Education & Opportunities
Learning, mentorship, research experience, and student engagement in artificial intelligence.
Education at AMIIE
The Applied Machine Intelligence Initiatives & Education (AMIIE) Lab is committed to making artificial intelligence education accessible, practical, and connected to real research problems. Our educational activities combine technical learning, hands-on experience, interdisciplinary collaboration, research mentorship, and scholarly communication.
AMIIE supports students at different stages of their academic journey, from undergraduate research experiences to graduate-level projects and doctoral research. We also develop workshops, tutorials, webinars, and open educational resources that help students and researchers build practical skills in AI and machine learning.
Learning Resources
Selected open educational resources and technical learning materials.
Data Science with Python
A video-based learning resource introducing practical concepts in Python and data science.
View on YouTubeJava Programming
A foundational video series covering Java programming concepts for students learning software development and computational problem solving.
View on YouTubeWorkshops & Tutorials
Selected educational activities developed or delivered around responsible AI, explainability, deep learning, and healthcare applications.
The REF-AI Workshop focused on responsible, explainable, and fair artificial intelligence in medical imaging informatics. The workshop addressed the growing need for AI systems that are transparent, fair, accountable, and aligned with responsible practices in healthcare. Topics included trustworthy AI, bias and fairness, interpretability, clinical decision support, and the challenges involved in moving AI systems from research toward practical healthcare use.
More InformationArtificial intelligence has demonstrated strong performance across many healthcare applications, but the black-box nature of complex models can limit trust and adoption in clinical environments. This half-day tutorial examined explainable AI in healthcare, with particular emphasis on transparency, interpretability, accountability, and the communication of AI-supported decisions in clinical settings.
More InformationThis tutorial introduced explainable deep few-shot learning for image localization and segmentation in medical imaging. Participants explored how limited labeled data, model explainability, and cloud-based computational resources can be combined to build practical medical imaging workflows.
More InformationThis workshop brought together researchers and students interested in deep learning for medical image analysis. Topics included image registration, object localization, segmentation, anomaly detection, image classification, and hands-on applications of deep learning in biomedical imaging.
More InformationAI Insight Webinar Series
AMIIE organizes the AI Insight Webinar Series, bringing together researchers, practitioners, students, and invited speakers to discuss emerging developments in artificial intelligence, machine learning, responsible AI, healthcare AI, and related areas.
The series complements AMIIE's research and educational mission by creating opportunities for technical learning, interdisciplinary discussion, and engagement with the broader AI community.
Student Research Opportunities
Opportunities for students interested in gaining hands-on research experience with AMIIE.
AMIIE welcomes motivated doctoral students with interests aligned with the lab's research areas, including artificial intelligence, machine learning, healthcare AI, computer vision, responsible and explainable AI, multimodal learning, graph machine learning, and generative AI.
Doctoral researchers affiliated with the lab are recruited through graduate programs at Pace University. Prospective students should first apply to the appropriate Pace University doctoral program and may contact the lab after admission to discuss research alignment and possible opportunities.
Pace University master's students interested in conducting a capstone project, independent research, or collaborative work with AMIIE are encouraged to reach out with a CV and a short summary of their research interests.
Opportunities depend on project availability, research fit, faculty capacity, and available resources. Funding should not be assumed unless a specific funded position is advertised.
Undergraduate students at Pace University who want to gain research experience are welcome to express interest in joining AMIIE. Depending on project needs, students may contribute to data preparation, literature review, experimentation, evaluation, visualization, software development, or other components of ongoing research.
Undergraduate opportunities are typically research-experience based and may be voluntary unless a funded position is specifically announced.
Current Availability
AMIIE does not list a permanent funded opening on this page. Specific funded research assistantships, internships, or project-based opportunities will be announced when available. Students interested in research collaboration are still encouraged to review the lab's research areas and contact the lab when there is a strong alignment.
How to Get Involved
Explore Our Research
Review AMIIE's current research programs and identify areas that align with your interests, experience, and academic goals.
View ResearchPrepare Your Background
Students contacting the lab should be prepared to share a CV or resume and a concise description of their research interests and relevant technical experience.
Contact AMIIE
If your interests align with the lab's work, contact AMIIE with a clear explanation of the research direction you would like to explore.
samirian@pace.edu