About AMIIE
Applied Machine Intelligence Initiatives & Education
The Applied Machine Intelligence Initiatives & Education (AMIIE) Lab, directed by Dr. Soheyla Amirian at Pace University, advances applied artificial intelligence research and education with a focus on real-world and socially meaningful problems.
Our research spans responsible and explainable AI, healthcare AI, computer vision and multimodal learning, graph-based machine learning, AI agents, and data-driven decision support. We develop and evaluate AI methods with an emphasis on interpretability, reliability, fairness, and practical impact.
AMIIE is an interdisciplinary research and education environment that brings together faculty, undergraduate and graduate students, researchers, and collaborators from computing, healthcare, and related disciplines. The lab is committed to mentoring the next generation of AI researchers through hands-on research, interdisciplinary collaboration, technical education, and scholarly dissemination.
Soheyla Amirian, Ph.D.
- Director, AMIIE Lab
- Assistant Professor, Seidenberg School of Computer Science and Information Systems
- Pace University
Research Areas
AMIIE develops and studies AI methods that are technically rigorous, interpretable, and connected to meaningful real-world applications.
Responsible, Explainable & Fair AI
Methods for understanding, evaluating, and improving the transparency, reliability, and fairness of AI systems.
AI for Healthcare & Biomedical Informatics
AI-driven approaches for biomedical data analysis, clinical research, medical imaging, biomarker discovery, and health informatics.
Computer Vision & Multimodal AI
Learning from images, video, text, and multimodal data for understanding, generation, and decision support.
Graph Machine Learning & Network Intelligence
Graph neural networks and network-based learning for structured, relational, and biological data.
Generative AI & AI Agents
Research on language models, agentic systems, reasoning, reliability, evaluation, and responsible deployment.
Natural Language Processing & Text Analytics
Computational methods for extracting knowledge, patterns, and actionable insights from textual data.
Learning with Limited & Complex Data
Few-shot, semi-supervised, and data-efficient learning for settings where labels or high-quality datasets are limited.
AI Education & Workforce Development
Hands-on AI education, research mentorship, technical workshops, and initiatives that broaden participation in AI.
Featured Research
Selected research directions that reflect AMIIE's current emphasis on responsible, interdisciplinary, and high-impact artificial intelligence.
AI for Healthcare & Biomedical Research
Developing data-driven and interpretable AI methods for biomedical research, health informatics, medical imaging, and decision support.
Responsible, Explainable & Fair AI
Designing AI systems that emphasize transparency, interpretability, fairness, reliability, and responsible real-world use.
AI Agents, Reasoning & Reliability
Studying agentic AI systems with an emphasis on reasoning quality, reliability, safety, and comparison with conventional language-model workflows.
News Archive
Announcements, publications, presentations, awards, and other AMIIE Lab milestones.
- 2026: The Pace AI Insight Webinar Series continued to bring researchers, educators, and industry experts together for conversations on emerging directions in artificial intelligence. View the webinar series.
- 2026: AMIIE Lab expanded its research activities in generative AI and AI agents, including work on reasoning, reliability, safety, and responsible evaluation of agentic systems.
- 2026: AMIIE Lab continued interdisciplinary research in responsible and explainable AI, healthcare AI, multimodal learning, and graph-based machine learning with graduate and undergraduate student involvement.
- 2026: Dr. Soheyla Amirian continued professional service and scholarly engagement through conference organization, session leadership, invited talks, and AI research community activities.
- Apr. 2025: Pace AI Insight Series. Registration and more information.
- Mar. 2025: Our team had a paper accepted in IEEE Access.
- Mar. 2025: AMIIE Lab announced new research opportunities. More information.
- Mar. 2025: Pace AI Insight Series. Registration and more information.
- Feb. 2025: Pace AI Insight Series. Registration and more information.
- Feb. 2025: A manuscript was submitted to IEEE EMBC 2025.
- Feb. 2025: A paper was accepted by the International Journal of Medical Informatics.
- Jan. 2025: Dr. Soheyla Amirian served as a Co-Chair of the International Conference on the AI Revolution: Research, Ethics, and Society (AIR-RES).
- Jan. 2025: A manuscript was submitted to IEEE Access.
- Jan. 2025: A manuscript was submitted to IEEE ICHI 2025.
- Jan. 2025: Dr. Amirian presented "AI in Healthcare" at the Icahn School of Medicine at Mount Sinai, Bado Lab, Tisch Cancer Institute, New York.
- Dec. 2024: Keynote speaker at the Seidenberg Annual Research Day.
- Dec. 2024: Program Committee member at the Seidenberg Annual Research Day.
- Dec. 2024: Keynote speaker at the CSCI'24 International Conference.
- Nov. 2024: Pace AI Insight Series.
- Nov. 2024: Selected as a 2024–2025 Faculty Fellow of the Helene T. and Grant M. Wilson Center for Social Entrepreneurship.
- Oct. 2024: Panelist, “The AI Revolution: How AI is Redefining our Daily Lives,” Pace University.
- Sep. 2024: Paper accepted in Global Mental Health, Cambridge University Press.
- Sep. 2024: Invited presentation at the GWU Biomedical Informatics Center, CTSI-CN, and Washington DC VA.
- Sep. 2024: Generative AI in Orthopedics manuscript accepted at JAMIA.
- Sep. 2024: Dr. Amirian joined Pace University.
- Jul. 2024: Paper published in Scientific Reports.
- Apr. 2024: The team won second place for a poster at the University of Georgia AI Research Day.
- Mar. 2024: NIH National Institute on Aging award.
- Feb. 2024: UGA Student Career Success Influencer Award 2023.
- Dec. 2023: Keynote speaker at the CSCI'23 International Conference.
- Nov. 2023: Keynote speaker at Health Informatics Grand Rounds, University of Pittsburgh.
- Oct. 2023: IEEE Atlanta Section Outstanding Educator Award 2023.
- Oct. 2023: Tutorial on explainable deep few-shot learning at ISVC 2023.
- Aug. 2023: Bone metastasis classification work accepted at CTOS.
- Jul. 2023: Explainable AI paper accepted at CSCE'23.
- Jul. 2023: Unbiased image segmentation paper accepted at IEEE BHI 2023.
- Apr. 2023: AI fairness in medical imaging paper accepted at IEEE ICHI 2023.
- Mar. 2023: Deep few-shot learning in medical imaging paper accepted at IEEE ICHI 2023.
- Dec. 2022: Manuscript accepted at CSCI 2022.
Research, Education & Collaboration
Explore the lab, our work, and opportunities to connect.
Research
Explore our research projects, methods, and interdisciplinary collaborations.
View ResearchPeople
Meet the students, researchers, collaborators, and faculty contributing to AMIIE.
Meet the TeamEducation & Opportunities
Discover educational activities, workshops, webinars, and opportunities to work with the lab.
Explore Education