Resources

The Artificial Intelligence Research (AIR) initiative at BU is a cross-disciplinary research initiative focused on machine intelligence.

Research group in AI and emerging media.

Research at the intersection of medical image analysis, machine learning, and bioinformatics.

Extracts consumer-behavior and market insights from text data using causal inference, generative models, deep learning, neural NLP, and interpretable ML.

Develops and applies computational methods to analyze and design the structure, function, interactions, regulation, and evolution of biological macromolecules.

AI research on out-of-distribution learning, dataset bias, domain adaptation, and vision-and-language understanding.

Research across machine learning, video analysis, statistical signal processing, information & control theory, and network science.

The BU Humanities and Artificial Intelligence Lab (HAIL) is an interdisciplinary research group where undergraduate and graduate students examine the social, political, and ethical implications of AI from both technical and humanities perspectives.

CS department group (AIR-affiliated) researching computer vision, machine learning, and human-computer interaction.

Explores machine learning, intelligent decision-making systems, and signal processing.

Develops machine-learning and theoretical-neuroscience models connecting artificial and biological intelligence.

Develops interpretable and explainable AI for climate variability, predictability, weather, and human–Earth systems.

Shared experimental facility for autonomous ground and air vehicles, robot design, perception, planning, and control.

Interdisciplinary center advancing autonomous and robotic systems, including AI-enabled perception, planning, control, and biomedical applications.

Develops computational imaging and microscopy systems combining physical models, optimization, and machine learning.

Combines formal methods and machine learning to build safe, reliable, and secure cyber-physical and AI systems.

Uses platform data and field/lab experiments to study human–AI interaction, recruiting, employment, and labor-market matching.

Researches formal synthesis, verification, control, and learning for hybrid systems and networked mobile robots.

Builds multimodal AI systems for dementia, kidney disease, pathology, and clinical decision support.

Uses machine learning and data-driven security methods to study online abuse, misinformation, cybercrime, and malicious AI use.

Investigates computer graphics and computational fabrication, extending into HCI and engineering mechanics.

Applies AI to biomedical science via computational, analytical, and technological innovation.

The AI and Education Initiative facilitates research at the intersection of AI and learning for all people, of all ages and backgrounds, and all the topics and contexts where learning happens.

University-wide initiative supporting responsible generative-AI education, experimentation, and adoption for students, faculty, and staff.

Uses algorithmic learning and experimental design to study molecular, cellular, and tissue-scale biological organization.

Studies generative AI and LLM deployment, reliability, governance, and economic and organizational impacts.

Studies distributed robot teams, multi-agent coordination, and socially compliant autonomous systems interacting with people.

Researches robust learning, reinforcement learning, optimization, NLP, autonomous systems, and predictive analytics in health.

Develops machine-learning and image-analysis methods for neuroimaging, clinical neuroscience, and computational medicine.

Applies machine learning and systems research to intelligent, energy-efficient computing and sustainable AI data centers.

Research group covering computer vision, graphics, machine learning, and human-computer interaction.

The Evidence-Based AI in Learning (EVAL) Industry Collaborative conducts rigorous research to evaluate how generative AI tools affect K-12 student learning outcomes.

Develops soft and medical robots, sensing, control, and computer-vision-guided systems for minimally invasive procedures.

Department-wide group spanning machine learning, computer vision, NLP, HCI, and robotics; part of the AIR initiative.

Teaching and innovation facility where students design, build, and test robots using perception, AI, autonomy, and advanced hardware.

Visual information processing: visual surveillance, human-computer interfaces, 3-D video capture/display, and biomedical image processing.