Carlos Costa

Raghu Ganti

Nick Fuller

Bishwaranjan Bhattacharjee

Mudhakar Srivatsa

Kim Martineau

News

Research

IBM’s text-to-SQL generator takes top place on a benchmark for handling complex database queries

IBM’s CodeFlare significantly cuts the time to automate transfer learning tasks for foundation models 

We're creating a hybrid data fabric that provides secure, governed data access from anywhere, and minimizes total cost of ownership for AI and analytics.

Data Management

IBM Solution: Data Fabric

Hybrid Cloud

Reducing friction for scientific and foundation model workflows in Kubernetes.

Simplified and Performant Access to Data in the Cloud

An LLM-Powered System for Enterprise Data 

FlowPilot

A lineage data management system for tracking data in hybrid cloud deployments.

ProvLake

Pathfinder: Building the Enterprise Data Map

NeurIPS 2024

Big Data 2024

AAAI 2025

Foundation Models

Hybrid Cloud Platform

Machine Learning

Generative AI

Granite

AI can help sift through the noise to identify significant patterns in the increasingly large pools of data generated by enterprises. We’re reimagining IT operations, using AI to automate core processes, better detection and diagnosis, preformance monitoring, and explainable action recommendations.

AI for IT

As organizations move to the hybrid cloud, they must protect sensitive data and comply with regulations that allow them to take advantage of AI. We’re designing systems to monitor and protect data, building trust in AI through robust evaluation, certification, and hardening against attacks.

Data and AI Security

Designing systems to monitor and protect sensitive data for robust and secure AI systems on the hybrid cloud.

The future of computing lies in the hybrid cloud. We're creating a hybrid data fabric that provides secure,  governed data access from anywhere, enables self-service discovery of the right data at the right time, and takes a holistic view at minimizing total cost of ownership for AI and analytics.

The hybrid cloud can help businesses tap into the potential of the cloud, while retaining the benefits of private environments. At IBM Research, we’re pursuing open-source standards and code to create an agile and scalable platform that can be deployed anywhere.

We're building tools to help AI creators reduce the time they spend designing their models. Our goal is to allow non-experts across industries to build their own AI solutions, without writing complex code or performing tedious tuning and optimization.

Automated AI

IBM Research and Red Hat make OpenShift enterprise-ready

CodeFlare drastically reduces time to set up, run, scale machine-learning experiments

Data Management

Our work

IBM’s text-to-SQL generator takes top place on a benchmark for handling complex database queries

IBM’s CodeFlare significantly cuts the time to automate transfer learning tasks for foundation models

Publications

Preparing Good Data for Generative AI: Challenges and Approaches (Good-Data)

Question-guided Insights Generation for Automated Exploratory Data Analysis

Data Wrangling task automation using Code-Generating Language Models

Vector Quantization with Sorting Transformation

PerSSD: Persistent, Shared, and Scalable Data with Node-Local Storage for Scientific Workflows in Cloud Infrastructure

TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes

IBM Solution: Data Fabric

Projects

FlowPilot

Pathfinder: Building the Enterprise Data Map

Simplified and Performant Access to Data in the Cloud

ProvLake

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