News articles about the LLSC and programs that are supported by the LLSC https://hdl.handle.net/1721.1/126568 2026-04-12T11:03:36Z 2026-04-12T11:03:36Z https://hdl.handle.net/1721.1/153215 2023-12-20T03:31:04Z 2023-12-19T00:00:00Z

Sequence Evaluation in Real Time The ability to identify and characterize the function of an unknown pathogen, such as a virus, bacteria, or toxin, from its biological sequence is critical to quickly determine the potential impact against human health. SEQer aims to build an end-to-end computational testbed that can stream biological sequences in real-time and determine their function to identify possible biothreats.

2023-12-19T00:00:00Z https://hdl.handle.net/1721.1/153202 2023-12-19T03:43:59Z 2023-12-18T00:00:00Z

Poisoning Cyberattacks to Design of Artificial Intelligence The MIT Lincoln Laboratory project Poisoning Cyberattacks to Design of Artificial Intelligence (PoCyDAIn) aims to develop a framework for assessing the impact of poisoning attacks on cyber-ML systems.

2023-12-18T00:00:00Z https://hdl.handle.net/1721.1/153201 2023-12-19T03:10:01Z 2023-12-18T00:00:00Z

Multimodal Vision for 3D Scene Interpretation The MIT Lincoln Laboratory project Multimodal Vision for 3D Scene Interpretation (MMV3D) translates multiple types of 2D imagery to create a 3D image that more accurately captures complex multi-surface geometry.

2023-12-18T00:00:00Z https://hdl.handle.net/1721.1/153199 2023-12-19T04:05:17Z 2023-12-18T00:00:00Z

Award-Winning Laboratory Technology Aims to Prevent Aircraft Collisions A next-generation collision avoidance system will help pilots and unmanned aircraft safely navigate the airspace.

2023-12-18T00:00:00Z https://hdl.handle.net/1721.1/153197 2023-12-19T03:05:10Z 2023-12-18T00:00:00Z

Using Machine Learning to Trace Genetically Engineered DNA A Laboratory team is using machine learning to trace the origin of DNA modifications. While previous studies focused on using plasmids—extra-chromosomal pieces of DNA—this team focused on trying to pinpoint exact computational tools used for editing the genome. The results of their work show that it may be possible to trace the origin of modifications back to a specific program, which may help identify the culprit in any attack involving genetic modification.

2023-12-18T00:00:00Z Zewe, Adam https://hdl.handle.net/1721.1/153196 2023-12-19T03:30:25Z 2023-12-05T00:00:00Z

AI Accelerates Problem-solving in Complex Scenarios Zewe, Adam Researchers from MIT and ETH Zurich have developed a new, data-driven machine-learning technique that could be applied to many complex logistical challenges, such as package routing, vaccine distribution, and power grid management.

2023-12-05T00:00:00Z https://hdl.handle.net/1721.1/153195 2023-12-19T03:35:40Z 2023-12-18T00:00:00Z

Combining Neural Networks and Histogram Layers for Underwater Target Classification To detect targets, sea vessels largely rely on passive sonar, which records sounds with an underwater microphone. However, techniques for processing and analyzing passive sonar data often struggle to disentangle the complex patterns in target recordings. To better capture statistical features within passive sonar data, a team from Lincoln Laboratory and the Advanced Vision and Learning Lab at Texas A&M University are adding local histogram layers into neural network architectures. This project employs two types of neural networks for automated feature learning that together can capture local relationships within audio signals while incorporating signal time dependencies.

2023-12-18T00:00:00Z https://hdl.handle.net/1721.1/153173 2023-12-15T03:02:52Z 2023-12-14T00:00:00Z

The Soviet Atomic Threat, Oppenheimer, and the Need for National Air Defense Widespread concern about the potential of a Soviet Union nuclear bomber threat prompted the Department of Defense (DoD) to initiate studies to evaluate the nation’s state of air defense against nuclear attack. This ultimately led to the creation of Project Lincoln and the establishment of Lincoln Laboratory. In the summer of 1952, a group of scientists, engineers, and military personnel met at Lincoln Laboratory to consider ways to improve the air defense of North America. Physicist J. Robert Oppenheimer visited the Laboratory to participate in this 1952 Summer Study, as did a number of other distinguished scientists. Oppenheimer became very concerned about the threat of air invasion after the summer study, and was a strong proponent for the programs established at Lincoln Laboratory.

2023-12-14T00:00:00Z Foy, Kylie https://hdl.handle.net/1721.1/153172 2023-12-15T03:04:44Z 2023-10-05T00:00:00Z

New tools are available to help reduce the energy that AI models devour Foy, Kylie The MIT Lincoln Laboratory Supercomputing Center (LLSC) is developing techniques to help data centers reel in energy use. Their techniques range from simple but effective changes, like power-capping hardware, to adopting novel tools that can stop AI training early on. One of their techniques can reduce the energy of training AI models by 80 percent. Their work is mobilizing green-computing research and promoting a culture of transparency.

2023-10-05T00:00:00Z Chu, Jennifer https://hdl.handle.net/1721.1/153171 2023-12-15T04:03:53Z 2023-09-20T00:00:00Z

Ancient Amazonians Intentionally Created Fertile “Dark Earth” Chu, Jennifer The Amazon river basin is known for its immense and lush tropical forests, so one might assume that the Amazon’s land is equally rich. In fact, the soils underlying the forested vegetation, particularly in the hilly uplands, are surprisingly infertile. Much of the Amazon’s soil is acidic and low in nutrients, making it notoriously difficult to farm. But over the years, archaeologists have dug up mysteriously black and fertile patches of ancient soils in hundreds of sites across the Amazon. This “dark earth” has been found in and around human settlements dating back hundreds to thousands of years. And it has been a matter of some debate as to whether the super-rich soil was purposefully created or a coincidental byproduct of these ancient cultures. Now, a study led by researchers at MIT, the University of Florida, and in Brazil aims to settle the debate over dark earth’s origins.

2023-09-20T00:00:00Z Morgan, Timothy Prickett https://hdl.handle.net/1721.1/153165 2023-12-15T03:30:55Z 2023-09-01T00:00:00Z

What Would You Do With a 16.8 Million Core Graph Processing Beast? Morgan, Timothy Prickett The US Defense Advanced Research Projects Agency has been looking into creating a massively parallel graph processor and interconnect since establishing the Hierarchical Identify Verify Exploit (HIVE) project back in 2017. Intel was chosen to make the HIVE processor and Lincoln Laboratory at MIT and Amazon Web Services were chosen to create and host a trillion-edge graph dataset for a system based on such processors to chew on.

2023-09-01T00:00:00Z https://hdl.handle.net/1721.1/152377 2023-10-06T03:29:43Z 2023-10-05T00:00:00Z

Developing Artificial Intelligence for Noncooperative Space Operations Using Kerbal Space Program With nearly 9,000 active and inactive satellites in orbit, space has become more crowded and competitive than ever before. Solving nascent problems in this domain — such as avoiding collision with debris or servicing malfunctioning satellites — requires more than just knowledge of orbital mechanics and spacecraft control. In the popular video game Kerbal Space Program (KSP), a team at the Laboratory saw a sophisticated multi-physics simulator capable of modeling all aspects of the aerospace domain. The team used the game engine to develop an open-source library and challenge suite, Kerbal Space Program Differential Games (KSPDG), designed to spur development of AI for a wide range of problems within the orbital domain.

2023-10-05T00:00:00Z Chu, Jennifer https://hdl.handle.net/1721.1/152376 2023-10-06T03:37:17Z 2023-08-30T00:00:00Z

Newly discovered planet has longest orbit yet detected by the TESS mission Chu, Jennifer More than 80 percent of confirmed exoplanets have orbits shorter than 50 days. Astronomers are starting to get a general picture of these planets’ formation, evolution, and composition. But the picture is much fuzzier for planets with longer orbital periods. Far-out worlds, with months- to years-long orbits, are more difficult to detect, and their properties have therefore been trickier to discern. Now, the list of long-period planets has gained two entries. Astronomers at MIT, the University of New Mexico, and elsewhere have discovered a rare system containing two long-period planets orbiting TOI-4600, a nearby star that is 815 light years from Earth. The discovery was made using data from NASA’s Transiting Exoplanet Survey Satellite, or TESS — an MIT-led mission that monitors the nearest stars for signs of exoplanets. The new, farther planet has the longest period that TESS has detected to date.

2023-08-30T00:00:00Z https://hdl.handle.net/1721.1/152304 2023-09-29T03:33:34Z 2023-09-28T00:00:00Z

ACAS X Provides Next-Generation Collision Avoidance ACAS X is a family of collision avoidance systems that use machine learning to optimize traffic alerts, resulting in dramatically reduced nuisance alerting and improved safety.

2023-09-28T00:00:00Z https://hdl.handle.net/1721.1/152299 2023-09-29T03:30:00Z 2023-07-24T00:00:00Z

Einblick Launches Deep AI Integration, Empowering Users to Go from Prompts to Multimodal Data Workflows Einblick recently released Einblick Prompt, bringing the power of OpenAI and ChatGPT straight into their canvas-based data notebooks. Prompt is an AI agent that reasons and solves users’ natural language prompts, allowing them to generate and debug code, create charts, and build machine learning models in seconds.

2023-07-24T00:00:00Z Sneider, Ethan https://hdl.handle.net/1721.1/152298 2023-09-29T03:53:34Z 2023-01-27T00:00:00Z

Roadmap for Uncertainty: Implementing Modernization and Operationalizing Change Sneider, Ethan The Phantom Fellowship Program at the Department of the Air Force-Massachusetts Institute of Technology Artificial Intelligence Accelerator (DAF-MIT AIA) is a great case study for how to be successful in new environments. The resulting mental model titled “The Roadmap for Uncertainty,” as derived from the Phantom Program, shows how success is a simple matter of having the right tools readily available.

2023-01-27T00:00:00Z https://hdl.handle.net/1721.1/152297 2023-09-29T03:33:57Z 2023-01-18T00:00:00Z

First Commercial Company Leverages DAF-MIT Maneuver ID Challenge to Advance AI The Department of the Air Force-MIT Artificial Intelligence Accelerator (AIA) announced that Crowdbotics is the first company to successfully leverage the Maneuver Identification Challenge to advance the field of AI. The DAF-MIT AIA Maneuver ID Challenge is an open challenge designed to enable AI coaching and automatic maneuver grading in pilot training.

2023-01-18T00:00:00Z Chase, Brittany https://hdl.handle.net/1721.1/152296 2023-09-29T03:40:56Z 2022-05-20T00:00:00Z

Security leaders learn AI fundamentals through MIT professional program Chase, Brittany The AI Accelerator partnered with MIT Lincoln Laboratory, DOD’s Chief Digital and Artificial Intelligence Office, and MIT’s Computer Science and Artificial Intelligence Laboratory to offer the AI for National Security Leaders course, teaching defense officials the basics of modern AI. The three-day training opportunity was designed to inform DOD senior leaders about AI’s national security implications and ways to apply it in their domains of responsibility.

2022-05-20T00:00:00Z https://hdl.handle.net/1721.1/152295 2023-09-29T03:06:49Z 2023-01-17T00:00:00Z

Phantom Fellowship Program Culminates in BAH Optimization Project The Phantom Fellowship is a rigorous program for Airmen and Guardians interested in gaining exposure to AI/ML technologies. Part of the Phantom Fellowship Program mission is to develop advocates of AI within the DoD to help the rest of the services understand the capabilities and limitations of AI. Participants of a recent capstone project in the program with the Department of the Air Force-Massachusetts Institute of Technology Artificial Intelligence Accelerator examined possible solutions using AI and Machine Learning models to support and augment the process of calculating Basic Allowance for Housing.

2023-01-17T00:00:00Z https://hdl.handle.net/1721.1/152294 2023-09-29T03:46:37Z 2023-05-26T00:00:00Z

MagNav Project Successfully Demonstrates Real-Time Magnetic Navigation In a groundbreaking achievement, the Department of the Air Force-Massachusetts Institute of Technology Artificial Intelligence Accelerator (AIA) MagNav project performed real-time magnetic navigation (MagNav) on the C-17A Globemaster III in flight, becoming the first organization to successfully demonstrate this cutting-edge technology in real-time on a Department of Defense aircraft. The successful demonstration of MagNav marks a significant milestone in advancing navigation capabilities for the U.S. Air Force.

2023-05-26T00:00:00Z https://hdl.handle.net/1721.1/152293 2023-09-29T03:45:28Z 2023-09-28T00:00:00Z

A Future of Unmanned Aerial Vehicles Though UAVs have continued to be produced, regulations have not been modified since they were first outlined in 2006. Now that these vehicles are being used for tasks such as package delivery, medical supply delivery, and infrastructure inspection, the need for regulations regarding them has become more pressing. Ngaire Underhill, Associate Staff member at MIT Lincoln Laboratory, Surveillance Systems group, has spent much time thinking about these issues in collaboration with the FAA.

2023-09-28T00:00:00Z Vazquez, Christian https://hdl.handle.net/1721.1/152292 2023-09-29T03:29:23Z 2023-04-21T00:00:00Z

MIT and Stanford researchers develop operating system with one major promise: Resisting ransomware Vazquez, Christian Computer science researchers at MIT and Stanford are developing an operating system with built-in cybersecurity defenses.

2023-04-21T00:00:00Z https://hdl.handle.net/1721.1/152291 2023-09-29T03:51:01Z 2023-09-28T00:00:00Z

Laboratory Study Compares Machine Learning Accelerator Technologies Dr. Albert Reuther, Senior Staff, Lincoln Laboratory Supercomputing Center (LLSC), is leading a Laboratory study that investigates and compares machine learning accelerators to better understand each accelerator’s strengths and weaknesses. With a better understanding of how accelerators lead to increased performance, the LLSC is able to advise the Department of Defense to make more informed decisions about which machine accelerators to utilize in their computer systems.

2023-09-28T00:00:00Z Chu, Jennifer https://hdl.handle.net/1721.1/152290 2023-09-29T03:48:55Z 2023-02-28T00:00:00Z

Phiala Shanahan is seeking fundamental answers about our physical world Chu, Jennifer With supercomputers and machine learning, MIT physicist Phiala Shanahan aims to illuminate the structure of everyday particles and uncover signs of dark matter.

2023-02-28T00:00:00Z Smith, Kaye https://hdl.handle.net/1721.1/150453 2023-04-07T03:26:59Z 2023-02-03T00:00:00Z

National Program Provides Real Cybersecurity Experience Smith, Kaye A team of University of Arizona graduate students was matched in spring 2022 with technical directors Jeremy Kepner and Hayden Jananthan from MIT Lincoln Laboratory to better identify the cyber characteristics of large-scale network data in order to help organizations defend cyberattacks.

2023-02-03T00:00:00Z Shah, Agam https://hdl.handle.net/1721.1/150452 2023-04-07T03:23:50Z 2023-01-20T00:00:00Z

Top HPC Players Creating New Security Architecture Amid Neglect Shah, Agam The concerns over security of high-performance computers are now being taken seriously in both the public and private sector, who are jointly defining a security architecture as part of a working group called High-Performance Computing Security, which is managed by National Institute of Standards and Technology and the National Science Foundation.

2023-01-20T00:00:00Z Zewe, Adam https://hdl.handle.net/1721.1/150451 2023-04-07T03:33:08Z 2023-01-11T00:00:00Z

Program teaches US Air Force personnel the fundamentals of AI Zewe, Adam MIT researchers launched a pilot program to develop and study an AI education program that could provide essential skills to Air Force personnel with varied backgrounds and job requirements.

2023-01-11T00:00:00Z https://hdl.handle.net/1721.1/150450 2023-04-07T03:24:09Z 2023-04-06T00:00:00Z

Parallel Thermal Analysis on LLSC The Lincoln Laboratory Supercomputing Center is exploring more designs and solving larger space thermal models using the supercomputing workflow developed by the Engineering Research Technical Investment project. The project integrated three key components for space systems design: a powerful thermal solver, Lincoln Laboratory’s Integrated Modeling and Analysis Software (LLIMAS), and the computing resources on the LLSC platform.

2023-04-06T00:00:00Z https://hdl.handle.net/1721.1/146347 2022-11-11T03:24:50Z 2022-11-10T00:00:00Z

Two Laboratory Teams Receive 2020 AI Accelerator Awards Two teams involving Laboratory staff were recognized with 2020 awards from the Department of the Air Force (DAF)−MIT Artificial Intelligence (AI) Accelerator. Recipients were selected from more than 150 airmen, researchers, faculty, and students from the DAF, the Laboratory, and MIT who are part of a collaborative effort to accelerate fundamental advances in AI. The AIA Director's Award, which highlights "excellence and impact with a focus on collaboration across the AIA and with stakeholders," recognized the Earth Intelligence Engine (EIE) project team for "cross-organizational collaboration, curation of novel datasets, visualization of forecasts, and delivery of an innovative challenge problem." The Earth Intelligence Engine (EIE) project targets three research areas — the earth intelligence platform, earth intelligence enhancement, and earth visual models — to build weather and climate resiliency for the U.S. Air Force. Resiliency against hurricanes, wildfires, flooding, sea level rise, and other extreme-weather and climate related threats is critical to protecting USAF resources such as military bases and ensuring mission readiness. The AIA Challenge Award recognized the Puckboard project team for designing and implementing two community challenge problems focused on aircrew scheduling. Puckboard is a web-based software application for scheduling pilots and loadmasters—personnel responsible for loading and unloading cargo and passengers—to mission and training flights.

2022-11-10T00:00:00Z Hinkel, Lauren https://hdl.handle.net/1721.1/146346 2022-11-11T03:36:45Z 2022-04-01T00:00:00Z

Generating New Molecules with Graph Grammar Hinkel, Lauren An efficient machine-learning method uses chemical knowledge to create a learnable grammar with production rules to build synthesizable monomers and polymers.

2022-04-01T00:00:00Z https://hdl.handle.net/1721.1/146345 2022-11-11T03:39:05Z 2022-11-10T00:00:00Z

Green AI Architecture Experimentation The Green AI Architecture Experimentation (GAIA-X) project is developing technologies to promote the concept of green computing by establishing foundational tools and proposing power-reduction strategies. The immediate goals center on developing intelligent tools and strategies to measure and track power consumption in the data center, and demonstrating the effectiveness of various power reduction strategies on AI training and experimentation.

2022-11-10T00:00:00Z https://hdl.handle.net/1721.1/146344 2022-11-11T03:24:07Z 2022-11-10T00:00:00Z

Laboratory Technology Helps Manage Flight Delays Through Severe Weather Historically, air traffic controllers (ATCs) have relied on past experience and looked at weather forecasts themselves to understand and prepare in advance for weather that could impact the flow of traffic. A MIT Lincoln Laboratory-developed technology called the Traffic Flow Impact (TFI) tool aims to help ATCs make detailed plans in advance using machine learning. The tool, which began development in 2013 and recently won an R&D 100 Award, utilizes multiple weather forecast models from the National Oceanic and Atmospheric Administration and machine learning to analyze the different forecast models. It also considers historical data of how traffic has been interrupted by weather in order to provide a breakdown of how ATCs may want to adjust the flow of traffic.

2022-11-10T00:00:00Z Foy, Kylie https://hdl.handle.net/1721.1/146335 2022-11-11T03:41:33Z 2022-08-24T00:00:00Z

Taking a magnifying glass to data center operations Foy, Kylie To gain insight into whether the MIT Lincoln Laboratory Supercomputing Center (LLSC) TX-GAIA supercomputer is being used as effectively as it can, the LLSC has been collecting detailed data on system usage over the past year. More than a million user jobs later, the team has released the dataset open source to the computing community. The goal is to empower computer scientists and data center operators to better understand avenues for data center optimization.

2022-08-24T00:00:00Z https://hdl.handle.net/1721.1/146228 2022-11-09T03:42:33Z 2022-11-09T00:00:00Z

Intelligent Tornado Prediction Engine The most common type of tornado in the Southeast region of the United States, known as a quasi-linear convective system tornado, is historically difficult to warn for, with lead times hovering under 7 minutes and a false alarm rate of over 75 percent. The Intelligent Tornado Prediction Engine (ITORPE) combines meteorological and machine learning expertise from MIT Lincoln Laboratory researches and the Lincoln Laboratory Supercomputing Center to perform extremely large-scale data fusion spanning several years’ worth of radar, satellite, model, and in situ observation platforms, to provide enhanced situational awareness to forecasters using a graphical interface to focus forecasters’ attention on the storms of highest importance.

2022-11-09T00:00:00Z Kepner, Jeremy Bader, David A. Davis, Tim Pearce, Roger Wolf, Michael M. https://hdl.handle.net/1721.1/146227 2023-01-23T04:01:27Z 2022-11-09T00:00:00Z

GraphBLAS and GraphChallenge Advance Network Frontiers Kepner, Jeremy; Bader, David A.; Davis, Tim; Pearce, Roger; Wolf, Michael M. The challenges associated with graph algorithm scaling led multiple scientists to identify the need for an abstraction layer that would allow algorithm specialists to write high-performance, matrix-based graph algorithms that hardware specialists could then design to without having to manage the complexities of every type of graph algorithm. With this philosophy in mind, a number of researchers (including two Turing Award winners) came together and proposed the idea that “the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks”

2022-11-09T00:00:00Z https://hdl.handle.net/1721.1/141669 2022-04-06T03:25:09Z 2022-04-05T00:00:00Z

Laboratory Workshops Highlight Use of Artificial Intelligence in National Security Teams at MIT Lincoln Laboratory are using AI to address national security challenges in collaboration with other organizations. To facilitate this research and promote collaboration with other organizations, the Laboratory virtually hosted the third annual Recent Advances in Artificial Intelligence for National Security (RAAINS) and Human-Machine Collaboration for National Security (HMC) workshops. The theme of this year’s HMC workshop was “Human-Centered AI.” The Department of Defense (DoD) and other national security organizations have recently highlighted this as a crucial focus for AI research and development, and also have called for a focus on AI ethics and reliability as AI increasingly becomes an equal partner in important national security work.

2022-04-05T00:00:00Z https://hdl.handle.net/1721.1/141668 2022-04-06T03:39:23Z 2022-04-05T00:00:00Z

Technology Office Announces Winners of the FoolMe Hackathon The MIT Lincoln Laboratory Technology Office wrapped up the FoolMe Challenge, a hackathon that was part of the Laboratory’s ongoing effort to observe trends in the manipulation of information, anticipate the broader implications to national security, and develop mitigation strategies. Teams worked to develop new methods of detecting manipulated images in six datasets that had been modified using different data poisoning techniques. The teams were judged based on the correct identification of manipulated images and the novelty of their problem-solving approach.

2022-04-05T00:00:00Z Kostovic, Aleksandar https://hdl.handle.net/1721.1/137837 2021-11-09T03:15:34Z 2021-10-29T00:00:00Z

MIT Researchers Turn to Unity 3D Game Engine for Supercomputer Diagnostics Kostovic, Aleksandar To speed up the process of monitoring, diagnosing, and fixing problems with multi-billion-dollar supercomputers researchers from the Laboratory and MIT have developed a new technology to visualize node monitoring, offering real-time system reporting in the Unity 3D game engine found in many video games.

2021-10-29T00:00:00Z Foy, Kylie https://hdl.handle.net/1721.1/131243 2021-09-09T03:16:44Z 2021-07-08T00:00:00Z

Air Force Pilots Get an AI-Assist With Scheduling Aircrews Foy, Kylie Scheduling C-17 aircraft crews is complicated. It’s a pain point for Airmen of 52 squadrons who operate C-17s, the military cargo aircraft that transport troops and supplies globally. This year, the Air Force marked four million flight hours for its C-17 fleet, which comprises 275 U.S. and allied aircraft. Each flight requires scheduling a crew of six on average. A team spanning MIT Lincoln Laboratory, the Department of the Air Force and the MIT Department of Aeronautics and Astronautics collaborated with their Air Force sponsor organization to develop an AI–enabled plugin for the existing C-17 scheduling tool that automates C-17 aircrew scheduling and optimizes crew resources.

2021-07-08T00:00:00Z https://hdl.handle.net/1721.1/131233 2021-09-03T03:08:45Z 2017-01-18T00:00:00Z

MIT Lincoln Laboratory Takes the Mystery Out of Supercomputing In this special guest feature, Dr. Jeremy Kepner from MIT Lincoln Laboratory describes the lab’s approach to developing algorithms that will keep their users productive as new processing technologies evolve.

2017-01-18T00:00:00Z Russell, John https://hdl.handle.net/1721.1/131232 2021-09-03T03:00:47Z 2019-03-11T00:00:00Z

MIT Lincoln Lab Offers Advice for Delivering On-Demand HPC Russell, John Looking for advice on how to deliver HPC to a diverse science user community? MIT’s Lincoln Laboratory has just posted a new paper – Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers – intended to fill the bill.

2019-03-11T00:00:00Z https://hdl.handle.net/1721.1/131231 2021-09-02T03:33:11Z 2016-12-26T00:00:00Z

Capitalizing on Machine Learning - from Life Sciences to Financial Services The promise of machine learning has a science fiction flavor to it: computer programs that learn from their experiences and get better and better at what they do. So is machine learning fact or fiction? The global marketplace answers this question emphatically: Machine learning is not just real; it is a booming field of technology that is being applied in countless artificial intelligence (AI) applications. In the life sciences arena, researchers are leveraging machine learning in their work to drive groundbreaking discoveries that may help improve the health and wellbeing of people.

2016-12-26T00:00:00Z https://hdl.handle.net/1721.1/131230 2021-09-02T03:25:40Z 2017-01-11T00:00:00Z

Supercharging Big Data Research with New England’s Fastest Supercomputer ISTC for Big Data caught up with ISTC for Big Data Principal Investigator and Lincoln Laboratory fellow Dr. Jeremy Kepner, who heads the LLSC, to learn more about Lincoln Laboratory's new supercomputer, how it’s helping ISTC research, and his work for the ISTC.

2017-01-11T00:00:00Z https://hdl.handle.net/1721.1/131229 2021-09-02T03:11:09Z 2021-09-01T00:00:00Z

Hydra-zen Framework Makes Scientific Computing Easier for Researchers Hydra-zen is a framework that allows researchers to better document and implement their changes to variables and settings used to perform complex experiments and save them alongside the resulting calculations, ensuring that the experiment can be duplicated. Hydra-zen aims to simplify and automate the scientific computing process.

2021-09-01T00:00:00Z https://hdl.handle.net/1721.1/131228 2021-09-02T03:29:49Z 2021-08-23T00:00:00Z

2020 IEEE Boston Section Distinguished Service Award Recipient The 2020 recipient of the IEEE Boston Section’s “Distinguished Service Award” (DSA) is Dr. Albert Reuther.

2021-08-23T00:00:00Z https://hdl.handle.net/1721.1/131150 2021-08-10T03:14:44Z 2021-08-09T00:00:00Z

Award-Winning Tools Enable Agencies to Efficiently and Easily Analyze Surveillance Footage Closed-circuit television and other surveillance systems are commonplace in busy, high-traffic areas. However, when investigators need to use these systems to search through recorded footage, these systems’ clunky interfaces and tools can create hours of work just in organizing footage. The Forensic Video Exploitation and Analysis (FOVEA) suite of tools, developed at the Laboratory in response to the Boston Marathon bombing in 2013, is an easy to set up and use solution to this problem.

2021-08-09T00:00:00Z Chu, Jennifer https://hdl.handle.net/1721.1/131140 2021-08-06T03:23:09Z 2020-03-23T00:00:00Z

MIT joins White House supercomputing effort to speed up search for Covid-19 solutions Chu, Jennifer MIT joins a consortium of supercomputing facilities to help speed the search for COVID-19 solutions.

2020-03-23T00:00:00Z https://hdl.handle.net/1721.1/131137 2021-08-03T03:22:35Z 2021-08-02T00:00:00Z

Dr. Jeremy Kepner Named Vice Chair of the Society for Industrial and Applied Mathematics (SIAM) Data MIning and Analytics Activity Group Dr. Jeremy Kepner has been elected vice chair of the Society for Industrial and Applied Mathematics (SIAM) Activity Group on Data Mining and Analytics (SIAG/DMA) for the 2014-2015 term. SIAM Activity Groups (SIAGs) provide a more focused forum for SIAM members interested in exploring one of the areas of applied mathematics, computational science, or applications.

2021-08-02T00:00:00Z https://hdl.handle.net/1721.1/131135 2021-07-30T03:28:47Z 2021-07-29T00:00:00Z

Dr. Vijay Gadepally Receives AFCEA International’s 40 Under 40 Award Dr. Vijay Gadepally was selected as a recipient of the Armed Forces Communications and Electronics Association (AFCEA) International’s 40 Under 40 Award for his work in the establishment and continued growth of the Lincoln Laboratory Supercomputing Center, which hosts New England’s most powerful supercomputer.

2021-07-29T00:00:00Z https://hdl.handle.net/1721.1/131126 2021-07-23T03:16:09Z 2021-05-14T00:00:00Z

Supercomputing Center Celebrates Fifth Anniversary The Lincoln Laboratory Supercomputing Center (LLSC) celebrated a significant anniversary this year, marking five years of the center’s mission to enhance the computing power available to the Laboratory, MIT, and other researchers.

2021-05-14T00:00:00Z https://hdl.handle.net/1721.1/131125 2021-07-23T03:16:32Z 2021-05-14T00:00:00Z

Cadets Collaboratively Intern Through the Air Force-MIT Artificial Intelligence Accelerator The Department of the Air Force-MIT Artificial Intelligence Accelerator (DAF-MIT AIA) is a collaboration between the Air and Space Forces and MIT to create new technology that will help the Air Force better complete their mission. The demand for interns on this program was fulfilled by ROTC cadets from Lincoln Laboratory and MIT.

2021-05-14T00:00:00Z https://hdl.handle.net/1721.1/130524 2021-04-27T03:20:54Z 2021-04-23T00:00:00Z

Staff Member Selected as 2021 SIAM Fellow The Society for Industrial and Applied Mathematics has selected Dr. Jeremy Kepner, Laboratory Fellow, Supercomputing Center, as an esteemed member of its 2021 class of SIAM Fellows.

2021-04-23T00:00:00Z https://hdl.handle.net/1721.1/130523 2021-04-27T03:28:18Z 2021-02-05T00:00:00Z

LINEAR Is On the Watch for Potentially Hazardous Asteroids NASA estimates that an asteroid the size of a car enters Earth’s atmosphere about once a year, creating a great fireball while burning up before reaching Earth’s surface; and roughly every 2,000 years, a football-stadium-sized meteoroid strikes Earth potentially causing significant damage. When will the next dangerous asteroid penetrate the atmosphere and seriously impact the Earth? Could that next asteroid be large enough to jeopardize civilization or the future of the human species? The Laboratory has been working since the late 1990s to help with the discovery and characterization of potentially hazardous asteroids. Laboratory researchers have found approximately one quarter of all known near-Earth objects (NEOs) that are at least 140 meters (460 feet) in size—large enough to have significant regional effects were they to impact the Earth.

2021-02-05T00:00:00Z https://hdl.handle.net/1721.1/130137 2021-03-16T03:20:13Z 2021-03-05T00:00:00Z

Bug-Injecting System Helps to Advance the State-of-the-Art in Debugging Software Bug finding systems are used after developers have written code to try to identify mistakes they have made. If these systems find a bug, they can be fixed before code is deployed. Unfortunately, these systems fail to find many bugs, which is one of the reasons why new vulnerabilities and crashes still exist in computer programs today. The scarce documentation of known bugs and how those bugs manifest in a program made it impossible to measure the success of bug-finding tools. The Large-scale Automated Vulnerability Addition (LAVA) system enables evaluation of bug-finding systems. The LAVA system produces thousands of realistic bugs that are automatically injected into pre-existing program code. Once these bugs are injected, various vulnerability discovery techniques and software can be tested to see how many of the bugs are found and how many are missed. Over the last five years, LAVA has become the first widely used benchmark for evaluation of bug-finding systems.

2021-03-05T00:00:00Z Martineau, Kim https://hdl.handle.net/1721.1/128286 2020-11-03T03:28:26Z 2020-02-11T00:00:00Z

Brainstorming energy-saving hacks on Satori, MIT’s new supercomputer Martineau, Kim Students participated in the Green AI Hackathon, co-sponsored by the MIT Research Computing Project and MIT-IBM Watson AI Lab, to explore methods for making artificial intelligence faster and more sustainable.

2020-02-11T00:00:00Z Ryan, Dorothy https://hdl.handle.net/1721.1/128285 2020-11-03T03:29:43Z 2019-03-12T00:00:00Z

Wilkinson Prize goes to developers of flexible Julia programming language Ryan, Dorothy Programmers developing applications for fields as diverse as astronomy, economics, artificial intelligence, energy optimization, and medicine often found themselves creating software with languages that offered slow computation. But in this era of big data, dynamic, flexible, and easy-to-implement code is required for programmers to efficiently build high-performance software tools needed for intensive data analysis. Enter Julia, an open-source language for advanced technical computing and data science.

2019-03-12T00:00:00Z Mandelbaum, Ryan F. https://hdl.handle.net/1721.1/128284 2020-11-03T03:15:05Z 2020-03-23T00:00:00Z

How Supercomputers Are Helping to Fight COVID-19 Mandelbaum, Ryan F. A host of companies, including IBM, Microsoft, and Google, along with universities and national labs have teamed up to form the COVID-19 High Performance Computing (HPC) Consortium. This new partnership is designed to provide scientists with supercomputing resources as they figure out how to combat the coronavirus-caused disease known as COVID-19.

2020-03-23T00:00:00Z Foye, Kylie https://hdl.handle.net/1721.1/128256 2020-10-30T03:06:43Z 2019-06-17T00:00:00Z

An algorithm with an eye for visibility helps pilots in Alaska Foye, Kylie In remote areas of Alaska, pilots check current or forecasted weather conditions before they fly, but a lack of automated weather observation stations throughout the Alaskan bush makes it hard to know exactly what to expect. To help, the FAA recently installed 221 web cameras near runways and mountain passes. Pilots can look at the image feeds online to plan their route. Still, it’s difficult to go through what could be hundreds of images and estimate just how far one can see. Laboratory staff have been working with the FAA to turn these web cameras into visibility sensors and have developed an algorithm, called Visibility Estimation through Image Analytics (VEIA), that uses a camera’s image feed to automatically determine the area’s visibility. These estimates can then be shared among forecasters and with pilots online in real time.

2019-06-17T00:00:00Z https://hdl.handle.net/1721.1/128255 2020-10-30T03:11:13Z 2019-06-17T00:00:00Z

Record-breaking DNA comparisons drive fast forensics DNA forensics is a powerful tool, yet it presents a computational scaling problem when it is improved and expanded for complex samples (those containing DNA from more than one individual) and kinship analysis. Laboratory staff developed an integrated web-based platform that provides expanded comparison capabilities without compromising speed or functionality. These new algorithms encode genetic markers as bits to allow for fast DNA comparisons in forensics.

2019-06-17T00:00:00Z https://hdl.handle.net/1721.1/128254 2020-10-30T03:16:34Z 2019-08-23T00:00:00Z

Video and Imagery Dataset to Drive Public Safety Capabilities Laboratory staff have been developing a computer vision dataset of operational and representative public safety scenarios. This dataset will enable technology development tailored to public safety scenarios, and includes operational images and videos from several organizations. They have labeled images so that machine learning algorithms can recognize a wide range of relevant public safety features in different environments. “The information within these images could improve various aspects of a response and recovery effort, such as damage assessment. Our dataset will enable the development of machine-learned analytics to prioritize and characterize images.”

2019-08-23T00:00:00Z https://hdl.handle.net/1721.1/128250 2020-10-30T03:08:04Z 2015-05-08T00:00:00Z

MIT SuperCloud The “big data” problem is pervasive across the DoD and is most commonly characterized by the three “Vs” of big data: volume, velocity, and variety. Now, increasingly, a fourth “V”, veracity (i.e., security), is becoming prominent. The MIT SuperCloud project has demonstrated significant quantitative impact on all of these areas. A key element of the MIT SuperCloud is its database management system, which allows unlimited instances of the National Security Agency (NSA)–developed Apache Accumulo database to be deployed on a project-by-project basis. This capability has moved Lincoln Laboratory to the forefront of the DoD big data community, as Accumulo becomes an increasingly widely used database for the U.S. intelligence community.

2015-05-08T00:00:00Z https://hdl.handle.net/1721.1/128247 2020-10-30T03:35:12Z 2016-04-15T00:00:00Z

Establishment of the Lincoln Laboratory Supercomputing Center Lincoln Laboratory has been a world leader in interactive supercomputing since the 1950s. Recently, the Laboratory acknowledged the importance of the LLGrid world-class computing capability with the establishment of the Lincoln Laboratory Supercomputing Center (LLSC) on 1 April. “By establishing the LLSC, Lincoln Laboratory will be able to better address supercomputing needs across all Laboratory missions, develop new supercomputing capabilities and technologies, and spawn even closer collaborations with MIT campus supercomputing initiatives,” said Dr. Jeremy Kepner, Laboratory Fellow, and head of the Supercomputing Center.

2016-04-15T00:00:00Z https://hdl.handle.net/1721.1/128236 2020-10-30T03:29:17Z 2016-09-09T00:00:00Z

A Tiny Organism with a Big Data Problem Prochlorococcus is the smallest and most abundant photosynthetic organism on earth. Despite its tiny size, it’s an organism of global importance. In recent decades, researchers have sequenced the organisms’ genomes. Advances in sequencing technologies have generated massive databases of ocean genomic data from around the world. So while there is rich data available about Prochlorococcus, mining the value of this Big Data is difficult because it requires simultaneously analyzing various types of complex information. For the past six months, the team has worked with the Chisholm Lab at MIT to develop applications within their BigDAWG architecture to fit the specific needs of the lab.

2016-09-09T00:00:00Z https://hdl.handle.net/1721.1/128124 2020-10-20T03:20:08Z 2016-10-14T00:00:00Z

Expanding Air Traffic Controllers’ View of Offshore Weather Researchers at the Laboratory, working with the Federal Aviation Administration, have designed the Offshore Precipitation Capability (OPC) to address the lack of airspace situational awareness for aircraft traversing oceanic sectors of the National Airspace System (NAS).

2016-10-14T00:00:00Z https://hdl.handle.net/1721.1/128123 2020-10-20T03:29:13Z 2016-12-02T00:00:00Z

Laboratory’s Supercomputing System Ranked Most Powerful in New England The new computing system, TX-Green, at the Lincoln Laboratory Supercomputing Center (LLSC) has been named the most powerful supercomputer in New England and the 3rd most powerful at a United States university on the TOP500 list of the world’s 500 most powerful supercomputers.

2016-12-02T00:00:00Z https://hdl.handle.net/1721.1/128122 2020-10-20T03:30:04Z 2017-02-10T00:00:00Z

Graph Processor Prototype In order to achieve significantly better graph computation performance, an advanced multiprocessor architecture has been developed that is optimized for analysis of large databases.

2017-02-10T00:00:00Z https://hdl.handle.net/1721.1/128121 2020-10-20T03:07:21Z 2017-04-14T00:00:00Z

Staff Release Open Source Software for BigDAWG Polystore System

2017-04-14T00:00:00Z https://hdl.handle.net/1721.1/128118 2020-10-20T03:08:57Z 2017-06-16T00:00:00Z

Enabling Massive Computation and Resiliency in the Internet-of-Things Era The Internet of Things (IoT), an ever-growing network of physical devices connected to the Internet, brings a unique set of challenges to the Department of Defense (DoD). These challenges include the billions of connected devices, the tremendous diversity of the data being generated by these devices, and the varied defenses required to protect the applications. To address challenges in working with diverse datasets, Laboratory staff and university collaborators created the BigDAWG polystore system.

2017-06-16T00:00:00Z https://hdl.handle.net/1721.1/128116 2020-10-20T03:00:25Z 2018-02-09T00:00:00Z

DataSToRM: Data Science and Technology Research Environment By analyzing graphs using specialized algorithms, complex relationships and deeper insight can be extracted from the raw information. In the last several years, the Laboratory has developed the Graph Processor, which has a unique hardware architecture that provides 100 to 1000 times better processing performance for analyzing large graph datasets. The Data Science and Technology Research Environment (DataSToRM) program is developing a software environment and algorithms to take advantage of the Graph Processor’s capabilities.

2018-02-09T00:00:00Z https://hdl.handle.net/1721.1/128115 2020-10-20T03:35:13Z 2018-06-08T00:00:00Z

Winds Forecast Rapid Prototype Wind causes significant problems for aviation, yet the Federal Aviation Administration has limited tools for forecasting its impact. MIT Lincoln Laboratory is developing technology to generate improved wind forecasts through the use of data fusion and machine learning.

2018-06-08T00:00:00Z https://hdl.handle.net/1721.1/128114 2020-10-20T03:31:02Z 2018-08-10T00:00:00Z

New Textbook Applies Mathematics to the Management of Big Data Mathematics of Big Data is the first book to present the common mathematical foundations of big data analysis across a range of applications and technologies.

2018-08-10T00:00:00Z https://hdl.handle.net/1721.1/128113 2020-10-20T03:18:30Z 2019-01-11T00:00:00Z

Lidar Scans Over the Carolinas Accelerate Hurricane Recovery The Federal Emergency Management Agency (FEMA) called upon MIT Lincoln Laboratory to use its state-of-the-art light detection and ranging (lidar) system to image the destruction from hurricane Florence. A high-resolution 3D model of the scanned area depicts the heights of structures and landscape features. Laboratory analysts can then process this data to glean information that helps FEMA focus their recovery efforts—for example, by estimating the number of collapsed houses in an area, the volume of debris piles, and the reach of flood waters.

2019-01-11T00:00:00Z McGovern, Anne https://hdl.handle.net/1721.1/128111 2020-10-20T03:22:44Z 2019-01-18T00:00:00Z

Supercomputers Can Spot Cyber Threats McGovern, Anne Lincoln Laboratory researchers have developed a technique to compress hours of internet traffic into a bundle that can be analyzed for suspicious behavior.

2019-01-18T00:00:00Z https://hdl.handle.net/1721.1/128108 2020-10-20T03:21:11Z 2019-02-15T00:00:00Z

Creating Synthetic Radar Imagery Using Convolutional Neural Networks Weather radar can track the location and intensity of storms and is useful for managing transportation around hazardous weather. Air traffic controllers, for example, rely on weather radar to track storms that could impact aircraft and flight schedules. Although land-based radar is sufficient to cover most continental air space, many offshore and oceanic controllers do not have sufficient access to the weather information that they need for proper air traffic management. Researchers at MIT Lincoln Laboratory developed the Offshore Precipitation Capability (OPC), a system that creates a radar-like depiction of precipitation — known as synthetic radar — by combining data from multiple nonradar sources, and implementing convolutional neural networks into the OPC system as a means of combining the nonradar data sources to create synthetic radar.

2019-02-15T00:00:00Z https://hdl.handle.net/1721.1/128107 2020-10-20T03:04:44Z 2019-07-12T00:00:00Z

MIT and U.S. Air Force Sign Agreement to Launch AI Accelerator MIT and the U.S. Air Force have signed an agreement to launch a new program designed to make fundamental advances in artificial intelligence that could improve Air Force operations while also addressing broader societal needs. The new program will focus on rapid deployment of artificial intelligence innovations in operations, disaster response, and medical readiness.

2019-07-12T00:00:00Z https://hdl.handle.net/1721.1/128029 2020-10-17T03:13:37Z 2019-11-01T00:00:00Z

Enabling the Foundations of AI: Data, Computation, and Algorithms MIT Lincoln Laboratory staff in the Lincoln Laboratory Supercomputing Center are developing tools to address challenges in data management and algorithmic techniques for novel neural network architectures to enable rapid prototyping of AI.

2019-11-01T00:00:00Z https://hdl.handle.net/1721.1/128028 2020-10-17T03:32:30Z 2019-12-06T00:00:00Z

LLx Project Seeks to Improve Online Hands-On Learning Online learning has been around for decades, but educators still struggle to adapt certain hands-on subjects to the web. At MIT Lincoln Laboratory, a team of researchers has been working on ways to close this practical learning gap through the Lincoln Laboratory Online Courses (LLx) project. The project was born from a desire to adapt the Laboratory’s internal course offerings for a wider audience. Its goal is to identify best practices for online hands-on learning in order to provide unique Laboratory classes to sponsors, students, and the general public as self-paced, massively open online courses (MOOCs).

2019-12-06T00:00:00Z https://hdl.handle.net/1721.1/128027 2020-10-17T03:11:58Z 2020-03-20T00:00:00Z

Cybersecurity Phenomenology Exploration

2020-03-20T00:00:00Z https://hdl.handle.net/1721.1/128020 2020-10-17T03:10:59Z 2020-05-15T00:00:00Z

New Algorithm Uses Supercomputing to Combat Cyber Attacks Sophisticated cyber attacks are on the rise. Early techniques for cyber attacks, such as guessing passwords manually, have evolved throughout the years—from session hijacking to ransomware and beyond. In order to manage this tricky cyber attack landscape, analysts need automated tools that can accurately detect and classify threats. Researchers are using machine learning to characterize anomalous behavior within a cyber network.

2020-05-15T00:00:00Z https://hdl.handle.net/1721.1/128002 2020-10-15T03:11:32Z 2020-06-12T00:00:00Z

High-Fidelity Multi-Physics Simulations for Hypersonics

2020-06-12T00:00:00Z https://hdl.handle.net/1721.1/128000 2020-10-15T03:13:44Z 2019-07-19T00:00:00Z

The Laboratory’s New AI Supercomputer is the Most Powerful at any University The new TX-GAIA computing system at the Lincoln Laboratory Supercomputing Center (LLSC) has been ranked as the most powerful artificial intelligence (AI) supercomputer at any university in the world. The ranking comes from TOP500, which publishes a list of the top supercomputers in various categories biannually.

2019-07-19T00:00:00Z https://hdl.handle.net/1721.1/127997 2020-10-15T03:26:50Z 2020-06-26T00:00:00Z

Staff Build Robust Algorithms to Strengthen Machine Learning Methods “Our ultimate goal is to provide the researchers and stakeholders of our community with a set of robustness tools, techniques, and best practices so that they can embrace the great promise of machine learning technology with the confidence that they can meet the safety and security demands that are specific to the national security domain,”

2020-06-26T00:00:00Z https://hdl.handle.net/1721.1/127995 2020-10-15T03:12:44Z 2020-08-28T00:00:00Z

Mission-ready Reinforcement Learning The Mission-ready reinforcement learning (MeRLin) program is looking to solve complex planning and coordination problems across a range of Department of Defense mission areas. MeRLin is focusing on developing and training Deep reinforcement learning (DRL) algorithms capable of maintaining performance on complex tasks with human allies.

2020-08-28T00:00:00Z