Women in Machine Learning

March 24 has been declared Ada Lovelace day in honour of the woman who many people regard to be the first computer programmer. In 1843 she wrote a program to compute Bernoulli numbers for Charles Babbage’s yet-to-be-built Analytical Engine. The day is intended to be “an international day of blogging to draw attention to women excelling in technology.” To do my bit, I thought I would highlight some women in machine learning who I believe fit the description.

Arguably, the highest profile woman in machine learning is Corinna Cortes, head of research at Google, New York. She did some important early work on support vector machines1 and the application of machine learning to large-scale data sets and continues to publish on kernel methods and other topics.

Daphne Koller is a professor at the Stanford AI Lab and another high-profile woman in machine learning. Her and her group work in probabilistic graphical models and their application to various problems in biological networks.

Dana Angluin is a professor at Yale and did some important early work in the theory of inductive inference, including the problem of learning with queries and learning finite automata. She also helped start COLT, now widely considered the top conference on learning theory.

Ulrike von Luxborg is a senior research scientist at the Max Plank Institute for Biological Cybernetics and recently appointed member of the prestigious German Young Academy. She has made important contributions to the theoretical side of machine learning and has written excellent overviews of spectral methods for clustering and statistical learning theory.

Finally, Marina-Florina (Nina) Balcan is one to watch. She has recently finished her PhD with Avrim Blum and will soon start at Georgia Tech. During her PhD she published an impressive number of important results on semi-supervised learning, active learning, clustering and learning with similarity functions.

All of this bodes well if my daughter–and Ada Lovelace’s namesake–decides to one day take up machine learning. Judging by the quality of the people listed above and the steady increase of women in computer science, I’m sure she will have no problem finding excellent female role models.

  1. She and Vapnik hold the patent on soft-margin classifiers.

Mark Reid March 24, 2009 Canberra, Australia
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