Machine Learning Center of Excellence (MLCOE)
Unlocking firmwide value with AI
About Us
As the largest machine learning team within JPMC, we are a dynamic and forward-thinking group of over 200 machine learning scientists, software engineers, product managers, annotators, project managers, and designers. Our team thrives on a positive culture of collaboration and partnership, where we work together as thought leaders to drive innovation.
With deep expertise across a wide range of ML disciplines, including Natural Language Processing (NLP), Large Language Models (LLM), Speech Recognition, Time Series, and Reinforcement Learning, we are committed to delivering cutting-edge solutions. Positioned globally across three continents and in more than 10 cities, we are dedicated to servicing the needs of the business and its customers with excellence and creativity.
What we do
We deliver artificial intelligence and machine learning solutions that range from reusable libraries and components to comprehensive managed platforms, all aimed at solving complex business challenges. Our success is measured by the impact we create for businesses, whether through direct commercial gains, enhanced employee engagement, or improved customer experiences.
As an integral part of the Chief Data & Analytics Office, we collaborate with all lines of business and corporate functions to drive firmwide adoption of AI through innovative solutions and unique developments. Our efforts include:
- Leveraging our deep technical expertise to partner with the business and solve complex problems.
- Researching, experimenting, and innovating new ML technologies, specifically applied to unique data sets in the finance domain.
- Building cutting-edge, highly scalable solutions that serve millions of customers.
Publications
Published Papers
- Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors, December 2024. Utilized dataset distillation and low-rank fine-tuning to enhance Small Language Models (SLMs) for simulating user agents in recommender systems. Our experiments provide compelling empirical evidence of the efficacy of our methods, demonstrating that user agents developed using our approach have the potential to bridge the gap between offline metrics and real-world performance of recommender systems.
- Enhancing Contract Negotiations with LLM-Based Legal Document Comparison, October 2024. This approach is the first in the literature to produce a natural language comparison between legal contracts and their template documents.
- Systematic Evaluation of Long-Context LLMs on Financial Concepts, October 2024. Evaluated the performance of state-of-the-art GPT-4 suite of LC LLMs in solving a series of progressively challenging tasks, as a function of factors such as context length, task difficulty, and position of key information by creating a real world financial news dataset.
- When and how to paraphrase for named entity recognition?, May 2023. Utilized simple strategies to annotate entity spans in generations and compare established and novel methods of paraphrasing in NLP such as back translation, specialized encoder-decoder models such as Pegasus, and GPT-3 variants for their effectiveness in improving downstream performance for NER across different levels of gold annotations and paraphrasing strength on 5 datasets.
Published Patents
- System and Method for Implementing a Client Sentiment Analysis Tool, IDF-01374-US02
- System and Method for Implementing an Intelligent Customer Service Query Management and Routing System, IDF-01458-US02
- System and Method for Generating and Implementing Context Weighted Words, IDF-01581-US02
- Systems and Methods for Contingency NAV Pricing, IDF-01621-HK01
- Systems and Methods for Contingency Net Asset Value Pricing, IDF-01621-EP01
- Field Management Continuous Learning System and Method, IDF-02441-US02
In the News
