Background

Sotiris Tzamaras is a young and talented data scientist who has a passion for machine learning and artificial intelligence. He recently finished his master's degree in information technology, with a focus on data science and artificial intelligence, at the University of Grenoble in France. Sotiris started his journey in the field of computer engineering when he graduated with a B.Sc. in Computer Engineering from the Department of Informatics and Telecommunications at the University of Ioannina in Greece. During his studies, he developed an interest in artificial intelligence and cyber-physical systems, and he participated in several projects related to these topics, one of which was illustrated through his work during the Erasmus+ program, in which he was accepted. In this page, you will be able to explore his academic and professional journey, his skills and interests, and his projects and achievements.

He was born and raised in Irakleio, a suburb of Athens, Greece. He developed an interest in computer engineering from an early age, and decided to study informatics and telecommunications at the University of Ioannina. There, he learned the fundamentals of programming, algorithms, databases, networks, and software engineering. Later in his educational path, he also became fascinated by the fields of artificial intelligence and cyber-physical systems, and decided to pursue further education in these domains.

After graduating with a bachelor's degree in computer engineering, Sotiris applied for a master's program in information technology at the University of Grenoble, one of the leading universities in France for computer science and engineering. He was accepted and moved to Grenoble, a city surrounded by mountains and known for its scientific and technological innovation. There, he enrolled in courses on machine learning, information access and retrieval, information visualization, knowledge representation, multi-agent systems, and reinforcement learning. He also had the opportunity to work as a research intern at the LIG (Laboratoire d'Informatique de Grenoble), where he conducted research with accompanied experiments on federated reinforcement learning in database management systems.

Sotiris has acquired a wide range of skills and tools that enable him to tackle complex data science and artificial intelligence problems. He is proficient in Python, the most popular programming language for data science, and its libraries such as Numpy, Scikit-learn, Pandas, Matplotlib, Tensorflow, and Keras. He also has experience with other languages and tools such as Node.js, SQL, SQLite3, Latex, Git, Unix scripting, PowerBI, Jupyter notebooks, and Plotly.js. He has a solid understanding of machine learning techniques such as supervised learning, unsupervised learning, deep learning, and reinforcement learning, and he is also keen on data visualization and information retrieval, which is evident from his experience in tools such as Jupyter notebooks, PowerBi and Plotly.js.

He is a versatile and curious developer who has participated in various projects that reflect his abilities and passions. One of his most recent projects is FeReD (Federated Reinforcement Learning in the DBMS), a system that contrasts the performance of cross-device federation using Q-learning. It provides detailed instructions for overcoming the challenges of implementing in-DBMS SQLite for both horizontal and vertical data partitioning. He is also constantly exploring and learning new domains, which he demonstrates through innovative projects such as his latest one, where he created a job skills extractor. The project involves a scraper that collects the job postings, training using different learning methods such as word embeddings and deep learning, and finally it offers various visualizations along the way.

A forever student with a strong drive and ambition, Sotiris strives to create cutting-edge AI models that can address real-world challenges. He is always curious and willing to learn new things and take on new tasks. He aims to be a role model of how passion, dedication, and hard work can lead to success in this exciting and fast-growing field.

Publications