About Me

  • Name:Georgios Tzannetos
  • Email:geo.tzannetos@gmail.com

Hi There!

My name is George and I am a data scientist. I always enjoyed automating processes, working with data, run simulations and doing science.

I am passionate about new technologies and methods, love to learn every day new things and apply them to my work. I enjoy doing research, and working on proof-of-concepts, as well as developing innovative softwares.

Starting as an engineer, with specialization in Robotics and Control Theory, I grew to become a data scientist, with strong background and experience in Deep Learning and Computer Vision.

However, I am always willing to expand my knowledge in Machine Learning and apply it in Computer Vision, as well as Natural Language Processing.

Currently, I am working as a Data Scientist in Connected Services at Fujitsu Germany.

My Timeline

  • Education

  • Master of Science, M.Sc

    Computational Science and Engineering

    Technical University of Munich, TUM
    Oct 2016 - Dec 2018

    Modules in Computer Science, Applied Mathematics and Scientific Computing
    Application Areas: Data Driven Computing, Big Data, Machine Learning, Deep Learning, Computational Physics
    GPA : 1.6, in German System

  • Diploma of Engineering

    Mechanical Engineering, equivalent to M.Sc

    National Technical University of Athens, NTUA
    Oct 2010 - Feb 2016

    GPA : 8.50
    Major: Mechanical Design, Control Systems & Robotics GPA: 9.05



  • Work Experience

  • Data Scientist

    Fujitsu, Sep 2019 - Current

    Part of the Delivery team of Fujitsu Connected Services, focusing on solutions in the automotive and manufacturing industry. Main tasks include the use, maintainance and further development of Fujitsu's AutoML system, especially for supporting and automating the visual inspection pipeline. Moreover, custom deep learning and machine learning solutions are developed for extending the AutoML platform. Working on the sandbox development of Fujitsu's Digital Manufacturing Platform. This is a solution towards smart factory, i.e connecting different levels and parts to improve the production performance. Among other tasks, I automated the collection, parsing and communication of factory data from a simulator to a database through MQTT protocol for performing analytics and visualization.

  • Data Scientist / Computer Vision, Intern

    AGT International R&D, Feb 2019 - Jul 2019

    Responsible for the virtual advertising project. Combination of classical computer vision methods, like image registration, developed in OpenCV, with deep learning methods, mainly Mask-RCNN, in Caffe2, to segment and produce highly accurate advertisement masks.

  • Machine Learning for Autonomous Driving

    AUDI Electronics Venture, May 2018 - Oct 2018

    Master Thesis, Automating the generation of a synthetic dataset, by superimposing objects on real images with the use of a computer graphics software, Blender. Image registration, depth completion and 3D road segmentation were implemented in Python. The created dataset was used to train a deep learning model to perform lane detection and performance was compared with real datasets, written in Keras.

  • Data Scientist, Working Student

    Trillr, Nov 2017 - Jan 2018

    Responsible for setting up and maintaining company’s database, deployed in Docker. Migrating existing MySQL database to MongoDB. Responsible for collecting data from various sources and APIs and storing them. Daily, weekly and long term data analysis, regarding different relevant KPIs was performed with Python, Pandas and Tableau to increase productivity and suggest improvements.

  • Undergraduate Research Assistant

    Control Systems Lab @ National Technical University of Athens, Jan 2015 - Feb 2016

    Diploma Thesis, Applications with Robotics software and hardware. For this purpose ROS (Robot Operating System) was used, which is supported by Linux. Theoretical analysis and real-time implementation of game theory to the robotic platforms, Asctec Firefly and Pioneer Mobile Robot. Applied sophisticated controls, such as model predictive control, written in C++.

My Interests

Machine Learning

Interested in all the types of ML, supervised, unsupervised and reinforcement. Enjoy working with structured and unstructured and apply different learning methods for a variety of tasks.

Deep Learning

Interested in applying end to end DL solutions to different problems. From data generation, exploration and cleaning to training, validation and deploying models. Really enthusiastic in researching new architectures and methods.

Computer Vision

Deeply interested in CV and making computers able to see and understand. Very keen on traditional CV approaches, as well as newer DL approaches, for solving problems like object detection, instance segmentation etc.

Robotics

Robotics have always fascinated me. Combining DL, CV, Control Theory and Automation, while using different sensors is a process I find exciting working on.

Computing & Simulation

Enjoy applying simulation of systems coming from different sciences, such as physics, molecular dynamics etc. Interesting in developing and applying numerical algorithms, along with scientific computing, and numerics to solve complex problems fast and efficient.

Artificial Intelligence

Interested in all the challenges and goals of AI, thus increasing the capabilities of the machines and working towards the problem of general AI, by combining multiple engineering and science fields.

Skills

Python

Linux

C/C++

Pytorch

Tensorflow

Numpy

Git

OpenCV

Pandas

Docker

Spark

Hadoop

Blender

SQL

Projects

Virtual advertisement Project

Live segmentation of advertisement during football game

Click to read more



Lane Detection using Synthetic Dataset

Augmented Reality Data Generation for Deep Learning Based Lane Detection

Click to read more



Robotics & Game Theory Project

A non cooperative game between an unmanned aerial vehicle and an unmanned ground vehicle.

Click to read more

Research Papers

Here there is a list of my published papers so far.

A competitive differential game between an unmanned aerial and a ground vehicle using model predictive control

G. Tzannetos, P. Marantos, and K. Kyriakopoulos, in IEEE, on the 24th Mediterranean Conference on Control and Automation, vol. 2016.

June 21-24th 2016

Contact me!

If you find my profile interested and you have anything you want to discuss or ask me, you are welcome to do it by sending me an e-mail or via the contact form below.

My E-Mail