Bob Williamson
Bob Williamson studies foundational questions in machine learning, including information, aggregation and selection bias. He earned an engineering degree in 1983 and a doctorate in 1990, worked at the Australian National University from 1990 to 2020 and moved to Tübingen in 2021. He also co-created NICTA. His research examines how to combine observations, how information relates to prediction risk, and how learning changes when data are selected or corrupted. As of October 2026, he is a professor in foundations of machine-learning systems and participates in Tübingen’s AI centre.
- Now at
- University of Tübingen
- Works
- 316
- Citations
- 19,096
- h-index
- 43
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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1983–1990
Engineering and doctoral training
Williamson earned an engineering degree at QIT in 1983 and a doctorate at the University of Queensland in 1990.
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1990–2020
Australian research career
Williamson worked at the Australian National University from 1990 to 2020 and co-created NICTA. The Australian Academy of Science elected him a Fellow in 2012.
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From 2021
Tübingen research
His Tübingen period began in 2021. He is a professor in foundations of machine-learning systems and works with students and postdoctoral researchers on basic learning questions.
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As of October 2026
Information and imperfect data
His projects examine aggregation, fairness, information measures and selection bias. One question is how a model should combine observations when the usual average loses relevant information. Another is how learning works when data corruption depends jointly on attributes and labels.
Sources and credits
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