A Greater Boston RIG project
Virtual devices are the most common site for security bugs in hypervisors. In our evaluation, we found new bugs in devices such as serial and virtio-net, ranging from memory corruptions to denial-of-service vulnerabilities. By combining well known coverage guidance techniques with domain-specific feedback, we found promising fuzzer performance, even for complex targets such as hypervisors.
No longer the realm of supercomputing labs and theoretical research, AI/ML is just beginning to expand its practical applications. The combination of academic approaches and industry use cases promises to accelerate its adoption. Our research spans the gamut of algorithm development, data hygiene, AI methods, and attacking old challenges with AI approaches.
|Adaptive Learning of Programming||The mission of Adaptive Learning of Programming is to make learning more efficient and engaging by personalizing educational systems using artificial intelligence techniques.||AI-ML||Brno||Masaryk University||brno||masaryk-university|
|OpenInfra Labs||OpenInfra Labs is an OpenStack Foundation project connecting open source projects to production to advance …||AI-ML, Cloud-DS, Hardware and the OS, Security, Privacy, Cryptography, Testing and Ops||Boston University, Northeastern University, UMass Amherst||boston-university northeastern-university umass-amherst|
|Open Cloud Testbed||The Open Cloud Testbed project will build and support a testbed for research and experimentation into new cloud platforms – the underlying software which provides cloud services to applications. Testbeds such as OCT are critical for enabling research into new cloud technologies – research that requires experiments which potentially change …||AI-ML, Cloud-DS, Hardware and the OS, Security, Privacy, Cryptography, Testing and Ops||Greater Boston||Boston University, Northeastern University, UMass Amherst||greater-boston||boston-university northeastern-university umass-amherst|
|Complex Event Processing||The aim of this project is to create a scalable open-source complex event processing (CEP) framework.||AI-ML||Tel Aviv||Technion||tel-aviv||technion|
|Predictive Analysis – Fault Tolerance||The goal of this project is to build a system that shall utilize Predictive Analysis technology to create a state-of-the-art fault-tolerance system that can lead towards the ability to “predict” based upon past events if and when faults such as component failures may occur.||AI-ML||Greater Boston, Tel Aviv||greater-boston tel-aviv|
|Electroencephalography (EEG) Feature Extraction||The research is meant to enable improvement of the management of patients with ESES. Electrical status epilepticus during slow wave sleep (ESES) is a rare age related disorder, appears in childhood, usually between ages 4 and 9 years, and disappears by puberty.||AI-ML||Tel Aviv||Tel Aviv University||tel-aviv||tel-aviv-university|
|Ceph: Wire-Level Compression-Efficient Object Storage Daemon Communication for the Cloud||The project’s purpose is to reduce storage network traffic (object, block, etc.) for the following cases: between the failure domains in cost-sensitive environments such as public clouds, and between nodes in cases where the network bandwidth is the bottleneck of the node performance.||AI-ML||Tel Aviv||IDC Herzliya||tel-aviv||idc-herzliya|
|Code2Vec: Learning code representations||This project analyzed semantic similarities of learned code embeddings parsed from open source python libraries such as numpy, pandas and sklearn. Still in progress is another analysis that learns code embeddings in a supervised manner with the C++ codebase for performance measurement of program execution in CPU with performance counters …||AI-ML||Greater Boston||Boston University||greater-boston||boston-university|