Corinna Cortes

Corinna Cortes
Alma mater Niels Bohr Institute
University of Rochester
Known for Support vector machines
Awards Paris Kanellakis Award (2008)
Scientific career
Institutions Google
Thesis Prediction of generalization ability in learning machines (1994)
Doctoral advisor Randal C. Nelson

Corinna Cortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York.[1] Cortes is a recipient of the Paris Kanellakis Theory and Practice Award for her work on theoretical foundations of support vector machines.[2]

Early life

Corinna Cortes was born in 1961 in Denmark.

Education and research

Cortes received her M.S. degree in physics from Copenhagen University in 1989. In the same year she joined AT&T Bell Labs as a researcher and remained there for about ten years. She received her Ph.D. in computer science from the University of Rochester in 1993. Cortes currently serves as the Head of Google Research, New York.[1] She is an Editorial Board member of the journal Machine Learning.[3]

Cortes' research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM).[4] Today, SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting.[2]

Personal life

Corinna has two children and is also a competitive runner.[5]

References

  1. 1 2 "Corinna Cortes". Google. Retrieved 8 November 2011.
  2. 1 2 "ACM Awards Recognize Innovators in Computer Science Who Solve Real World Problems". Association for Computing Machinery. Retrieved 8 November 2011.
  3. ↑ "Machine Learning - Editorial Board". Springer. Retrieved 8 November 2011.
  4. ↑ Cortes, Corinna; Vladimir Vapnik (1995). "Support-Vector Networks". Machine Learning. 20: 273–297.
  5. ↑ "Machine Learning NY Conference Biography".
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