Hello, I'm Giannis, a Data Scientist working with weather data. My expertise lies in utilizing a combination of rule-based and machine learning methods to develop cutting-edge weather models. I enjoy analyzing weather data, uncovering hidden insights, and contributing to the progress of meteorology.
Starting from November 2022, I've been working as a Data Scientist at WeatherXM, a community powered web3 weather station network. My role involves analyzing weather data, evaluating and enhancing weather forecasts, implementing quality of data evaluation mechanisms, and creating impactful weather data visualizations.
I am currently contributing as an independent consultant for the EuroHyPerCon project (funded by the EuroHPC JU - a joint initiative between the EU, European countries and private partners), which focuses on the connectivity requirements and network design for High-Performance Computing (HPC) systems in Europe.
My duties include analyzing unstructured text data, exploring trends to derive meaningful insights and using visualization techniques to present summaries.
I am currently contributing as an independent contractor for the development of a Climate Risk Assessment Platform.
I have previously developed a weather website and volunteered as a Teaching Assistant for the "Introduction to Programming" and "Data Structures" courses at the Department of Informatics and Telecommunications of NKUA, supporting students in their learning process. In addition, I have volunteered as a translator for the CERN Indico project and meteonetwork.it, contributing to the dissemination of valuable scientific content.
In December 2023 I was the Lead Instructor for the "Data Science and Machine Learning for Weather and Climate" workshop at Google DevFest Athens 2023.
In September 2024, I started my master's studies at University of Amsterdam, pursuing the MSc in Artificial Intelligence.
In July 2023, I completed my studies at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens. My specializations included Data and Knowledge Management, as well as Software. You can find my published thesis, "Machine Learning Snowfall Retrieval Algorithms for Satellite Precipitation Estimates," here. In June 2024, my work was selected for the annual e-book showcasing top theses from the Department.
In 2019, I graduated from High School, where my academic achievements included qualifying for the National Mathematical Olympiad, organized by the Hellenic Mathematical Society. Additionally, I actively participated in the Panhellenic Competition in Informatics.
I. Dravilas, S. Dafis, G. Kyros, K. Lagouvardos, and M. Koubarakis, 'Towards a Machine Learning Snowfall Retrieval Algorithm for GPM-IMERG', Environmental Sciences Proceedings, vol. 26, no. 1, 2023.
Also presented at the 16th International Conference on Meteorology, Climatology, and Atmospheric Physics on September 2023.
S. Keppas, H. Balis, I. Dravilas, and J. Pagonis, 'Designing a Global Weather Station Network', EGU General Assembly 2024, Vienna, Austria, 14-19 Apr 2024, EGU24-9241.
Presented at EGU General Assembly 2024.
During my thesis, I explored the application of advanced Machine Learning techniques, with a particular focus on Deep Learning algorithms, to estimate the phase of satellite precipitation estimates. Both numerical and in-situ observational weather data were also utilized in this study.
Over the past few years, I've accomplished a wide array of computer science projects, ranging from personal endeavors to university assignments.
Refactored and optimized all the quality of data mechanisms used at WeatherXM, resulting in a 50% increase in processing speed.
Developed a high-resolution, post-processing, rule-based snow forecasting model for Greece in Python using downscaling tehcniques.
Conducted Sentiment Classification of users' text reviews using Deep Learning (Feed-forward, Recurrent neural networks, and a pre-trained BERT model in PyTorch) and developed a Question Answering engine.
Developed a waterspout forecasting model using SWI Index and numerical weather data in Python.
Created a music genre classifier for short music clips using PyTorch-based Feed-forward and Convolutional Neural Networks.
Visualized weather data in Python, using GRIB and netCDF formats, as well as Matplotlib and Cartopy libraries.
Developed a facial recognition system using Yale B database face images, employing the Eigenfaces technique, including PCA and a nearest neighbour classifier. Additionally created a classification system for images containing handwritten digits, using SVM.
Conducted Sentiment Analysis on Covid-19 tweets obtained from Twitter, using a range of Data Mining techniques and Machine Learning algorithms.
Analyzed data of New York City taxi trips and created a Random Forest Regressor for predicting the duration of taxi trips.
Implemented polygonization algorithms for point sets in a plane, and optimized their areas using operational research concepts in C++ with the CGAL library.
Developed software and protocols in C/C++ for Linux, using processes, threads, pipes, signals and the internet protocol.
Solved the constraint satisfaction Radio Link Frequency Assignment Problem using Artificial Intelligence.
Developed parallel solutions for Monte Carlo simulations using Pthreads, OpenMP and MPI.