IMPRESSUM

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• Amélie Anglade


• Belforter Str. 20, 10405 Berlin, Germany


• amelie.anglade+contact@gmail.com



• Registered in Berlin, Germany as a freelancer (Freiberuflich)


• VAT-ID-No.: DE 296 649 676



• Online Dispute Resolution website of the EU Commission
  In order for consumers and traders to resolve a dispute out-of-court, the European Commission developed the Online Dispute Resolution Website: www.ec.europa.eu/consumers/odr



• Legal disclaimer
  The contents of these pages were prepared with utmost care.
  Nonetheless, we cannot assume liability for the timeless accuracy and completeness of the information.
  
  Our website contains links to external websites. As the contents of these third-party websites are beyond our control, we cannot accept liability for them. Responsibility for the contents of the linked pages is always held by the provider or operator of the pages.



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As First MIR Engineer

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Research, prototyping and implementation of Search engines, Recommendation Systems and Machine Learning algorithms for Music Discovery. Also worked on signal processing and audio fingerprinting as well as Women in Tech outreach programmes.

• Employer: SoundCloud
• Date: July 2011 - August 2013
• Domain: Music Information Retrieval, IR and Machine Learning



• Watch: DiscoRank presentation at the Realtime Conf 2013

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As Senior Engineer

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In charge of building recommendation/personalisation features as well as the entire web backend development. Leading the developer team, onboarding, training and supervising programmers across the entire stack.

• Employer: frestyl
• Date: September 2013 - June 2014
• Domain: RecSys and Web Development



• Watch: frestyl panel at the Berlin Apple Store

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As Freelance Data Science Consultant

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Developed Big Data and graph-based algorithms for the MusicGraph.AI platform. Extended the set of acoustic features available on the MusicGraph. Experimented with Deep Learning approaches to various MIR problems.

• Client: Senzari
• Date: October 2015 - July 2015
• Services: Big Data, Audio Signal Processing and Deep Learning



• Try: MusicGraph.AI

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As Postgraduate Researcher

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PhD candidate, affiliated with the Centre for Digital Music and the OMRAS2 project (large UK project funded by the Engineering and Physic Sciences Research Council). Conducted research and development on knowledge representation of musical harmony and modelling of genre, composer and musical style using Machine Learning techniques and Logic Programming.

• Affiliation: Centre for Digital Music, Queen Mary University of London
• Date: April 2007 - June 2011
• Domain: Research, MIR, harmony, Automatic Genre Classification, Inductive Logic Programming



• Read: PhD thesis

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As Graduate Student Researcher

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Research and implementation of clustering techniques to create and maintain user communities that share specific music tastes in order to build P2P radio channels.

• Affiliation: Philips Research
• Date: August 2006 - January 2007
• Domain: RecSys, Complex Network Theory, Clustering Algorithms, P2P



• Read: Publication at RecSys 2007

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As Data Science Instructor

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Taught a 3-day hands-on introduction to Python, NumPy, SciPy, pandas and scikit-learn masterclass as well as a 1-day Graph and Network Theory and Algorithms masterclass during the 5th batch of this Data Science education programme.

• Client: Data Science Retreat
• Date: September - October 2015
• Service: Data Science Teaching

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