Machine Learning Research Group

Mission

The mission of the Machine Learning Research Group (MLRG) is to scale Machine Learning (ML) across Oracle. We do this by:

  • Undertaking fundamental ML research. MLRG members' research interests cover a wide range of ML work.
  • Collaborating with product groups and business units world-wide in order to develop ML-based solutions that will improve their products and services.
  • Building proof-of-concept systems that demonstrate the viability of an ML approach to solving particular business problems. The MLRG will transfer both the code and the ML methodology to the product group.
  • Helping product groups understand how to operationalize Machine Learning solutions in order to get the maximum benefit from them.
  • Acquiring and building tools for ML.
  • Building an ML community at Oracle. The MLRG hosts an internal ML summit each year that brings together ML practitioners from across Oracle.
  • Managing data acquisition and storage for ML work.

Collaborations

The MLRG collaborates with a number of groups inside Oracle.

Oracle Sales

We have been working with Oracle's Sales team to understand how we can use ML to optimize Oracle's sales process. We have built lead scoring algorithms that identify the leads that are most likely to convert and are working on models that help us understand our customers future product needs.

Fusion Financials

We have been working with the Fusion Financials team to understand how Natural Language Processing techniques can be used to automatically extract useful information from invoices and receipts.

Oracle Digital Assistant

We have been working with the Oracle Digital Assistant team on a variety of Natural Language Processing projects, including investigating scalable, fault-tolerant, multi-lingual contextual embeddings.

Research Interests

The MLRG's research interests span a wide range of topics in ML and NLP.

Core Machine Learning

  • Fairness and privacy in ML
  • Feature selection
  • The mathematical underpinnings of ML

Statistical Natural Language Processing

  • Model-based sentiment analysis
  • Named entity recognition
  • Entity linking and coreference resolution
  • Product attribute extraction
  • Large-scale, multilingual contextual embedding models

Scalable Machine Learning

  • Probabilistic Programming
  • Parallel inference
  • Machine Learning on GPUs

Deep Learning

  • Transfer learning in image recognition models for cancer detection
  • Deep learning for NLP


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