Faculty profile
Bryar Shareef, Ph.D.
Multimodal and agentic AI for medicine, scientific discovery, and spatial intelligence
I am an Assistant Professor of Computer Science at UNLV and director of the Advanced AI Research Lab. My research develops reliable artificial intelligence that connects medical images, language, biomedical data, and human expertise.
Medical and biomedical AI form the foundation of this work. From that foundation, my group studies multimodal learning, model reliability, scientific discovery, and intelligent systems that can reason in visual and spatial environments.
Research
Connected research areas
Our work begins with healthcare and biomedical challenges, then extends to the methods and intelligent systems needed to address them responsibly.
Multimodal and vision–language AI
Learning from images, text, prompts, and structured clinical information to build grounded and useful AI systems.
Medical image analysis
Robust analysis of ultrasound, MRI, histopathology, and 3D biomedical images for clinically meaningful applications.
Reliable and interpretable AI
Reliability, generalization, uncertainty, explanation, and careful evaluation with experts kept in the decision process.
EHR and clinical data intelligence
Electronic health records, clinical time series, and structured data for early warning and decision support.
3D vision understanding and world models
Representing and reasoning about objects, scenes, motion, and interactions in three-dimensional environments.
Agentic AI, spatial intelligence, and AR/VR
Intelligent agents and immersive systems that reason and act in visual and spatial environments.
AI for scientific and biomedical discovery
Computational pathology, bioinformatics, image–omics integration, and spatial transcriptomics.
AI for drug discovery
Molecular representation, graph learning, candidate prioritization, and data-driven biomedical discovery.
AI for forensic analysis
Computational analysis of craniofacial, dental, imaging, and other multimodal forensic evidence.
Selected work
Recent publications
- 2026 · AI in Emergency Medicine Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: A Scoping Review
- 2026 · PASP Machine Learning-Driven Analysis of kSZ Maps to Predict CMB Optical Depth τ
- 2026 · IEEE EMBC NullBUS: Multimodal Mixed-Supervision for Breast Ultrasound Segmentation
News
Recent updates
IHCA scoping review published
Our completed review of machine-learning models for predicting in-hospital cardiac arrest is published in Artificial Intelligence in Emergency Medicine.
NullBUS accepted at IEEE EMBC 2026
Our work on multimodal mixed-supervision for breast ultrasound segmentation has been accepted for presentation.
Prospective students
Research opportunities in the AAR Lab
Motivated Ph.D., M.S., and undergraduate students interested in medical AI, multimodal learning, reliable AI, or scientific computing are welcome to review the lab's research and application guidance.
Student opportunities