Roni Rosenfeld

Professor, Machine Learning, Language Technologies, Computer Science, and Computational Biology

Bio

Roni Rosenfeld (BSc, mathematics and physics, Tel-Aviv University; PhD, computer science, Carnegie Mellon University) is a professor of machine learning, language technologies, computer science, and computational biology in the School of Computer Science at Carnegie Mellon University, Pittsburgh, Pennsylvania. He has taught machine learning and statistical language modeling to thousands of undergraduate and graduate students since 1997, and has been a mentor to five post-doctoral students and an advisor to a dozen PhD students and many Masters and undergraduate students. From 2018 till 2024, he served as head of the machine learning department.

Roni’s current research interests are in tracking and forecasting epidemics. The Delphi research group, which he co-founded and co-leads since 2012, has been playing a leading role in the development of epidemic forecasting technology in the U.S., and has been named a National Center for Epidemic Forecasting by the U.S. CDC.

Roni has previously worked in statistical language modeling, speech recognition, human machine speech interfaces, and the use of speech and language technologies to aid international developments. He has published some 150 scientific articles in academic journals and peer reviewed conferences, is a recipient of the Spira Teaching Excellence Award (2017), and twice the recipient of the Allen Newell Medal for Research Excellence (1992, 2022).

Education

PhD, Computer Science, Carnegie Mellon University, School of Computer Science, Pittsburgh, PA, United States • 1994

MSc, Computer Science, Carnegie Mellon University, School of Computer Science, Pittsburgh, PA, United States • 1991

BSc, Mathematics and Physics, Tel Aviv University, Tel Aviv, Israel • 1985

Research

Current:

Forecasting Epidemics: The long term vision of our Delphi research group is to make epidemiological forecasting as universally accepted and useful as weather forecasting is today. As was the case with weather forecasting, this will likely take a long time. In the shorter term, we select high value epidemiological forecasting targets (currently Influenza and Dengue); create baseline forecasting methods for them; establish metrics for measuring and tracking forecasting accuracy; estimate the limits of forecastability for each target; and identify new sources of data that could be helpful to the forecasting goal.

Past (no longer accepting students):

Information and Communication Technologies for Development (ICT4D), and specifically Spoken Language Technologies for Development (SLT4D), which is the term we coined for our own subfield of ICT4D: finding ways to use spoken language technologies (like automatic speech recognition, speech synthesis, and human-machine dialog systems) to aid socio-economic development around the world.

Machine Learning for Social Good (ML4SG): We continuously seek problems in non-profits and government organizations, domestically and abroad, which can benefit from machine learning solutions, and match them with suitable teams of students and supervising faculty. If your organization could use free machine learning or data science expertise to help improve its societal impact, please contact us. Best cases are those where the potential for societal impact is evident, the questions are well defined, and significant relevant data is available. Otherwise, we can work with you to get your problem ready for our students. This initiative is now overseen by Prof. Rayid Ghani.

Data Numeracy for All: I believe that universal data numeracy is as important in the 21st century as universal literacy was in the 20th. We need to increase the understanding of (and comfort with) data in all segments of society. I am interested in devising effective ways of doing that.undefinedundefined

Publications

All publications, including:

• Computational Epidemiology and Computational Biology
• Spoken Language Technologies for Development
• Human-Machine Speech Communication
• Statistical/Machine Learning Methods in Speech and Language Processing

My Favorite Quotes