About me
I started my scientific journey studying physics at the Aristotle University of Thessaloniki. Shortly after completing my M.Sc. degree in computational physics, I moved to the picturesque alpine town of Annecy, France, where I did my PhD in experimental particle physics at CERN. If you’d like to know more, you can find my PhD thesis here.
After finishing my PhD, I made the transition into data science, where I continued applying scientific reasoning to real-world problems. The topics were different, but the core mindset remained the same: Combine theory with data to make sense of complex systems and uncover hidden patterns.
Currently, I’m a Fellow Postdoctoral Researcher at Lawrence Berkeley National Laboratory, working with A-Lab, an autonomous laboratory for materials discovery. My work focuses on combining chemistry, data science, and automation to make sense of complex synthesis and characterization data — blending statistical reasoning, machine learning, and interactive tools. It’s an exciting space where science meets systems thinking.
I revel in the problem-solving aspect, but I don’t want to stop there: Being able to communicate my findings and their theoretical underpinnings to audiences of wildly different backgrounds has always been dear to my heart.
When I’m not sifting through bits and bytes, you’ll often find me running or cycling in the mountains. Apart from a slight obsession with challenges of all kinds, I also just love being outdoors! And then there’s painting, which is my preferred form of meditation — have a look here if you’re curious.
“To learn is a natural pleasure, not confined to philosophers, but common to all men.”
Aristotle
. . .
2024 - current |
Postdoctoral Fellow |
Lawrence Berkeley National Lab |
2021 - 2023 |
Lead Data Scientist |
Tempr. |
2020 - 2021 |
Data Scientist |
Dreamin |
2016 - 2019 |
PhD Particle Physics |
CERN |
2014 - 2016 |
MSc Computational Physics |
Aristotle Un. of Thessaloniki |
2008 - 2014 |
BSc Physics |
Aristotle Un. of Thessaloniki |
- Computer Vision, Kaggle.
- Managing Machine Learning Projects with Google Cloud, Google Cloud.
- TensorFlow2 and Keras Deep Learning Bootcamp, Udemy.
- TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, Coursera.
- Machine Learning with Python, Coursera.
- Data Visualisation with Python, Coursera.
- Deep Learning Prerequisites: The Numpy Stack in Python, Udemy.
- Beyond Jupyter Notebooks, Udemy.
Projects
Computer Vision Anomaly Detection
Anomaly detection in MNIST digits using unsupervised learning and evaluates the model's performance with ROC AUC.
Parkinson UPDR-scores Forecasting
Predictions of MDS-UPDR scores, which measure progression in patients with Parkinson's disease.
Time Series Forecasting
Time series forecasting using the ARIMA model.
EDA Cancer Deaths
Exploratory data analysis (EDA) on global death data from 1990 to 2019.
Multi-Armed Bandit
A Multiarmed Bandit algorithm using the UCB algorithm.
Interactive Dashboard for Materials Research
Interactive dashboard to visualize and interpret materials synthesis and characterization data. Built with Python and HoloViz Panel.
Data Imputation
Evaluating different imputation methods for time series datasets to handle missing values.
Google Foobar Challenge 2022
Complete solutions of all levels in Google's secret programming challenge.
Portfolio Projects
A summary of projects I worked on during my PhD and in my current role.
Presentations
Knowledge transfer is one of the key aspects of science. I was always driven by outreach activities; I like to simplify and visualise any scientific concept I am working on. A selected sample of my work is given below.
Lectures:
Talk at MRS Spring Meeting 2025
Data Infrastructure for Automated Labs and Framework for Interpretation of Characterization Results.
Artificial Intelligence - Basics
Introduction to artificial intelligence fundamentals.
Neural Networks Introduction
Visual guide to understanding neural network basics.
Data Visualisation Techniques
The basics and more advanced techniques in data visualisation.
From Academia to Data Science
Tips and mindset shifts when transitioning into the data science field.
PhD Thesis Defense
My final PhD presentation summarizing key research contributions.
Articles:
No More Scrum for Data Scientists
Reflections on how agile methods like Scrum may not fit scientific workflows.
The Ambiguity of Charisma
Exploring how we define and misinterpret charisma in leadership and communication.
Outreach
A collection of my outreach lectures and presentations — from scientific conferences to visual explainers and transitions into data science.
MRS Spring Meeting 2025
Data Infrastructure for Automated Labs and Characterization Frameworks.
AI Fundamentals
Introduction to artificial intelligence for general audiences.
Neural Networks 101
Visual introduction to neural networks and deep learning.
Data Visualisation Techniques
Basics and advanced techniques for visualizing scientific data.
From Academia to Data Science
Mindset and tools when transitioning into the data science field.
PhD Thesis Defense
Summary of research contributions during my PhD at CERN.
Paintings
Art Gallery
Painting is my preferred form of meditation — here are a few selected works. Click to view full size.
Visualizations
EDA Tips in Restaurant Dashboard
Healthcare Data Analysis Dashboard