Open to research collaborations

Issam Alzouby

PhD Student, UNC Charlotte  / 

I am a PhD student at UNC Charlotte. My research focuses on generative models for human motion synthesis and perception. I develop AI systems for controllable, physically plausible motion generation. Currently exploring how language-guided learning can enable natural movement in humanoid robotics, and real-time digital twins across physical and virtual environments.

Email me Curriculum Vitae GitHub
Charlotte, NC AI4Health Center issamalzouby.com
t2m · seq[8] Generated motion sequence
Affiliations
UNC Charlotte AI4Health Center Duke Heart Center UNC School of Medicine Stanford University Stanford HAI City of Charlotte iRepairCLT
Selected publications

Peer-reviewed work

Co-authored with collaborators at the UNC School of Medicine and Duke Heart Center. Full list and conference presentations on the research page.

Is Artificial Intelligence Saving Lives? A Meta-Analysis of Real-World Clinical Impact

Chouffani El Fassi, S., Ngan, Z., Alzouby, I., et al.

Meta-analysis examining the real-world clinical impact of AI interventions across healthcare settings.

In revision after peer review · Nature Communications

Gaps and Opportunities in the Medical AI Market

Alzouby, I., Chouffani El Fassi, S., Shahrour, L., et al.

Comprehensive analysis of current gaps and future opportunities in the medical AI industry.

Under review · Nature Medicine

Computer Algorithms to Optimize Organ Donation After Circulatory Death (DCD)

Chouffani El Fassi, S., Alzouby, I., Yüksel, I., Khan, T., et al.

Novel algorithms designed to improve organ donation outcomes after circulatory death.

Under review · Nature Medicine

AI Tool to Time ECMO for SVC Syndrome Relief: A Use Case in Nonseminomatous Germ Cell Tumor Resection

Chouffani El Fassi, S., Khoury, L., Alzouby, I., Haithcock, B.

AI-based tool for optimizing ECMO timing in nonseminomatous germ cell tumor resection cases.

Under review · Nature Medicine
Recent

News

  • Submitted my first first-author technical AI paper.

  • Began a second summer as an AI instructor, spending the summer at Stanford University.

  • Presented research at the Charlotte AIForward Symposium.

  • Accepted a PhD offer at UNC Charlotte.

  • 1st place and Global Nominee at the 2025 NASA Space Apps Challenge for Raptor RAG, a retrieval-augmented generation system built on NASA's space biology corpus.

  • Began my first summer as an AI instructor with Stanford's AI4ALL program.

  • Graduated from UNC Charlotte with a B.S. in Computer Science.

  • Awarded Best Oral Presentation in Cardiac Surgery at the 62nd Eastern Cardiothoracic Surgical Society meeting.

  • Began my academic journey as an undergraduate research assistant at the UNCC AI4Health Center.

Selected work

Systems I have built

A few representative projects. The full archive covers research systems, clinical tools, robotics, and hardware.

MMM-272 text-to-motion results
Research system

MMM-272: Enhanced Text-to-Motion Generation

State-of-the-art text-to-motion generation with 272-dimensional representation, achieving superior animation quality for game engines.

FID 0.093PyTorchSMPL
1st place NASA Space Apps Challenge project
NASA Space Apps 2025

Raptor RAG — Space Biology Retrieval

Built Raptor RAG: an advanced retrieval-augmented generation system designed around NASA's space biology data for intelligent ingestion and retrieval.

Global nomineeLLMsVector search
Organ donation algorithm visualisation
Duke & UNC research

DCD Organ Donation Algorithm

An algorithm projecting a 94% improvement in organ-donation success while reducing ICU workload, developed with the Duke Heart Center.

+94% successHealthcare AI

Let's build something that matters

I'm always glad to talk about motion generation, medical AI, or collaborating on research. The fastest way to reach me is email.

ialzouby@charlotte.edu LinkedIn GitHub