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Henry Wynn Prize

An annual prize recognising student excellence, expertise, collegiality and engagement beyond the classroom

Henry was a much-loved mentor to new generations. In his book Against Sacrifice (2021), he reinforced the need to better value life itself and the human qualities of empathy and imagination.

The Henry Wynn Prize is awarded annually to an undergraduate student (from any year of study) who has completed one of the DSI’s module options.

The prize recognises excellence and will be awarded to a high-performing student who has demonstrated an outstanding application of domain expertise to data science.  
 
The recipient must have not only excelled within the taught curriculum but also shown outstanding initiative in applying their knowledge beyond the classroom, contributing in a meaningful way to the wider data science community at LSE.  
 
The winner receives £250. 

Professor Henry Wynn (1945-2024)

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Professor Henry Wynn was a professor in the LSE Department of Statistics for over 20 years, Chair of the Centre for the Analysis of Time Series (CATS), a DSI Affiliate and former President of the Royal Statistical Society. Henry spent his long career leading research and teaching in a number of UK universities. His work was recognised with many honours, awards and fellowships. 

Henry was renowned for his intellect and collegiality and had an infectious sense of excitement about his subject. He was very active in fostering connection by engaging widely with colleagues across LSE and beyond. It is in this spirit that this award seeks to reward not only a high-performing student but to also recognise their collegiality and engagement with the DSI community. 

Find out more about Henry's life and work.

Selection Process

  • The winner will be selected at the end of each academic year by a committee consisting of all DSI course leaders and tutors.
  • All students who have completed one of the DSI undergraduate modules will be considered.
  • The prize winner will be announced in the final week of Spring Term.

Judging Criteria 

In order to be considered, a student must achieve a mark of 70 or above for a DSI module. As well as this, three key factors will be taken into account: 

  • Domain Expertise: a key aim of the DSI is to facilitate an interdisciplinary approach to the study of data science. Therefore, the recipient of this award must demonstrate a clear and effective application of domain expertise from their respective field of study, showcasing how their knowledge integrates with data science. 

  • Collegiality: this award honours a student who actively fosters a sense of community among their peers, consistently engaging in course-related communication channels and beyond. Through their actions they offer support, share knowledge, and contribute to the collaborative spirit, ensuring the success and well-being of others within the learning environment. 

  • External Engagement: an engagement with data science beyond the taught course.

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