PeopleofSuperintelligence

Researcher

Francesco Orabona

Francesco Orabona is an associate professor of computer science at KAUST whose work focuses on parameter-free machine learning, online learning, optimization and statistical learning theory, and who manages the OPTIMAL Lab. After earning electrical-engineering degrees at the University of Naples Federico II in 2003 and the University of Genoa in 2007, he held postdoctoral positions at the IDIAP Research Institute and the University of Milan. In 2012, he coauthored a guaranteed-convergence method for multi-kernel learning, followed by a parameter-free online-learning framework with Dávid Pál in 2016 and a 2017 paper with Tatiana Tommasi on training deep networks without learning rates. At Boston University from 2018 to 2023, he worked on optimization and machine learning, became an action editor of the Journal of Machine Learning Research in 2020, and won an NSF CAREER Award in 2021. KAUST appointed him associate professor in 2023, and he served in senior conference roles at NeurIPS, ICML and COLT in 2025 and 2026 while releasing a revised version of his book Online Learning: A Modern Introduction Using Convex Optimization in June 2026.

Timeline

  1. 2003–2007

    Electrical engineering degrees

    In 2003, he earns B.Sc. and M.S. degrees in electrical engineering at the University of Naples Federico II, and he completes a Ph.D. in electrical engineering at the University of Genoa in 2007.

  2. 2007–2011

    Postdoctoral roles at IDIAP and Milan

    From 2007 to 2009, he works as a postdoctoral researcher at the IDIAP Research Institute in Switzerland. He then serves as a postdoctoral researcher at the University of Milan from 2010 to 2011.

  3. 2012–2016

    Multi-kernel learning and coin betting

    With Luo Jie and Barbara Caputo, he publishes Multi Kernel Learning with Online-Batch Optimization, a stochastic-gradient algorithm for multi-kernel learning with guaranteed convergence, in the Journal of Machine Learning Research in 2012. In 2016, he and Dávid Pál coauthor Coin Betting and Parameter-Free Online Learning, a framework for parameter-free online linear optimization and learning with expert advice.

  4. 2017

    Deep networks and a Google award

    He and Tatiana Tommasi coauthor Training Deep Networks without Learning Rates Through Coin Betting at NIPS in 2017, introducing a stochastic-gradient procedure for deep networks that does not require learning-rate setting. Google Research also grants him a Google Research Award in 2017.

  5. 2018–2023

    Boston University and an NSF award

    From 2018 to 2023, he works in Boston University's Electrical & Computer Engineering Department on optimization and machine learning, where he manages the Optimization and Machine Learning Lab and wins the NSF CAREER Award in 2021 for research on more automated machine-learning algorithms. During this period, he becomes an action editor of the Journal of Machine Learning Research in 2020 and an associate editor of IEEE Transactions on Information Theory in 2022.

  6. 2023

    Associate professor at KAUST

    KAUST appoints him associate professor of computer science in 2023; before joining KAUST, his career includes positions at Stony Brook University, Yahoo Research, the Toyota Technological Institute at Chicago, the University of Milan and the Idiap Research Institute. In 2023, he also co-chairs the International Conference on Algorithmic Learning Theory (ALT).

  7. 2025–2026

    Conference roles at COLT, ICML, NeurIPS

    In 2025, he serves as Senior Area Chair of NeurIPS and ICML, and in 2026 he serves as Senior Area Chair of ICML and Senior PC Member of COLT. After submitting the book Online Learning: A Modern Introduction Using Convex Optimization to arXiv in December 2019, he releases a revised version in June 2026.

  8. As of October 2026

    Managing the OPTIMAL Lab

    He is an associate professor of computer science in the Computer, Electrical and Mathematical Sciences and Engineering Division at KAUST, where he manages the OPTIMAL Lab and serves as principal investigator for Adaptive Machine Learning. His research interests include parameter-free machine learning, online learning, batch/stochastic optimization and statistical learning theory.

Career

  1. NowKing Abdullah University of Science and TechnologyAssociate Professor of Computer Science
  2. 2018–2023Boston University

Education

  1. 2007University of GenoaPh.D. in electrical engineering
  2. 2003University of Naples Federico IIB.Sc. and M.S. in electrical engineering

From the biography.

  1. The United States employs an estimated 48% of people shaping AIIncludes King Abdullah University of Science and Technology. We estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes King Abdullah University of Science and Technology. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes King Abdullah University of Science and Technology. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. The median h-index is 48 at universities and 14 at frontier AI labsIncludes King Abdullah University of Science and Technology. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Francesco Orabona | Associate Professor, Computer Science | Computer, Electrical and Mathematical Sciences and Engineering cemse.kaust.edu.sa. Checked 22 October 2026.
  2. Francesco Orabona - Associate Professor, Computer Science - King Abdullah University of Science and Technology kaust.edu.sa. Checked 22 October 2026.
  3. Multi Kernel Learning with Online-Batch Optimization jmlr.org. Checked 22 October 2026.
  4. [1602.04128] Coin Betting and Parameter-Free Online Learning arxiv.org. Checked 22 October 2026.
  5. Training Deep Networks without Learning Rates Through Coin Betting proceedings.neurips.cc. Checked 22 October 2026.
  6. Former CDS Faculty Fellows | Faculty of Computing & Data Sciences bu.edu. Checked 22 October 2026.
  7. Francesco Orabona wins CAREER award for work on machine learning | College of Engineering bu.edu. Checked 22 October 2026.
  8. CEMSE New Faculty: Francesco Orabona, Associate Professor of Computer Science | Computer, Electrical and Mathematical Sciences and Engineering cemse.kaust.edu.sa. Checked 22 October 2026.
  9. Francesco Orabona francesco.orabona.com. Checked 22 October 2026.
  10. [1912.13213] Online Learning: A Modern Introduction Using Convex Optimization arxiv.org. Checked 22 October 2026.