Medical Imaging
MRI, CT, low-dose CT, histopathology, image classification, segmentation, and explainable computer vision.
Assistant Professor · Florida A&M University
Carlos A. Theran-Suarez, Ph.D.
Advancing machine learning, deep learning, computer vision, medical imaging, multimodal analytics, and scalable AI systems for healthcare and societal applications.
Profile
About
Dr. Carlos A. Theran-Suarez is an Assistant Professor of Computer and Information Sciences at Florida A&M University and Director of the Applied Computational Imaging and Multidomain Analytics Lab (ACIMA Lab), focused on biomedical and geospatial applications. His research integrates machine learning, deep learning, computer vision, remote sensing, natural language processing, multimodal data analysis, and scalable computing to address challenges in medical imaging, neurodegenerative disease assessment, cancer detection, geospatial analysis, explainable AI, and trustworthy model evaluation.
He also has extensive teaching experience in advanced undergraduate and graduate courses in computer science and data science, including programming languages, algorithms, databases, machine learning, generative AI, and data science. In addition, he has served as a faculty instructor in study-abroad programs in Italy, teaching computing and data science in an international and interdisciplinary environment International Collaboration.
Education
- Ph.D., Computer and Information Science and Engineering, University of Puerto Rico at Mayagüez
- M.S., Scientific Computing, University of Puerto Rico at Mayagüez
- B.S., Mathematical Sciences, Universidad del Atlántico
Core Research Areas
Research Laboratory
Applied Computational Imaging and Multidomain Analytics Lab
Biomedical and Geospatial Applications
The ACIMA Lab develops intelligent imaging, machine learning, and multimodal analytics methods that transform complex biomedical, clinical, geospatial, sensor, and textual data into interpretable and actionable knowledge.
Healthcare Analytics
AI methods for neurodegenerative diseases, cancer, diabetes, clinical risk modeling, and decision support.
Multimodal Intelligence
Fusion of imaging, clinical, sensor, remote-sensing, and language data using modern neural architectures.
Trustworthy AI
Explainability, model evaluation, bias assessment, LLM observability, reproducibility, and responsible deployment.
Imagery Fusion
Remote-sensing image analysis for crop monitoring, agricultural assessment, and environmental applications. Image super-resolution using multimodal data fusion to improve spatial quality, classification, and decision-making.
Research Portfolio
Research Topics and Projects
Previous and current projects.
Lab Members
Students and Collaborators
The ACIMA Lab brings together undergraduate and graduate students working on applied machine learning, deep learning, computational imaging, biomedical analytics, and geospatial research. Each member contributes to a specific project aligned with the lab’s mission, including medical image analysis, disease detection, remote-sensing applications, crop monitoring, image super-resolution, multimodal data fusion, and trustworthy AI.
Student Research
Presentations and Research Experiences
ACIMA Lab students presenting their research at conferences, symposiums, poster sessions, and academic events.
Scholarly Work
Selected Publications
The selected publications highlight representative contributions from the ACIMA Lab across computational imaging, machine learning, deep learning, biomedical analytics, geospatial applications, and trustworthy AI. These works reflect collaborations with students and research partners and demonstrate the lab’s commitment to developing practical, data-driven solutions for complex scientific and societal challenges.
Sponsored Research
Funding and Student Support
Sponsored funding supports research assistants, student stipends, mentoring, conference participation, computing resources, software, equipment, and dissemination. Do not publish confidential account, payroll, or personally identifiable financial information.
| Project | Sponsor | Period | Role | Students | Support |
|---|
Instruction
Courses Taught
The courses taught reflect experience across foundational, advanced, and applied areas of computer science and data science. They emphasize algorithmic thinking, programming, database systems, machine learning, generative AI, and practical problem-solving for undergraduate and graduate students.
Join the Lab
Research Opportunities
Motivated undergraduate and graduate students interested in machine learning, deep learning, computer vision, healthcare AI, remote sensing, data science, or scalable computing are encouraged to inquire about available projects.
Recommended preparation
- Python programming and data structures
- Foundations in probability, statistics, and linear algebra
- Machine learning or deep learning coursework
- Experience with Git and reproducible experimentation
- Commitment to research documentation and teamwork
Student inquiry checklist
Include the following in your email:
- Degree program and expected graduation date
- Research interests and preferred project area
- Relevant coursework and technical experience
- Résumé or CV
- GitHub, portfolio, or prior work, when available
- Weekly availability and intended research period
Contact
Connect
For research collaboration, student mentoring, invited talks, and academic service inquiries.