Laura Haas

Professor of Computer Science University of Massachusetts Amherst

Amherst MA

Laura Haas researches the use of large language models for data integration and data integration tools to support LLMs.

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Biography

Laura Haas is a professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. She joined the university in 2017 after a distinguished career at IBM, where she reached the rank of IBM Fellow. She directed multiple research efforts in data management, data science, and data integration, and served as director of IBM Research's Accelerated Discovery Lab (2011-2017), director of Computer Science at IBM's Almaden Research Center (2005-2011) and head of IBM Research's worldwide exploratory science program (2009-2013). Dr. Haas served as the first permanent dean of Manning CICS, overseeing significant growth in both size and stature, substantial increases in faculty and student diversity, the design and construction of a new academic building, and raising over $100 million in funding from government, industry and private philanthropy, including the transformative Manning endowment. She spearheaded numerous initiatives in quantum computing, robotics and public interest technology. She is currently re-engaging in research, focused on the use of LLMs for data integration and data integration tools to support LLMs.

Dr. Haas's honors include the Anita Borg Institute Technical Leadership Award, ACM SIGMOD Edgar F. Codd Innovation Award, IEEE Computer Society Computer Pioneer Award, and several prestigious IBM awards, including a Corporate Award for information integration technology. She is an ACM Fellow, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences.

Education

Harvard University

A.B.

Computer Science

1978

University of Texas at Austin

Ph.D.

Computer Science

1981

Select Recent Media Coverage

Local police chiefs weigh in on pitfalls, benefits of AI scanner apps

Greenfield Recorder print

2026-08-27

This article investigates incidents of emergency dispatch traffic being misinterpreted or falsely reported due to apps like CrimeRadar, which listen to real-time radio traffic that is then interpreted by AI to highlight ongoing emergency responses. Dr. Haas explains how human language interpretation and comprehension is different than that of a machine learning model, and how this can lead to inaccuracies.

Select Publications

Leveraging Data and People to Accelerate Data Science

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

Laura Haas

2017-04-19

Doing data science - extracting insight by analyzing data - is not easy. Data science is used to answer interesting questions that typically involve multiple diverse data sources, many different types of analysis, and often, large and messy data volumes. To answer one of these questions, several types of expertise may be needed to understand the context and domain being served, to import and transform individual data sets, to implement effective machine learning and/or statistical methods, to design and program applications and interfaces to extract and share data and insights, and to manage the data and systems used for analysis and storage. In the IBM Research Accelerated Discovery Lab, we are studying how data scientists work, and using what we learn to help them gain insights faster. In this talk, we will look at what we have learned to date, through user studies and experience with tens of analytics projects, and the environment that we’ve built as a result.

The power behind the throne: Information integration in the age of data-driven discovery

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Laura Haas

2015-05-27

Integrating data has always been a challenge. The information management community has made great progress in tackling this challenge, both on the theory and the practice. But in the last ten years, the world has changed dramatically. New platforms, devices and applications have made huge volumes of heterogeneous data available at speeds never contemplated before, while the quality of the available data has if anything degraded. Unstructured and semi-structured formats and no-sql data stores undercut the old reliable tools of schema, forcing applications to deal with data at the instance level. Deep expertise in the data and domain, in the tools and systems for integration and analysis, in mathematics, computer science, and business are needed to discover insights from data, but rarely are all of these skills found in a single individual or even team. Meanwhile, the availability of all these data has raised expectations for rapid breakthroughs in many sciences, for quick solutions to business problems, and for ever more sophisticated applications that combine and analyze information to solve our daily needs. These expectations raise the bar for integration technology, while opening the door for it to play a broader role. Integration has always been a key player in handling data variety, for example, but now more than ever must deal with scale (in the number of types as well as in the volume and speed of data). While data cleansing has been one step of an integration pipeline, this technology must be leveraged throughout data integration, so that the integration process is better able to deal with the uncertainty in data, offering means to eliminate or reduce it, or, to elucidate it by linking important contextual information, such as provenance and usage. The complexity of today's data-driven challenges in fact suggests that the integration process should be context-aware, so that data sets may be combined differently depending on the proposed usage.