• GPA: 8.69 / 10.00
• Track: Computer Science
• Specialization: Data and Knowledge Management
• Thesis: "GreekQA: A Crowdsourcing Platform and its Use for Creating a Greek Question Answering Dataset"
  
• Advisor: Prof. Manolis Koubarakis

• Conducting various experiments to evaluate the performance of Greek and multilingual language models by fine-tuning on the GreekQA dataset and transfer learning with a zero-shot cross-lingual approach on GreekQA, SQuAD, and FQuAD datasets.
• Supervising the collection of additional question and answer pairs for the GreekQA dataset by students.

• Computed the Greek Wikipedia’s internal PageRanks to select high-quality articles in Greek and curated passages.
• Implemented a web crowdsourcing annotation platform for the collection of question and answer pairs on curated passages.
• Analyzed the questions, answers, and the reasoning required to answer the questions based on the corresponding passages.
• Evaluated the human performance on proposed GreekQA dataset for future experimental evaluation of language models.

• Improved and extended custom named entity recognition and intent classification of the EU-funded Question Answering System in Maritime (QAMar) project for the purposes and functioning of METIS virtual assistant chatbot.
• Designed deep learning, tree-based ensemble, and statistical models for maritime tabular data regression and classification.
• Participated in the development of a vessel speed optimization application targeting fuel consumption minimization based on time-series segmentation, particle swarm optimization, and vessel performance prediction models using maritime weather forecasting.
• Βuilt data pipelines for the maintenance of tables with a change data capture design through jobs orchestration.
• Automated the monitoring, retraining and deployment process of ML models building experimental end-to-end MLflow pipelines.
• Implemented a vessel performance analysis and route cost prediction application for the estimated routes of vessels.
• Designed and implemented interactive data visualization dashboards of maritime applications for end users.

• Proposed and implemented an unsupervised univariate sensor data anomaly detection system combining statistical and machine learning techniques. Incorporated this system into a data pipeline that handles over 10 billion raw measurements per month.
• Built a data reception quality evaluation system that detects and assesses the lost measurements from sensor devices.
• Evaluated the categorization of sensor devices by applying data profiling and multiclass classification algorithms.

K08: Data Structures & Algorithms, Spring 2023

YS02: Artificial Intelligence, Fall 2022

K08: Data Structures & Algorithms, Spring 2020