Resume
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.
Intro
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
Education
ProVictus
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.
AI4EU
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.
Projects
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.
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.
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.
LioNets: Local Interpretation of Neural Networks through Penultimate Layer Decoding
Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
AIMLAI-XKDD at ECMLPKDD 2019
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.
Hatebusters: A Web Application For Actively Reporting Youtube Hate Speech
Antonios Anagnostou, Ioannis Mollas, Grigorios Tsoumakas
IJCAI-ECAI 2018
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.
Publications
Greek Language
Native Language
English Language
B2 Degree
Languages
Python, C, C++, Java, Sql, PHP,
Javascript/Typescript (nodeJS), HTML5, CSS3
Pro Level
Matlab, Objective-C, QML, Swift, Bash/Shell Script
Mid Level
Programming
Languages
Codeblocks, NetBeans, Visual Studio Code,
PyCharm, DreamWeaver, Sequel Pro
Pro Level
Matlab, XCode, Android Studio, Qt Creator,
SQL Server Management Studio
Mid Level
Platforms &
Programms
Final Cut Pro X, Motion, Compressor,
Adobe After Effects, Photoshop, Audacity,
Microsoft Office Suite, Keynote, Pages, Weka
Pro Level
Other
Programms
macOs
Mountain Lion to Catalina
Windows
XP to 10
Linux
Ubuntu, Debian (Kali), Fedora, Mint
Operating
Systems
Peukorama, Eleftheroupoli Kavalas
Barista, Waiter, Cashier
June 2015 - September 2018
Youtube, Google Inc.
Creator
February 2011 - now
Working
Experience
IJCAI 2021
Online
August 19-26, 2021
ECAI 2020
Online
August 29 - September 8, 2020
RW 2020
Online
June 24-26, 2020
ECML-PKDD 2019
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