden” digital data workers.

Shelby Ziccardi

Advisor: Stephen Guy
Thesis title: Scalable Characterization of Human Movement from Video Data: Methods and Applications in Clinical Settings

Abstract: Understanding the way humans move their bodies can provide insight into neurologic and physical health.Video recording is an accessible method of capturing (a)typical patterns of movement. Existing methods to characterize motion quality from video are resource-intensive, limited in scope, or unscalable to large datasets. This dissertation develops algorithms for scalable, video-based movement analysis using statistics and deep learning to address these limitations. These methods will be tested for validity in healthy and clinical populations (e.g. cerebral palsy). This work has the potential to increase the efficacy of clinical practice and lead to earlier detection of motor dysfunction.

Hunmin Lee

Advisor: Catherine Qi Zhao
Thesis title: Human–AI Co-Adaptation: A New Paradigm for Stable Neural Control

Abstract: Current neural interfaces require constant recalibration as biological signals drift from sensor placement changes, muscle fatigue, and metabolic shifts. My dissertation solves this through two innovations: human–AI co-adaptation, where human users and AI models learn together via continuous feedback, and noninvasive peripheral nerve sensing that captures stable, intention-level motor commands before muscle translation. Preliminary results show sustained 90%+ decoding accuracy across 50+ days with 300ms response times. This work reframes motor decoding as a bidirectional learning partnership rather than one-sided AI adaptation, advancing practical neural control for prosthetics, assistive robotics, and human-computer interaction.