About AI MIND Lab
About AI MIND Lab
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The AI MIND Lab (Artificial Intelligence for Medical Innovation and Novel Discoveries) at UCF brings together computer science and clinical sciences to create intelligent, trustworthy AI for healthcare. Our research spans deep learning, multimodal data fusion, generative and foundation models, and explainable AI, with applications in oncology, cardiology, neuroimaging, pathology, and beyond. By combining cutting-edge computation with clinical collaboration, we aim to advance precision medicine and real-world patient care while training the next generation of AI innovators.
Principal Investigator
Principal Investigator
Pegah Khosravi, PhD
Associate Professor at University of Central Florida (UCF)
Institute for Artificial Intelligence, Department of Computer Science
Department of Clinical Sciences, College of Medicine
Senior Deputy Editor of AI, Journal of Magnetic Resonance Imaging
GoogleScholarGitHubCurrent Academic Research Team
Current Academic Research Team
TBA
PhD Student
COM and IAI, UCF
Federated Learning Project
Sai K. Nallamothu
MSc Research Assistant
COM and IAI, UCF
OncoCertainty Project
Md. Ehsanul Haque
External Research Collaborator,
East West University,
Deep Learning Project
Opportunities at UCF AI MIND Lab
I am recruiting Master’s and Ph.D. students in Computer Science at UCF to join the AI MIND Lab. Our program emphasizes cutting-edge research, strong methodological foundations, and high-quality publications. Ph.D. students are expected to graduate with at least two peer-reviewed conference papers (e.g., NeurIPS, CVPR, MICCAI) and two full-length journal articles in high-impact venues. Ph.D. students typically join the lab through the official UCF Computer Science Ph.D. program and may participate in directed research through CAP7919. For detailed and up-to-date degree requirements, please visit the official UCF Graduate Studies page.
Master’s students may participate in the lab through CAP6908 (Independent Study), which provides structured research experience and direct mentorship. CAP6908 is typically the first pathway for Master’s students interested in joining the lab. Students are expected to define and develop a research project aligned with the lab’s research directions and work toward submission to a top-tier peer-reviewed venue. Projects initiated during the spring semester are typically expected to target venues such as NeurIPS or ICML, while projects initiated during the fall semester generally align with MICCAI or related conferences in medical imaging and AI for healthcare.
Due to the nature of our research, which involves sensitive clinical data and long-term collaborative projects, the lab does not accept volunteer researchers. All students must be formally affiliated with UCF through an academic program or structured research mechanism to ensure appropriate commitment, continuity, and compliance with institutional requirements.
At this time, the lab does not provide RA/TA funding or financial support for new students. Applicants are expected to have independent funding or be supported through their academic program.
At the AI MIND Lab, students are expected to maintain professionalism, attend meetings regularly, meet deadlines, and actively contribute to research. Students who do not meet these expectations may not continue in the lab.
Fall Admission
December 1: Final deadline for international applicants
July 1: Final deadline for domestic applicants
Spring Admission
December 1: Final deadline for domestic applicants
July 1: Final deadline for international applicants
GPU & HPC Resources at AI MIND Lab
At AI MIND Lab, we leverage UCF’s advanced high-performance computing ecosystem to power large-scale medical AI and multimodal learning research. Our primary resource is the UCF ARCC Newton GPU Cluster, equipped with cutting-edge NVIDIA V100 and H100 GPUs and 100 Gb/s InfiniBand networking that enables efficient large-model training and high-throughput experimentation. UCF’s HPC environment also includes the Stokes CPU cluster and a high-speed research network backbone designed for data-intensive workflows.
For storage, we rely on ARCC’s high-performance Lustre file system and the RStore project-based storage platform, both optimized for HPC-grade throughput, fast I/O, and long-term data retention. These systems support collaborative work, large dataset management, and seamless integration with HPC jobs. Secure College of Medicine storage provides dedicated space for IRB-regulated clinical and imaging datasets. Together, these UCF-managed computing and storage resources form a powerful infrastructure that enables our lab to operate at scale and support cutting-edge medical AI research.
Ongoing Projects
At AI MIND Lab, we are committed to advancing AI-driven medical research through collaborations supported by robust data agreements. These agreements ensure compliance with ethical standards, privacy regulations, and institutional guidelines, granting access to high-quality datasets essential for our research. Below are our ongoing projects, a brief description of their goals, and their associated collaborations:
Multimodal AI for Vocal Fold Pathology Detection and Classification: Developing multimodal deep learning models that integrate stroboscopic video, and clinical data to detect, classify, and track vocal fold pathologies.
Vision-Language Models for Cardiovascular Imaging and Clinical Reasoning: Developing scalable multimodal foundation models that jointly learn from cardiovascular images, reports, and clinical context to enable robust representation learning, reasoning, and generalization across cardiac imaging tasks.
Transformer Guided Implicit Neural Diffeomorphic Registration for Brain MRI: Developing a next-generation neuroimaging registration framework.
Longitudinal AI for Early Breast Cancer Detection and Tumor Evolution Modeling: Developing a multimodal deep learning framework that leverages longitudinal mammography to identify early imaging patterns associated with breast cancer before tumors become clinically visible. This work aims to support earlier detection and improve personalized screening strategies.
Pan-Cancer Multimodal AI for Diagnosis and Prognosis: Designing vision-language models that jointly reason across radiology, histopathology, and genomic data to enable accurate diagnosis, subtype classification, and risk prediction across diverse cancer types.
Diagnostic Certainty in Multimodal Medical AI: Developing AI methods to study how diagnostic confidence evolves as information from multiple medical imaging modalities is integrated, with the goal of improving reliability and trustworthiness of clinical AI systems.
Top AI Conferences 2026
🧠 General AI / ML Conferences 2026
🧠 General AI / ML Conferences 2026
ICML: Jul 6-12 | Seoul, South Korea | icml.cc
AAAI: Jan 20-27 | Singapore | aaai.org
CVPR: Jun 3-7 | Colorado, USA | cvpr.thecvf.com
ACL: Jul 2-7 | San Diego, US | 2026.aclweb.org
Asilomar: Oct 25-28 | Pacific Grove, US | asilomarsscconf.org
AISTATS: May 2-5 | Tangier, Morocco | aistats.org
ICLR: Apr 23-27 | Rio de Janeiro, Brazil | iclr.cc
NeurIPS: Dec 6-12 | Sydney, Australia | neurips.cc
IJCAI: Aug 15-21 | Bremen, Germany | ijcai.org
ECCV: Sep 8-13 | Malmö, Sweden | eccv.ecva.net
ICCV: Oct 19-25, 2025 | Honolulu, Hawaii, US | iccv.thecvf.com
🧬 Medical / Healthcare AI Conferences 2026
🧬 Medical / Healthcare AI Conferences 2026
MICCAI: Oct 4-8 | Abu Dhabi, Unite Arab Emirates | miccai.org
MIDL: Jul 8-10 | Taipei, Taiwan | midl.io
ISBI: Apr 8-11 | London, UK | biomedicalimaging.org
AMIA: Nov 15-19, 2025 | Atlanta, USA | amia.org
ML4H: Dec 6-7, 2025 | San Diego, USA | ml4h.cc
EMBC: Jul 26-30 | Toronto, Canada | embc.embs.org
AIME: Jun 7-10 | Ottawa, Canada | aime25.aimedicine.info
AACR: Apr 17-22 | San Diego, USA | aacr.org
RSNA: Nov 29-Dec 3 | Chicago, Illinois, USA | rsna.org
MLHC: Aug 13-14 | Baltimore, MD, USA | mlforhc.org
CHIL: Jun 28-30 | Seattle, WA, USA | chil.ahli.cc
Ways to Connect
You are welcome to meet with me during my weekly office hours across both UCF campuses. To ensure I can give you my full attention, please email to schedule an appointment before your visit.
Tuesdays and Thursdays (8:00 AM - 4:00 PM) - Institute for Artificial Intelligence (IAI), Global Building, Office 319A or Conference Room GB103.
Monday and Wednesdays (8:00 AM - 4:00 PM) - College of Medicine, Lake Nona, Office 410B.
For virtual or ongoing professional connection, LinkedIn is my sole online presence. You are welcome to connect with me.
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