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A key feature of Horizon is our Transitional Assistant Professor scheme – a mechanism recruiting the most promising research fellows onto a pathway that guarantees a transition into an academic lecturing position over several years.  Through a number of agile projects, Horizon ‘TAPS’ will further develop their impact skills and rapidly establish their careers as independent academics.

Take a look at Horizons current Transitional Assistant Professors and discover about their research interests under the ‘About’ menu

About

Georgiana’s research and impact activities are dedicated to resolving challenges that the world is facing today through data science.  Most recently, as part of a Knowledge Transfer Partnership between N/Lab and OLIO Exchange Ltd.,  she developed the first UK-wide machine-learning model of food-insecurity.  Georgiana is passionate about women’s careers in STEM and cultivating scientific curiosity and enjoyment from an early age.

 

 

 

 

Horia is a multidisciplinary trained researcher who started with integrating physiological sensors and non-invasive brain monitoring devices, such as functional Near Infrared Spectroscopy (fNIRS), in the field of Human Computer Interaction (HCI) and Human Factors (HF).  Horia’s primary focus is to further advance the understanding of brain and physiological data in naturalistic study settings by developing new ways of processing and analysing this data, to produce new scientific knowledge and establish guidelines of applying brain and physiological data in the field of HRI evaluation.

 

 

 

 

Neelima’s research addresses designing responses to the challenges of personal data leverage in future media experiences.  She has worked with the BBC on a number of projects which involve design, evaluation and dissemination focussed on the use of alternative privacy preserving techniques. 

 

 

 

 

Helena is an inter-disciplinary researcher working at the intersection of society and technology.  Her  research has focused on a number of topic areas including, social media, algorithmic bias, AI, and social robots and she has taught subjects including software requirements, ethics, responsible innovation, and computers in society.

Yordan studies problems in statistical machine learning focussing on interpretable ML and exploratory applications.   His focus is on Bayesian nonparametrics and hierarchical generative models and most of his applied work is in biomedical engineering – algorithms for digital health, precision medicine and proteomics.

Blog

Principled machine learning – new paper

Yordan led this co-authored paper, with David Saad – published in the IEEE Journal of Selected Topics in Quantum Electronics. The paper introduces the underlying concepts which give rise to some of the commonly used machine learning methods and points to their advantages, limitations and potential use in various areas of photonics.  

Horizon TAPs attending ACM CHI 2022

  The ACM CHI Conference on Human Factors in Computing Systems is the premier international conference of Human-Computer Interaction (HCI). CHI – pronounced ‘kai’ – annually brings together researchers and practitioners from all over the world and from diverse cultures, backgrounds, and positionalities, who have as an overarching goal to make the world a better …

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