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Machine Intelligence and Data Science Lab
By leveraging advanced machine learning and data mining algorithms, we focus on the analysis, experimental evaluation, design and implementation of theory, logic and systems, with an emphasis on developing explainable, interpretable and theoretically sound tools to create new and innovative technology.
About Us
About Us
We are the Machine Intelligence and Data Science (MINDS) Lab, led by Dr. Jamell Dacon. Our mission is to advance trustworthy, impactful, and human-centered AI through interdisciplinary research at the intersection of machine learning, data science, and society.
Our research interests spans several core areas:
Trustworthy NLP and Language Justice: Developing robust, fair, and interpretable language technologies that address bias, dialect variation, and harmful online content.
Educational AI and Learning Analytics: Designing AI systems for student support, pedagogical co-regulation, educational retrieval, and robust assessment under distribution shift.
Health AI and Clinical Decision Support: Building personalized, data-driven systems for chronic disease management, predictive analytics, and scalable health informatics.
Computational Social Science and Equity Analytics: Applying AI and data science to investigate societal harms, public discourse, and inequity.
AI Robustness, Interpretability, and Distribution Shift: Developing methods that improve generalization, reliability, and methodological rigor in high-stakes domains.
Across these areas, we are committed to advancing trustworthy AI by emphasizing fairness, accountability, transparency, interpretability, robustness, and real-world impact. Through innovative research and collaborative partnerships, we leverage AI and data science to address pressing societal challenges and create technologies that benefit diverse communities.
Future of AI Ethics
Future of AI Ethics
As AI continues to evolve, so must our approach to its ethical implications. We are committed to shaping a future where AI is developed and deployed responsibly—ensuring fairness, transparency, and accountability in every decision. Our research explores the evolving landscape of AI ethics to create systems that benefit all of society.
Research Areas
Research Areas
Our research focuses on trustworthy AI for socially consequential domains, with emphasis on education, health, language justice, and robust machine learning.
Trustworthy NLP and Language Justice
We study bias, robustness, dialect variation, harmful language, and fairness in language technologies, with a focus on African American English and other underrepresented language varieties.
Educational AI and Learning Analytics
We develop AI systems that support teaching, learning, and student feedback, including robust educational retrieval, co-regulation, and evaluation under distribution shift.
Health AI and Clinical Decision Support
We build personalized, data-driven AI for chronic disease management, predictive analytics, and scalable health informatics in high-stakes clinical settings.
Computational Social Science and Equity Analytics
We apply AI and data science to study social harms, public discourse, inequity, and the societal impacts of algorithmic systems.
AI Robustness, Interpretability, and Distribution Shift
We develop methods that improve reliability, generalization, interpretability, and methodological rigor in AI systems used in high-stakes domains.
Instruction, Collaboration and Community Engagement
We engage in interdisciplinary collaboration and community engagement to ensure that AI research and development are aligned with societal needs and values.
Disclaimer
Disclaimer
Any opinions, findings, conclusions, or recommendations expressed on this website are those of the author and do not necessarily reflect the views of any organization.
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