CV
General Information
| Full Name | Mélanie Roschewitz |
| m.roschewitz21@imperial.ac.uk | |
| Languages | English, French, German |
Research Experience
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Oct 2021 - Now PhD in Computing
Imperial College London, Departement of Computing, UK - Under the supervision of Prof. Dr. Ben Glocker.
- Fully funded by an Imperial College President's Scholarship.
- My research focuses on reliability, robustness and safety of AI systems for medical imaging.
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Apr.22 - Oct.22 ML Research Intern
Kheiron Medical Technologies, London, UK - Investigating domain generalisation and model robustness in breast screening applications.
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Apr.20 - Sep.21 Applied Researcher (I & II)
Microsoft Research Cambridge, Health Intelligence, UK - Part of Project InnerEye
- Developing open-source machine learning models for medical image analysis and personalised treatment.
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Sep.19 - Mar.20 AI Resident
Microsoft Research Cambridge, All Data AI, UK - Part of the Enterprise Knowledge team: constructing a knowledge base from business data using unsupervised ML and probabilistic programming.
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Feb.18-Aug.18 Student Research Assistant
ETH Zürich, Chair of Risk and Insurance Economics, CH -
Jun.17 - Aug.17 Statistics Intern
Statistics Canada, Ottawa, CA
Education
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2019 M.Sc. Data Science (distinction)
ETH Zurich, CH - Original title: Master of Science ETH in Data Science, mit Auszeichnung. GPA: 5.8/6.
- Courses include: Machine Learning, Big Data, Computational Statistics, Deep Learning, Computational Biology, Ultrasound Imaging, Experimental Design.
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2017 Postgraduate diploma in Statistics (distinction)
Université de Strasbourg, FR - Original degree title: Maîtrise en Statistique, mention très bien. Rank 1/14.
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2016 B.Sc. Mathematics (distinction)
Université de Strasbourg, FR - Original degree title: Licence de Sciences, Technologies, Santé, Mention Mathématiques, mention très bien. Rank 1/85.
Teaching activities
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2022 - Present Graduate Teaching Assistant
Imperial College London, UK - Machine Learning For Medical Imaging: guest lecturer, coursework design and marking (Spring 2024)
- Deep Learning & Machine Learning For Imaging: supervision of a weekly exercise session and marking of coursework (Spring 2023)
- Probability & Statistics: supervision of a weekly exercise session and marking of weekly assignments (Spring 2022)
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Aug 2024 - Present Head of Teaching Content
GirlsWhoML, UK - In charge of teaching content for the 'GirlsWhoML workshops' a series of lectures taught across various universities in the UK, designed to introduce core principles of Machine Learning to women studying subjects not related to computer science, to enable them to leverage these tools in their own field. In charge of designing new courses (lectures + practical coding exercises) as well as updating existing ones.
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Fall 2018 Teaching Assistant
ETH Zürich, CH - Big Data: supervision of a weekly 2-hour exercise session. Design of exercise sheets and solutions.
Outreach & community activities
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Outreach
- Head of Content (since Aug 24) and lecturer (Fall 22 and 23) for the "GirlsWhoML workshops", a series of lectures designed to introduce core principles of Machine Learning to women studying subjects not related to computer science, to enable them to leverage these tools in their own field.
- Research talk at Imperial College’s "girls-only computing taster day", tailored for high school students aged 14-18; sharing my path to becoming a computer scientist as well as examples of AI applications in the healthcare domain.
- Assistant for the "Hello World Hack" (June 23 and 24), day of introduction to coding for girls in primary schools (aged 6-9), helping them to solve coding and machine learning games.
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Reviewing activities
- Reviewed for various ML for health venues: Nature Medicine, NeurIPS, MICCAI workshops, NeurIPS workshops...
Scholarships
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2021 - 2025 Imperial College President's PhD Scholarship
- Awarded to candidates with excellent academic performance and promising research potential.
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2024 Margaret and Martin Gotheridge Award (British Federation of Women Graduate)
- BFWG academic awards are awarded to female doctoral students for “outstanding academic achievements and ability to communicate complex research to a non-specialist audience.