RESEARCH
OVERVIEW
A call for project proposals in 2019 resulted in 10 projects that encompass a total of 15 research workstreams. The projects engage more than 150 faculty, researchers, and students, who are affiliated with more than 20 different organizational units across MIT Campus and MIT Lincoln Laboratory. All project teams involve Air Force personnel, who are embedded in the research teams and serve as liaisons between projects and Department of Defense stakeholders. The projects started in January 2020 and advance AI research in a broad range of areas, including weather modeling and visualization, optimization of training schedules, and enhancement of autonomy for augmenting and amplifying human decision-making. The research activities of the AI Accelerator have been successfully expanding, including seed research projects in collaboration with the Naval Postgraduate School and the United States Space Force, as well as an AI Education Research project that started in January 2021. AI Accelerator publications can be found on our Google Scholar page.
AI Accelerator Projects
Guardian Autonomy for Safe Decision Making
Air Guardian aims to advance AI and autonomy by developing algorithms and tools for augmenting and amplifying human decision making. The AI Guardian assists humans by suggesting actions using data from the past and fusing inputs from sensors and information sources. Support from an AI Guardian system is especially useful in the presence of surprises and complex situations. Guardian’s end-to-end machine learning algorithms learn from experts how to respond with common sense reasoning in highly dynamic and surprising situations. Our goal is to enable an agent to perceive its environment, identify short-term risks, reason about intentions and behaviors of its operator, and other cooperative and adversarial agents to determine the best course of action. This will lead to Guardian autonomy systems capable of anticipating potential hazardous situations in the future.
Team
– Daniela Rus (MIT PI)
– Ross Allen (MIT Lincoln Laboratory Lead)
– Ho Chit Siu (MIT Lincoln Lab Lead)
– Josh Rountree (DAF Liaison)



Transferring Multi-Robot Learning through Virtual & Augmented Reality for Rapid Disaster Response
This project seeks to develop a new framework and class of algorithms that allow unmanned aerial systems to learn complex multi-agent behavior in simulator environments, then seamlessly transfer their knowledge from simulation to real-world field environments. The team envisions a first responder system where a swarm of autonomous aircraft are virtually trained on how to navigate and cooperate in a simulation of a novel disaster area. The system then transfers the learning gained in the simulation to the real autonomous aircraft swarm. An aircraft deploys a large “mothership” ground station which releases these trained autonomous aircraft to automatically perform time-critical, labor-intensive tasks like surveying disaster areas and locating and identifying survivors.
Team
– Sertac Karaman (MIT PI)
– Luca Carlone (MIT Co-PI)
– Daniel Griffith (MIT Lincoln Laboratory Lead)
– Stephanie Riley (DAF Liaison)


Multimodal Vision for Synthetic Aperture Radar
Team
– Phillip Isola (MIT PI)
– Taylor Perron (MIT Co-PI)
– Bill Freeman (MIT Co-PI)
– Miriam Cha (MIT Lincon Laboratory Lead)
– Nathaniel Maidel (DAF Liaison)

AI-Assisted Optimization of Training Schedules
In order to improve the immensely complex and time-consuming process of manually scheduling aircraft flights, this project aims at automating aircraft flight scheduling to improve scheduling efficiency and robustness in the presence of uncertainty. This will optimize training flight schedules while providing explainability and removing silos in decision-making. This technology enables schedulers to quickly and effectively re-build schedules in the presence of rapidly changing circumstances, vastly accelerating planning and decision cycles. While initially focused on aircraft flight scheduling, this technology applies to all complex resource-allocation tasks across many sectors.
Team
– Hamsa Balakrishnan (MIT PI)
– Mike Snyder (MIT Lincoln Laboratory Lead)
– Eric Robinson (DAF Liaison)


Fast AI: Data Center & Edge Computing
Fast AI: Quick Development of Portable High-Performance AI Applications
– Charles E. Leiserson (MIT PI)
– Tao B. Schardl (MIT)
– Neil Thompson (MIT)
– Vijay Gadepally (MIT Lincoln Laboratory Lead)
– Andrew Bowne (DAF Liaison)
ML-Enhanced Data Collection, Integration & Outlier Detection
– Tim Kraska (MIT PI)
– Manya Ghobadi (MIT Co-PI)
– Michael Stonebraker (MIT Co-PI)
– Samuel Madden (MIT Co-PI)
– Benjamin Price (MIT Lincoln Laboratory Lead)
– Andrew Bowne (DAF Liaison)

Conversational Interaction for Unstructured Information Access and Language Learning
Conversations Interaction for Unstructured Information Access
The AI Accelerator Natural Language Processing project aims to advance conversational agents, knowledge representation, and prediction algorithms on flat/text image data and on Air Force missions. As the field of artificial intelligence advances, as we memorialize more of our work in data, and find more devices in our homes, it’s crucial that people are able to interact with the technology in meaningful ways – as humans, language matters – especially in discovering information on digital systems. The goal is to advance the AI community with conversational interaction and knowledge extraction for open-domain conversation and into unstructured information.
– Jim Glass (MIT PI)
– Boris Katz (MIT Co-PI)
– Leslie Shing (MIT Lincoln Laboratory Lead)
– Eric Robinson (DAF Liaison)

AI for Personalized Foreign Language Education
The AI Accelerator Natural Language Processing for foreign language project focuses on building personalized foreign language education framework, which includes a model of the language knowledge to be acquired. This work leverages current foreign language, tailored to the expected level of knowledge by the learner at various stages of coursework, and sets to standardized proficiency test measures. The model will help personalize the learning experience and illuminate when and/or where learning outcomes are unfavorable to the student.
– Shigeru Miyagawa (MIT PI)
– Emma Teng (MIT Co-PI)
– Michael Yee (MIT Lincoln Laboratory Lead)
– Megan Muniz (DAF Liaison)

The Earth Intelligence Engine
The Earth Intelligence Engine
Team
– Dava J. Newman (MIT PI)
– Christopher Hill (MIT Co-PI)
– Stephanie Dutkiewicz (MIT Co-PI)
– Mark Veillette (MIT Lincoln Laboratory Lead)
– Nick Chisler (DAF Liaison)


Explainable Machine Learning
– Aleksander Madry (MIT PI)
– Arvind Satyanarayan (MIT Co-PI)
– Antonio Torralba (MIT Co-PI)
– Steven Gomez (MIT Lincoln Laboratory Lead)
– Theo Tsiligkardis (MIT Lincoln Laboratory Lead)
– Nick Chisler (DAF Liaison)



Continual and Few-Shot Learning
– Pulkit Agrawal ( MIT PI)
– Regina Barzilay (MIT Co-PI)
– Marin Soljacic (MIT Co-PI)
– Olga Simek (MIT Lincoln Laboratory Lead)
– Nick Chisler (DAF Liaison)


Robust AI Development Environment
– Asu Ozdaglar (MIT PI)
– Aleksander Madry (MIT Co-PI)
– Pablo Parrilo (MIT Co-PI)
– Olivia Brown (MIT Lincoln Laboratory Lead)
– Nick Chisler (DAF Liaison)


Objective Performance Prediction & Optimization Using Physiological and Cognitive Metrics
This project brings together experts in biomedical instrumentation, signal processing, neurophysiology, psychophysics, computer vision, Artificial Intelligence (AI), and Machine Learning (ML), as well as Air Force pilots, to develop and test AI-based, multi-modal physiologic sensor fusion approaches for objective performance prediction and optimization. The project will leverage immersive virtual environments to train pilots and unobtrusively measure predictors of performance. A series of Challenge Datasets developed from the program will be used to engage the community. Partnering with multiple governmental research efforts and Air Education and Training Command’s myriad pilot training units, the team seeks to provide proof-of-concept by demonstrably accelerating pilot training timelines, producing “better pilots faster.” These methods for accelerating training can then be transferred to all modes of learning across many disciplines and any task requiring significant cognitive effort.
– Thomas Heldt (MIT PI)
– Tamara Broderick (MIT)
– Vivienne Sze (MIT)
– Hrishikesh Rao (MIT Lincoln Laboratory Lead)
– Laura Brattain (MIT Lincoln Laboratory)
– Kyle McAlpin (DAF Liaison)



Robust Neural Differential Models for Navigation and Beyond
There are several different GPS-alternatives being researched across the DoD and civilian sectors to address a GPS alternative; however, each alternative comes with additional costs and use cases. Magnetic Navigation presents an alternative GPS system that relies on magnetic resonance of the Earth – a system that is largely known and unchanging – to navigate. Some of the current problems with magnetic navigation involve 1) reducing excess noise on the system, such as magnetic outputs from the Aircraft itself, 2) determining position at a real-time pace or speeds consistent with military systems, and 3) combining with other systems to present a full-alternative GPS system. The present project looks into using robust neural differential models to solve magnetic navigation shortcomings and provide a viable alternative to GPS.
– Alan Edelman (MIT PI)
– Chris Rackauckas (MIT)
– Jonathan Taylor (MIT Lincoln Laboratory Lead)
– Kyle McAlpin (DAF Liaison)


AI-Enhanced Spectral Awareness and Interference Rejection
This project seeks to apply AI to enhance the USAF’s ability to detect, identify, and geolocate unknown radiofrequency (RF) signals, while providing tools for adaptive interference mitigation and smart spectrum analysis. These capabilities enhance Air Force Intelligence Surveillance and Reconnaissance (ISR) missions, communications, signals intelligence (SIGINT), and electronic warfare. Results will increase bandwidth utilization efficiency and spectrum sharing, improve Air Force communications performance in high interference environments, produce higher-quality RF signals intelligence, and improve system robustness to adversarial attacks and interference.
Team
– Gregory W. Wornell (MIT PI)
– Yury Polyanskiy (MIT Co-PI)
– Jarilyn Hernandez Jimenez (MIT Lincoln
Laboratory Lead)
– Binoy Kurien (MIT Lincoln Laboratory Lead)
– Lindsey McEvoy (DAF Liaison)



AI Education Research: Know-Apply-Lead (KAL)
KAL is an exploratory research project that aims to advance educational research activities that promote maximum learning outcomes at scale for learners with diverse roles and educational backgrounds, ranging from Air Force and Department of Defense (DoD) personnel to the general public. The project team will research and evaluate various pedagogical practices and learning benefits associated with training Air Force personnel in AI topics over a variety of existing courses, map out the landscape of educational needs and competencies, and pilot experimental learning experiences with the goal of outlining early prototypes for innovative technology-enabled training and learning. The research is expected to provide insights that will benefit AI learners across the nation while supporting the DoD’s objective to develop elite and world-class AI-ready services.
Team
– Cynthia Breazeal (MIT PI)
– Katerina Bagiati (MIT)
– Andrés Felipe Salazar Gomez (MIT)
– Kathleen Kennedy (MIT)
– Lori Glover (MIT)
– Sanjeev Mohinra (MIT Lincoln Laboratory Lead)
– Megan Muniz (DAF Liaison)
