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Masha Naslidnyk is a PhD student at the Foundational AI CDT at University College London, advised by Fran├žois-Xavier Briol and Carlo Ciliberto. Prior to starting her PhD, she was a Machine Learning Scientist at Amazon Research in Cambridge, where she worked on Alexa question answering (2015-2019), and then on Gaussian processes for supply chain emulation (2019-2021). Masha graduated from Part III in Pure Mathematics at the University of Cambridge in 2014.
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Research Summary
Masha's research is focussed on reliable machine learning theory and methodology for models that can be used in safety-critical applications. Specifically, she is looking into Gaussian process regression estimators that are resilient to model misspecification, meaning those that optimise robustness, according to the different notions of robustness. These notions highlight different ways in which the data generating process can be misspecified in real-world scenarios---this could be because of something going wrong during data collection, or because of wrong assumptions by the modeller. When the cost of error is high, using a non-robust estimator---which is the common default---could lead to even slightly contaminated data throwing off the entire model. Masha's studying alternative estimators that are more robust to contaminated data, or incorrectly chosen model class.
Teaching Summary
Masha has been a tutor with Cambridge Spark, an education technology company that enables organisations to achieve their business goals by educating their workforce in data science, since March 2022. She covers courses on a wide variety of topic in Machine Learning, Data Science and coding, for a diverse group of learners whose background varies from complete beginners to advanced students looking to expand their toolbox.
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