This week the Association for Computer Machinery presented CSAIL principal investigator Daniel Jackson with the 2017 ACM SIGSOFT Outstanding Research Award for his pioneering work in software engineering. (This fall he also received the ACM SIGSOFT Impact Paper Award for his research method for finding bugs in code.)An EECS professor and associate director of CSAIL, Jackson was given the Outstanding Research Award for his “foundational contributions to software modeling, the creation of the modeling language Alloy, and the development of a widely used tool supporting model verification.”
In recent years, a host of Hollywood blockbusters — including “The Fast and the Furious 7,” “Jurassic World,” and “The Wolf of Wall Street” — have included aerial tracking shots provided by drone helicopters outfitted with cameras. Those shots required separate operators for the drones and the cameras, and careful planning to avoid collisions. But a team of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and ETH Zurich hope to make drone cinematography more accessible, simple, and reliable.
When a power company wants to build a new wind farm, it generally hires a consultant to make wind speed measurements at the proposed site for eight to 12 months. Those measurements are correlated with historical data and used to assess the site’s power-generation capacity.This month CSAIL researchers will present a new statistical technique that yields better wind-speed predictions than existing techniques do — even when it uses only three months’ worth of data. That could save power companies time and money, particularly in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly costly.
Self-driving cars are likely to be safer, on average, than human-driven cars. But they may fail in new and catastrophic ways that a human driver could prevent. This project is designing a new architecture for a highly dependable self-driving car.
The Arabic language is spoken by over one billion people around the world. Arabic presents a variety of challenges for speech and language processing technologies. In our group, we have several research topics examining Arabic, including dialect identification, speech recognition, machine translation, and language processing.
Automatic speech recognition (ASR) has been a grand challenge machine learning problem for decades. Our ongoing research in this area examines the use of deep learning models for distant and noisy recording conditions, multilingual, and low-resource scenarios.
Our main goal is to automatically search for relevant answers among many responses provided for a given question (Answer Selection), and search for relevant questions to reuse their existing answers (Question Retrieval).
We are interested in applying insights from distributed computing theory to understand how ants and other social insects work together to perform complex tasks such as foraging for food, allocating tasks to workers, and choosing high quality nest sites.
Our main goal is to develop fact checking algorithms that can assess the credibility of claims mentioned in the textual statements and provide interpretable valid evidence that explains why a certain claim is considered as factually true or fake.
Our goal in this project is to understand how one can test if a particular dealer's shuffles follow a certain pattern. We have developed a theoretical framework for the same and wish to understand its performance in practice.
One of the challenges of processing real-world spoken content, such as automatic speech recognition, is the potential presence of different languages and dialects. Language and Dialect identification can be a useful capability to identify which language is being spoken during a recording.
Our project focuses on developing a general human motion prediction framework that can be applied in a variety of domains, ranging from manufacturing to space robotics, in order to improve the safety and efficiency of human-robot interaction.
We develop algorithms, systems and software architectures for automating reconstruction of accurate representations of neural tissue structures, such as nanometer-scale neurons' morphology and synaptic connections in the mammalian cortex.
Our goal is to create a theoretical framework and effective machine learning algorithms for robust, reliable control of autonomous vehicles. Key threads include developing metrics of confidence; and designing deep learning algorithms for parallel autonomy.
In this project, we aim to develop a framework that can ensure and certify the safety of an autonomous vehicle. By leveraging research from the area of formal verification, this framework aims to assess the safety, i.e., free of collisions, of a broad class of autonomous car controllers/planners for a given traffic model.
This week it was announced that MIT professor and CSAIL principal investigator Tomas Lozano-Perez has been awarded the 2021 IEEE Robotics and Automation Award for his “foundational contributions to robot motion planning and visionary leadership in the field.”