Emma Roscow
About Emma Roscow
Emma Roscow is a Senior Scientist specializing in Machine Learning at EcoVadis in Barcelona, Catalonia, Spain. She has a diverse background in research and engineering, with a focus on neuroscience, data science, and sustainability.
Current Role at EcoVadis
Emma Roscow is currently working as a Senior Scientist (Machine Learning) at EcoVadis, located in Barcelona, Catalonia, Spain. In this role, she focuses on leveraging her extensive experience in building scalable machine learning applications to support complex operations. Her work primarily aims to lower the barriers for companies to evaluate and improve their sustainability practices.
Previous Experience at Naska.AI
Emma Roscow served as a Machine Learning Engineer at Naska.AI from 2022 to 2023. During her one-year tenure, she was instrumental in designing and creating machine learning pipelines. These pipelines help automate labor-intensive processes, contributing to reducing waste, saving costs, and lowering carbon emissions for the clients.
Academic Background and Research
Emma Roscow holds a Doctor of Philosophy (PhD) in Neural Dynamics from the University of Bristol, where she studied from 2015 to 2019. Her research focused on fundamental questions about learning and memory, utilizing skills in data analysis, programming, and machine learning. She also earned a Bachelor of Science degree in Psychology from the University of Warwick, studying there from 2010 to 2013.
Research Interests and Contributions
Emma Roscow's research interests center on how the brain self-organizes to form and consolidate memories, particularly how neural activity during sleep aids this process. She has demonstrated how sleep-related neural activities selectively enhance memories of rewards and errors, which supports reinforcement learning. Emma is also interested in how innovations in neuroscience can inspire new AI algorithms and vice versa.
Machine Learning in Social and Environmental Impact
Emma Roscow is passionate about using data science to address global issues. She has contributed her machine learning and data science skills to organizations such as Omdena, DataKind UK, and the World Data League. Her work involved performing statistical analyses and developing machine learning models for initiatives aimed at promoting social and environmental good.