Johnny Mollas



Greece, GR

Hi, everybody! I'm John. My friends and my godfather call me Johnny, and here are some things you need to know about me. Born and raised in Eleftheroupoli (Liberty City in English * * * GTA vibes * * *) of Kavala, Greece, on 7 May 1996, in a unique, but above all, extremely good family. I've been crazy about everything tech since my younger age. After graduating from my high school, I graduated from the Aristotle University of Thessaloniki with a Bachelor's degree and a Master's degree in Informatics. Right now, I am a PhD student at the same university in the field of Explainable Artificial Intelligence, and specifically Interpretable Machine Learning, and I am a member of the Intelligent Systems Lab. My love of technology is limitless, which is why I research this area continuously. My vision is to work in the future for Apple Inc.


Explainable Artificial Intelligence, PhD

Aristotle University Of Thessaloniki

Octomber 2018 - now

Artificial Intelligence, MSc

Aristotle University Of Thessaloniki - 9.46

Octomber 2018 - April 2020

School of Informatics, BSc

Aristotle University Of Thessaloniki - 8.2

Octomber 2014 - July 2018



The goal of this project is the transfer of knowledge in the field of predictive maintenance to Link Technologies SA in order to apply it for public transportation vehicles. The project involves the execution of a knowledge transfer plan that will study the current capabilities of the company, but also the actions it must follow, in order to succeed in solving the problem of predictive maintenance in public transport.

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The AI4EU project will efficiently build a comprehensive European AI-on-demand platform to lower barriers to innovation, to boost technology transfer and catalyse the growth of start-ups and SMEs in all sectors through Open calls and other actions. The platform will act as a broker, developer and one-stop shop providing and showcasing services, expertise, algorithms, software frameworks, development tools, components, modules, data, computing resources, prototyping functions and access to funding.

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ETHOS: a multi-label hate speech detection dataset

Ioannis Mollas, Zoe Chrysopoulou, Stamatis Karlos, Grigorios Tsoumakas

Complex and Intelligent Systems, Springer

In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined.

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VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance

Spyridon Paraschos, Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas

IJCAI 2021

This paper introduces a visualisation tool incorporating interpretations to display information derived from predictive maintenance models, trained on time-series data.

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LionForests: Local Interpretation of Random Forests

Ioannis Mollas, Nick Bassiliades, Ioannis Vlahavas, Grigorios Tsoumakas

NeHuAI-2020 Workshop of ECAI2020

In this paper, we provide a sequence of actions for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An explanation provided by these systems using our approach, which we call "LionForests", can be a simple, comprehensive rule.

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LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding

Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas


This paper explores a methodology on providing explanations for a neural network's decisions, in a local scope, through a process that actively takes into consideration the neural network's architecture on creating an instance's neighbourhood, that assures the adjacency among the generated neighbours and the instance.

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Hatebusters: A Web Application For Actively Reporting Youtube Hate Speech

Antonios Anagnostou, Ioannis Mollas, Grigorios Tsoumakas


Hatebusters is a web application for actively reporting YouTube hate speech, aiming to establish an online community of volunteer citizens. Hatebusters searches YouTube for videos with potentially hateful comments, scores their comments with a classifier trained on human-annotated data and presents users those comments with the highest probability of being hate speech. It also employs gamification elements, such as achievements and leaderboards, to drive user engagement.

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Greek Language

Native Language

English Language

B2 Degree


Python, C, C++, Java, Sql, PHP,
Javascript/Typescript (nodeJS), HTML5, CSS3

Pro Level

Matlab, Objective-C, QML, Swift, Bash/Shell Script

Mid Level


Codeblocks, NetBeans, Visual Studio Code,
PyCharm, DreamWeaver, Sequel Pro

Pro Level

Matlab, XCode, Android Studio, Qt Creator,
SQL Server Management Studio

Mid Level

Platforms &

Final Cut Pro X, Motion, Compressor,
Adobe After Effects, Photoshop, Audacity,
Microsoft Office Suite, Keynote, Pages, Weka

Pro Level



Mountain Lion to Catalina


XP to 10


Ubuntu, Debian (Kali), Fedora, Mint


Peukorama, Eleftheroupoli Kavalas

Barista, Waiter, Cashier

June 2015 - September 2018

Youtube, Google Inc.


February 2011 - now

My Channel


IJCAI 2021


August 19-26, 2021

ECAI 2020


August 29 - September 8, 2020

RW 2020


June 24-26, 2020


Würzburg Germany

September 16-20, 2019

ACAI-2019/HAISS-2019 Summer School

Chania, Crete, Greece

July 1-5, 2019

#Hash Code

Google Inc.

February 2017 and 2018

Grow Greek Tourism Seminar

Google Inc. and CSD Auth

Octomber 2016

9th Hellenic Conference On AI

Aristotle University Of Greece

May 2016

Digital Marketing

Xinis Education Festival

March 2015

3D Game Development in Unity

Mediterranean College

December 2014

The International Student Carbon Footprint Challenge

Stanford University

April 2013

Conferences &