Data Science, AI & Machine Learning
Data Science, artificial intelligence (AI) and machine learning involve making accurate predictions, data mining, machine learning, and more to guide business decisions. Research areas include: bio inspired control using data from animals, computational biology, computational imaging, cyber security, medical informatics, simulation, and video analytics.
Moths Teach Drones to Fly
Research is first to apply animal data to autonomous vehicle navigation When an autonomous drone is deployed for a mission, it flies on a specific, programmed route. But if there are any surprises along the way, the drone has a difficult time adapting to a change because it hasn’t been programmed on how to do […]
Science Robotics: A New Approach to Teaching Robots
The two robots, Jaco and Baxter, must work together to cook, assemble and serve a hot dog. They must detect and find their supplies, shown in the upper left-hand corner, which are tracked with motion capture throughout the experiment. A new machine-learning framework could be used to complete high-risk, complex tasks Machine learning can identify […]
Azer Bestavros to Lead BU’s Faculty of Computing and Data Sciences
Azer Bestavros, who gained international prominence as the founding director of BU’s Rafik B. Hariri Institute for Computing and Computational Science & Engineering, has been named associate provost for computing and data sciences, a new role that will lead the University’s efforts in the highly competitive and fast-growing field. He will lead the new Faculty […]
InTrans: Modular Security on an Open Cloud
This project explores the intriguing possibilities that result from the combination of two tools: cryptographic software that distributes any computing task over several machines with strong security guarantees as long as the machines are isolated, and a multi-provider cloud datacenter that offers to any tenant the ability to rent multiple isolated machines that are administered […]
Multiplication by Divisions
Materials, Systems Divisions Cross Traditional Barriers to Facilitate Research Fuel cell use could be one of the best ways to mitigate climate change—fuel cells work like batteries, provide efficient power and don’t emit air pollutants. But there are multiple barriers in research and development before they will be available to a commercial market. Professor Soumendra Basu […]
Abraham Matta Wins NSF Grant for Application Software Project
CISE Faculty Affiliate Abraham Matta, Professor (CS, SE) and a Hariri Research Fellow received a grant from the Division of Computer and Network Systems, National Science Foundation for his proposal titled “CNS Core: Small: Collaborative Research: HEECMA: A Hybrid Elastic Edge-Cloud Application Management Architecture”. This project will examine how application software can be partitioned and deployed over different parts of […]
SCH: INT: Distributed Analytics for Enhancing Fertility in Families
The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, involving both men and women, and resulting […]
AF: Small: Collaborative Research: New Representations for Learning Algorithms and Secure Computation
Recent success of machine learning is due in part to the availability of large datasets for training and testing purposes. However, the training process is computationally intensive and collected datasets are often privacy sensitive. This has led to providing Machine Learning as a Service (MLaaS), where data providers store their data in the cloud and […]
Optimizing and Learning Strategies for Protein Docking
Protein docking is defined as predicting the three-dimensional structure of the docked complex based on knowledge of the structure of the components. Experimental techniques for this purpose are often expensive, time-consuming, and in some cases, not feasible; hence the need for computational docking methods. The problem of finding the docked conformation is generally formulated as […]
Neuro-Autonomy: Neuroscience-inspired Perception, Navigation, and Spatial Awareness for Autonomous Robots
State-of-the-art Autonomous Vehicles (AVs) are trained for specific, well-structured environments and, in general, would fail to operate in unstructured or novel settings. This project aims at developing next-generation AVs, capable of learning and on-the-fly adaptation to environmental novelty. These systems need to be orders of magnitude more energy efficient than current systems and able to pursue complex goals in […]