Principal Machine Learning Scientist at Expedia Group
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I am currently working as Principal Machine Learning Scientist at Expedia Group focusing on advertisement. In the past I have been leading the workstream of Embeddings where I built multiple foundational models (like hotel2vec) that power multiple downstream tasks across Expedia Group.
Aristotle University of Thessaloniki (AUTH)
PhD, Machine Learning
January 1, 2004 – January 1, 2009
Aristotle University of Thessaloniki (AUTH)
MSc, Information Sciences
January 1, 2004 – January 1, 2006
University of Ioannina
BSc, Informatics
January 1, 1999 – January 1, 2004
Expedia Group
Principal Machine Learning Scientist
March 1, 2021 – Present
Expedia Group
Lead Machine Learning Scientist
April 1, 2019 – March 1, 2021
Expedia Group
Senior Machine Learning Scientist
February 1, 2017 – March 1, 2019
VISEO
Researcher in Data Science
October 1, 2014 – February 1, 2017
Greater Grenoble Metropolitan Area
University of Joseph Fourier, Grenoble
Post-doc researcher
October 1, 2011 – September 1, 2014
Greater Grenoble Metropolitan Area
University of Western Macedonia
Adjunct Lecturer at the Department of Informatics Engineering and Telecommunications
October 1, 2010 – February 1, 2011
TEI of Western Macedonia
Scientific Associate
October 1, 2010 – February 1, 2011
Aristotle University of Thessaloniki
Research Associate, Machine Learning and Knowledge Discovery Group, Department of Informatics
September 1, 2010 – September 1, 2011
IEK
Instructor
January 1, 2008 – January 1, 2009
Aristotle University of Thessaloniki
Software Developer
September 1, 2007 – October 1, 2008
Smart Media Management for Social Customer Attention (SOMA)
November 1, 2015 – Present
The European project SOMA (Smart Media Management for Social Customer Attention) is a project to help companies manage social networks in conjunction with databases. First SOMA goal is to efficiently manage customer’s interactions by analysing comments in social networks in order to detect questions, complains and concerns and to automatically provide fast, efficient and accurate answers. Since customers are not all of the same importance. SOMA second goal is to detect those who are more influent for a certain product/brand. Indeed, because influent customers have the power to spread a product around the world they will be given special attention. Influence will be detected by using a combination of linguistic and non-linguistic descriptors. SOMA third goal is to provide a link between existing companies CRMs and SOMA customer’s database in order to centralize all the customer’s interactions and information. The main project result will be a prototype that will facilitate a more comprehensive use of the social media corporate channels between customers and companies. As a result, companies will improve the attention to customers in a smart way, thanks to an innovative tool which aims at improving their satisfaction, but also keeping track of any information exchange from both sides. Moreover, based on what can be inferred from the social networks (e.g. conversations, interests, social engagements and emotions) the system will be able to identify influent users for a specific marketing campaign. It will be implemented in English, French and Spanish languages.
Viseo Smart Data
September 1, 2015 – Present
This project aims at agregate the capitalisable parts of the production done during the different collaborative projects and experimentation. The VSD platform is a production platform able to operate in a short time new needs coming from customer. Its main fonctional purpose is to transform non structured data into ready-to-use knowledge .
BioASQ
October 1, 2012 – Present
A challenge on large-scale semantic indexing and question answering
Cultural Fit Analysis
The candidate's background includes diverse experiences across academia (lecturer, researcher) and industry (ML Scientist roles at Expedia, Researcher at VISEO). The projects listed, such as SOMA and Viseo Smart Data, involve collaborative, multi-disciplinary efforts, suggesting an ability to adapt to different work environments. The progression within Expedia Group indicates a capacity for growth and long-term commitment. The target role of 'Backend Engineer' is a slight pivot from a pure 'Machine Learning Scientist' role, which might require an assessment of their core backend engineering skills beyond ML application.
Soft Skills & Operational Fit
The candidate's extensive experience in research and academic roles, coupled with progression in industry, suggests strong analytical and problem-solving skills. However, without psychometric test results or interview data, it is difficult to assess specific soft skills like teamwork, communication style, or stress handling. The project descriptions indicate an ability to work on complex, multi-faceted problems.