Georgios Balaouras

Data Scientist

Aristotle University of Thessaloniki

About me

Hi, I’m Georgios Balaouras, and my unsatisfied curiosity fuels my deep interest in Data Science, Machine Learning, and Deep Learning. Starting this September, I’ve taken on the role of a Data Scientist, becoming a member of the CESA Data & Analytics Center of Excellence at EY.

I graduated from Aristotle University of Thessaloniki with a MEng degree in Electrical and Computer Engineering. My diploma thesis, titled Data Collection and Analysis of Energy Consumption of Mobile Phones using Machine Learning Techniques, was held in association with Intelligent Systems & Software Engineering Labgroup (ISSEL) under the supervision of Professor Andreas L. Symeonidis and PostDoc Researcher Manos Tsardoulias.

Furthermore, I was part of IDT LAB, directed by Vasileios Mezaris, a research team of CERTH-ITI, exploring automatic Video Summarization via Deep Learning techniques in collaboration with Evlampios Apostolidis. Our research work resulted in five publications within the Video Summarization domain, using self-attention mechanisms to produce complete and concise synopsis of the video content. In addition to my academic pursuits, I also successfully fulfilled my mandatory military service, which spanned from September 2022 to June 2023, providing me with valuable life experiences and insights.

Download my resumé.

u/Grandure once said …

I'm not scared of a computer passing the Turing test... I'm terrified of one that intentionally fails it.

Interests

Generative Adversarial Networks
(Self-)Attention Mechanisms
(Un)supervised learning
Embedded Systems

Education

MEng in Electrical and Computer Engineering, 2020
Aristotle University of Thessaloniki (AUTh)

Experience

Data Scientist

EY

Sep 2023 – Present Thessaloniki, Central Macedonia, Greece

Deep Learning Engineer & Researcher Assistant

Intelligent Digital Transformation Laboratory (CERTH - ITI)

Jan 2021 – Aug 2022 Thessaloniki, Central Macedonia, Greece

Our main goal was to generate a concise synopsis that conveys the important parts of a full-length video automatically, either in a supervised or an unsupervised way. More precisely, using an Encoder-Decoder architecture we were trying to model the temporal dependency among video frames and learn how to estimate frames’ importance. At first, we were mostly using Recurrent Architectures, like vanilla RNNs and LSTMs, and Generative Adversarial Networks (GANs), where an LSTM-based VAE (Generator) tries to confuse the Discriminator about the originality of the produced summaries. Despite the success of these architectures, problems like limited parallelization and challenging training, led us in exploring Attention Mechanisms and Transformers models for our ongoing works. Finally, all our developments are open-source and based on PyTorch.