The Artificial Intelligence Research (AIR) initiative at BU is a cross-disciplinary research initiative focused on machine intelligence. It brings together researchers whose work aims to create intelligent systems that reliably make decisions, reason about data, and communicate with humans. The primary research focus is on new computational models aimed at general artificial intelligence, i.e. agents that exhibit the skills and learning capacity close to human ability. The initiative crosses the boundaries of computer vision, natural language processing, machine learning, robotics, and human-computer interaction research and promotes AI education at Boston University.
AIR hosts weekly meetings at the Hariri Institute for Computing. Research talks include PhD student presentations on ongoing or published work, local guests from the greater Boston area, and distinguished speakers from other universities and industry partners.
AI research is conducted by the initiative’s core and affiliated faculty, postdocs, graduate students, and undergraduate students whom they support. Specialized focus on different areas of AI is managed and conducted by affiliated research labs associated with AIR. Learn more about recent projects here.
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RememberingMargrit Betke, a visionary computer scientist, award-winning researcher, beloved mentor, and advocate for using technology to improve lives.
Professor Betke passed away peacefully on August 13, 2025 after a long illness. Through her research, mentorship, and dedication to using technology for the greater good, she left a lasting impact on artificial intelligence and the countless students and colleagues she guided. Her legacy continues in AI and in the communities she helped build at BU and beyond.
Artificial Intelligence and Emerging Media (AIEM) The Artificial Intelligence and Emerging Media (AIEM) research group is to conduct research and foster education in areas related to artificial intelligence and emerging media. We explore and create techniques from machine learning, natural language processing, and computer vision to interpret emerging media. Batman Lab The Batman lab conducts research at the intersection of medical vision (medical image analysis), machine learning, and bioinformatics. The main themes of research in the lab are about the main challenges of AI in healthcare: (1) Explainability, (2) Data Efficiency, (3) Multimodal Data Fusion, and Causality. The lab works on Alzheimer’s Disease, Chronic Obstructive Pulmonary Disease (COPD), and Non-Alcoholic Fatty Liver Disease (NAFLD) projects. BioMolecular Engineering Research Center (BMERC) BMERC objectives are to develop and apply computational methods for the analysis and design of structures, functions, interactions, regulation and evolution of biological macromolecules; to use such understanding to contribute to the advancement of modern biological, engineering and medical sciences; and to connect basic sciences with engineering and medical applications. Bridges from Artificial Intelligence to Understanding and Reprograming Biology Group (2AI2BIO)* Group This group is generally interested in contributing to the development of transformative solutions for biological and biomedical problems by introducing computational, analytical, biomedical, conceptual or technological innovations. Its main focus is on application of AI to Biomedical Science. Computationally they are interested in High Performance Algorithms, Theory, Data mining, Machine Learning and AI. Business Insights through Text (BIT) Lab This lab explores, examines, and extracts consumer behavior or market insights through abundantly available, yet severely untapped text data. Via a variety of methodologies including causal inference, generative models, deep learning, neural NLP, bayesian statistics, interpretable machine learning, spanning topics such as social media analytics, digital consumer management, persuasion, platform design, innovation, human-ai collaboration, our studies are focused on providing empirical evidence and empirical generalization to develop or extend consumer behavior and market theories. Computer Vision and Learning Group The Computer Vision and Learning Group conducts research in Artificial Intelligence focuses on out-of-distribution learning, dataset bias, domain adaptation, vision and language understanding, and other topics in deep learning. Data Science & Machine Learning Lab The Data Science & Machine Learning Lab’s research areas include Machine Learning, Video Analysis, Statistical Signal Processing, Information & Control Theory, and Network Science. Image and Video Computing (IVC) The Image and Video Computing group is part of the BU Department of Computer Science and is an affiliated lab of the Artificial Intelligence Research (AIR) Initiative at the Hariri Institute for Computing. The IVC conducts research in many aspects of computer vision, machine learning, and human-computer interaction. Learning, Intelligence + Signal Processing (LISP) Lab Can intelligence be learned? The LISP lab is passionate about exploring, disseminating, and innovating research in machine learning, intelligent decision making systems, and signal processing in order to answer this question. Shape Lab This lab investigates research problems in Computer Graphics and Computational Fabrication, with interests extending into Human Computer Interaction and Engineering Mechanics. They are a team of computer scientists, makers, and engineers, with a long list of inspirations from architectural design to sports technologies. Visual Information Processing (VIP) Lab The VIP Laboratory belongs to the Information and Data Sciences (IDS) group in the Department of Electrical and Computer Engineering. Broadly focused in the area of visual information processing, research spans various projects, including visual surveillance, human-computer interfaces, 3-D video capture, representation and display, as well as biomedical image processing.
Remembering Margrit Betke, a visionary computer scientist, award-winning researcher, beloved mentor, and advocate for using technology to improve lives.
