This project is building an advanced pipeline for automating the machine learning including model selection, feature selection, hyper parameter tuning and model explainability (MLX)
Performance analysis of computer system architectures exploiting new technology trends using advanced workload characterization and modeling techniques.
Research project to explore distributed computing problems relevant to Big Data Analytics, Cloud Infrastructures and modern networking and storage technologies.
The KeyBridge project is exploring applying machine learning and deep learning techniques to OCI operational data to improve security, reliability and efficiency of Oracle Cloud.
This project aims to improve the interpretability, accuracy and performance of explaining (both local and global) machine learning models.
The mission of the Machine Learning Research Group is to scale Machine Learning across Oracle by researching and developing ML-based solutions that improve Oracle's products and services.
OSInt is a big data analytics project that aims to gather information about vulnerabilities in libraries
Parallel, Efficient, In-Memory Graph Processing supported by a high level domain-specific language
Parfait is a static code analysis tool from Oracle Labs that finds vulnerabilities in web applications written in C/C++, Java, and Python.
Programming language design, semantics, algorithms, and implementation with an emphasis on convenient use of parallelism using multicores and GPUs.
RAPID provides a scale out architecture for providing very high performance for SQL analytics. This technology has been optimized for OCI Gen 2 and will be available as MySQL analytics service
Project RASPunzel aims to deliver an automated and scalable runtime application self-protection (RASP) solution for Java.
Randomized Decomposition is a mathematical programming technique for solving hard, non-convex mathematical programming problems.
Exploration of new secure language concepts and secure abstractions that can be applied in future languages to prevent vulnerabilities in code written in those languages.
Bringing Modern Compiler Technology and Programming Languages to Data Processing Engines
Introduce self-optimization capabilities into Oracle's computer systems and processes based on adaptive learning methods
Affogato is a dynamic taint analysis engine for Node.js.
The Callisto project investigates how systems software can evolve to better support parallel and distributed runtime systems on tightly-coupled clusters and on large NUMA systems.
CAD system with schematic capture and layout features.
The project aims to develop FPGA offload engine to accelerate existing software in various high value applications including ingestion of data into engineered systems and Big Data applications.
A tool that lets you query a graph of extracted code dependencies to find symbols, show references, follow chains and determine the impact of changes to your multimillion-line codebase.
Finding security vulnerabilities in the Java Platform (i.e., the Java Development Kit) using static program analysis techniques.
The Memory Systems Research team identifies and develops technologies that improve memory system performance for targeted applications
Static program analysis techniques focusing on developing precise and scalable analyses for finding bugs in large-scale C and C++ source code.
Research project exploring potential effects of emerging byte-addressable persistent memory technologies on software and systems design.
Principles and techniques for developing concurrent programs that are scalable, efficient, and correct.
Soufflé: a Datalog compiler optimised for static program analysis, e.g. points-to and taint. It translates Datalog programs to efficient and parallel C++ programs and enables rapid prototyping.
Titan implements a peer-to-peer overlay network message routing service.
Develops high-speed circuit technologies and design methods that enable novel architectures.