AI Research Initiative

AIR students and faculty standing on Cummington Mall

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 meetings occur weekly at the Hariri Institute for Computing. Research talks include Ph.D. student presentations on ongoing or published work, local guests from the greater Boston area, and distinguished speakers from other universities and industry partners. Please refer to the AIR calendar for upcoming dates and talk information.

Research

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. As the initiative’s contributors reach various milestones in their work, links to publications, presentations, and media coverage will be available here as well as any new labs.

Affiliated Research Labs

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.

People

Core Faculty

AIR Affiliates

Events

Speakers at AIR events have included many different individuals with different backgrounds and levels of academic and professional accomplishments. Seminar topics include computer vision, natural language processing, machine learning, robotics, and human-computer interaction. To receive emails of upcoming AIR Seminars, join the google group, air_seminar_public, by jumping to this link with your BU google account and click ‘Ask to join group’.

Open AIR

Distinguished Speaker Series

Past Speakers

2024

Jan 24, 2024: Mihaela van der Scharr, Professor of Machine Learning, Artificial Intelligence and Medicine – University of Cambridge
Feb 28, 2024: Ravi Ramamoorthi, Professor of Computer Science – University of California, San Diego
Mar 6, 2024: Andrew Gordon Wilson, Associate Professor of Machine Learning, New York University
Mar 27, 2024: James Zhou, Associate Professor of Biomedical Data Science, Stanford University
Apr 3, 2024: Svetlana Lazebnik, Professor of Computer Science – University of Illinois
Apr 10, 2024: Deva Ramanan, Professor of Robotics – Carnegie Mellon University

2023

Apr 25, 2023: David Jacobs, Professor of Computer Science and UMIACS, University of Maryland
Dec 13, 2023: Sharon Xiaolei Huang, Professor & Associate Dean of Undergraduate Studies the College of Information Sciences & Technology – Penn State

2022

Feb 4, 2022: Claire Monteloni, Associate Professor of Computer Science – University of Colorado
Feb 7, 2022: Aisha Walcott-Bryant, Senior Technical Staff Member & Research Manager – IBM Research Africa
Feb 14, 2022: My Phan, PhD Student in Computer Science – Unviersity of Massachussets Amherst
Feb 28, 2022: Afra Feyza Akyürek, PhD Student in Computer Science – Boston University

2021

Feb 8, 2021: Yin Li, Assistant Professor of Biostatistics & Computer Science – University of Wisconsin
Feb 22, 2021: Timnit Gebru, Computer Scientist & Co-Founder of Black in AI
Mar 15, 2021: Ekin Akyürek, PhD Student in Computer Science – MIT
Mar 29, 2021: Tao Du, PhD Student in Computer Science – MIT
Apr 5, 2021: Ahmed Taha, PhD Student in Computer Science – University of Maryland
Apr 12, 2021: Kristen Grauman, Professor of Computer Science – University of Texas
Oct 21, 2021: Jimuyang Zhang, PhD Student – Boston University
Nov 2, 2021: Samory Kpotufe, Associate Professor of Statistics – Columbia University
Dec 3, 2021: Rogerio Feris, Principal Scientist at IBM
Dec 7, 2021: Csaba Szepesvari, Professor of Computer Science – University of Alberta

2020

Sept 9, 2020: David Bau, PhD Student at MIT, former Engineer at Google
Sept 21, 2020: Juergen Gall, Professor – University of Bonn
Oct 19, 2020: Serge Belongie, Professor of Computer Science – Cornell University

2019

Feb 4, 2019:Samy Bengio, Research Scientist – Google Brain, Program Chair – Neural Information Processing System (NIPS)
Apr 8, 2019: Karrie Karahalios, Professor of Computer Science, Co-director of the Center for People and Infrastructures – University of Illinois, Senior Research Scientist at Adobe Research
Apr 22, 2019:Yoram Singer, Professor of Computer Science – Princeton University, Head of Principles of Effective Machine Learning (POEM) for Google Brain
Dec 12, 2019: Maria-Florina Balcan, Professor of Computer Science – Carnegie Mellon University

Speaker Series

Weekly Meetings

Events

View upcoming AIR Initiative events on the Hariri Calendar

Past Events

For recordings of past AIR events, please visit the AIR Initiative playlist on the Hariri Institute YouTube channel.