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Director/Head of Data Science
I see myself as an innovation leader, passionate about leveraging the power of machine learning and data science to deliver products and solutions that drive business value. With more than 20 years of experience and a PhD in Machine Learning, I have leveraged my research background and technical expertise in shaping and implementing innovative industry IT solutions. In my current role, I am actively involved in defining user-centric products, proposing pragmatic and feasible solutions. As a results-driven leader with experience in multinational projects, my key strengths include: - Innovative product definition - Problem-solving and critical thinking - Machine Learning applied to Text and Image content - Team coaching and leadership
Ethnikon kai Kapodistriakon Panepistimion Athinon
Doctor of Philosophy (Ph.D.), Computer Science / Machine Learning
January 1, 2000 – January 1, 2006
Pierre and Marie Curie University
Master's Degree, Computer Science / Machine Learning
January 1, 1996 – January 1, 1997
National Technical University of Athens
Bachelor's Degree, Computer Engineering
January 1, 1991 – January 1, 1996
Elsevier
Director/Head of Data Science
March 1, 2023 – Present
Elsevier
Principal Machine Learning Scientist
June 1, 2019 – March 1, 2023
Elsevier
Senior Machine Learning Expert
September 1, 2016 – June 1, 2019
IRI
Manager, ACE - Innovation Team
February 1, 2014 – July 1, 2016
Athens, Greece
University of Peloponnese (UOP)
Lecturer
January 1, 2007 – January 1, 2010
Tripoli, Greece
Educational Institute of Certified Public Accountants (IESOEL)
Lecturer
January 1, 2006 – January 1, 2007
Athens, Greece
NCSR "DEMOKRITOS"
Researcher
January 1, 2005 – January 1, 2014
Athens, Greece
National and Kapodistrian University of Athens
Assistant lecturer
January 1, 2003 – January 1, 2004
Athens, Greece
University Paris 6Par
Assistant Lecturer
January 1, 1997 – January 1, 1999
Paris, France
AMINESS - Analysis of Marine Information for Environmentally Safe Shipping
January 1, 2014 – Present
The goal of the AMINESS project is to contribute in the safety, management and monitoring of the sea environment and the Aegean Sea in particular. Reducing the possibility of ship accidents in the Aegean Sea is important to all economic, environmental, and cultural sectors of Greece. Oil spill cleanups can cost over 1 billion Euros, whereas accidents involving water soluble cargos would result in irrevocable changes to the Aegean ecosystem. Despite an increase in traffic, there are no national-level monitoring policies and ships formulate routes according to their best judgment. However, to reduce their own financial risk, shipping companies would directly benefit from a system that can reduce the possibility of an accident involving their own ship.
USEFIL - Unobstrusive Smart Enviroments For Independent Living
January 1, 2013 – Present
The USEFIL project aims to address the gap between technological research advances and the practical needs of elderly people by developing advanced but affordable in-home unobtrusive monitoring and web communication solutions. USEFIL intends to use low cost "off-the-shelf" technology to develop immediately applicable services that will assist the elderly in maintaining their independence and daily activities. Installation of the USEFIL system will not require retrofitting in a person’s residence and will be almost invisible once installed. Because the system will be "software driven," based on open source platforms, applications can be easily added or subtracted with no real limit to the overall number of services offered. USEFIL intends to provide guidelines for the community of technology developers to optimize future generation of applications for an ageing population. Technology implementation will be based on user acceptance and an understanding of user interactions that will truly address user needs. In USEFIL, I worked for the development of the system's data fusion component, using complex event processing methodologies, based on probabilistic logic programming
BioASQ - A challenge on large-scale biomedical semantic indexing and question answering
January 1, 2012 – Present
BioASQ pushes for a solution to the information access problem of biomedical experts by setting up a challenge on biomedical semantic indexing and question answering (QA). Biomedical knowledge is dispersed in hundreds of heterogeneous knowledge sources and databases; many of them are connected on the Linked Open Data cloud. Biomedical experts, on the other hand, are in constant need of highly specialized information, which they cannot easily obtain. To address their needs, an information system needs to "understand'' the data and answer efficiently the experts' questions. Often, however, experts need responses that cannot be answered by a single information source. To integrate information from disparate sources, semantic indexing of the vast quantities of information is needed to bridge the experts' needs with the available data sources. Semantic indexing is currently achieved by manual annotation, and does not scale up. Automating this process requires large-scale classification of data into hierarchically organized concepts. QA methods capable of "interpreting'' questions in terms of the same concepts are also needed. BioASQ pushes towards improved biomedical semantic indexing and QA via ambitious, yet realistic challenge tasks. The challenge runs in two stages, designed to (a) adapt traditional semantic indexing and QA methods to the needs of biomedical experts, and (b) collect feedback and improve the experimental setting itself. A large computational infrastructure, already available to the consortium, is used to evaluate competing systems. Evaluation measures have been established before the challenge. Biomedical experts participate in the consortium, both as partners and through a supporting network of third parties.
PRINCE2® Practitioner Certificate in Project Management
PeopleCert
June 24, 2026 – Present
PRINCE2® Foundation Certificate in Project Management
PeopleCert
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background includes significant academic research, lecturing, and industry roles in data science and machine learning. The projects listed (AMINESS, BioASQ, USEFIL) demonstrate an interest in applying data analysis and machine learning to diverse, impactful domains like environmental safety, biomedical information, and independent living. This breadth of application and a history of contributing to both academic and commercial innovation suggests a strong cultural fit for roles that value continuous learning, problem-solving, and impactful data-driven solutions. The progression from researcher to Director/Head of Data Science at Elsevier shows ambition and leadership growth.
Soft Skills & Operational Fit
The candidate's extensive experience in leading data science teams, managing innovation, and lecturing suggests strong leadership, mentorship, and communication skills. Their involvement in coordinating research projects and improving operational efficiency (e.g., 12x speed up in run times at IRI) indicates a results-oriented and problem-solving mindset. The project descriptions, while lacking specific technologies, highlight an ability to understand and address complex, real-world problems, which aligns with a senior data analyst role requiring strategic thinking and problem decomposition.