Research Assistant and PhD candidate designing, implementing and evaluating AI systems that support society. Strong research interest and years of experience in of machine learning and social sciences.
ETH is a research partner in the EU funded project "ASSET Consumerism", which is a part of the Horizons 2020 program. My research includes the design, implementation and testing of the recommender system and any machine learning application that supports the analysis and preprocessing of consumer and product data. In the scope of the project, I participated in the organization of the ASSET challenge in the Social Impact Data Hack. ETH proposed the ASSET Challenge about extracting meta-information from unstructured crowd-sourced product data that can be used to make more sustainable purchase decision.
A collaborative research with researchers from NTUA. The aim of the project is to design and apply a multi-agent reinforcement learning system on simulations and experiments derived from social sciences.
Specializing in the usage of learning and optimization algorithms for human behavior modeling and prediction. My daily duties include the design, development and deployment of machine learning models on the ETH cluster. Furthermore, I am responsible for the analysis of experimental results and the (co)authorship of scientific papers, project reports, teaching and proposals.
Incelligent specializes in cellular network optimization and analysis. I focused mainly on applying big data analysis and machine learning on data of several cellular network operators. I contributed in the development of several machine learning algorithms and pipelines used in the company and introduced the usage of deep neural networks such as LSTMs and Convolutional neural networks. Some of my daily tasks were:
Sleed is a Digital Marketing and E-business company. As a full-stack developer, I took part in many projects involving the development of web services, web sites and e-shops. During my time at Sleed I developed strong web development and database skills.
Daily tasks included assisting in the following:
Indicative passed coursers: Deep Learning, Advanced Topics in
Information Retrieval and Natural Language Processing, Data Science
in Techno-Socio-Economic Systems
Thesis Title (TBD): "Supporting Sustainable Development via Artificial Intelligence"
Indicative coursers: Information Retrieval, Machine Learning and Data Mining, Natural Language Processing
Thesis: "Part of speech tagging in Greek texts with word embeddings and deep neural networks"
I joined the Erasmus program during the last year of my B.Sc.
I attended several courses on business analytics and
computational intelligence. It was during these courses that I came
across Machine Learning and studied neural networks for the first time,
which later became one of my main research interests.
Indicative Courses: Data Mining and Text Mining, Computational Intelligence
The bachelors in Management Science offered me a broad view on several
fields such as Computer Science, Economics, Finance,
Quantitative Analysis, Management etc. During that time, my initial
research interest in optimization and machine learning was developed.
Furthermore, I acquired valuable knowledge and
several skills that supported me later in pursuing my research interests.
Indicative Courses: Mathematics, Information and Telecommunication Systems, Decision Making, Information Systems and Dabases, Advanced Topics on Software Engineering, Statistics, Quantitive Methods in Finance, Digital Content Management and Human Computer Interaction, Decision Making
Thesis (Elective): "Operations Research and Recommender Systems"