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Hi there, I'm Georgios Balaouras - aka mpalaourg mpalaourg

  • šŸ”­ Previously working on a DL Architectures, for Automatic Video Summarization using Attention Mechanisms.
  • šŸ„… 2024 Goal: Explore LLMs and their capabilities. Build more, solve more business problems, bring more value.
  • āš” Fun fact #1: My favorite debug method is a print("got this far") statement.
  • āš” Fun fact #2: Iā€™m a procaffeinator.
  • šŸ  Thessaloniki, Central Macedonia, Greece.
  • šŸ¢ @EY/Data & Analytics, Thessaloniki, Greece.

Connect with me:

Programming languages, Frameworks and Tools:

  • Currently Mastering

ā€‚ā€‚ā€‚ python pytorch scikit_learn git linux bash

  • Long time no see

ā€‚ā€‚ā€‚ c cplusplus mysql mongodb java android r matlab

  • To be mastered ...

ā€‚ā€‚ā€‚ docker kubernetes

  • My free time fun

ā€‚ā€‚ā€‚ raspberrypi arduino flask postman

šŸ˜ƒ Still here? (click for a joke)

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George Balaouras's Projects

ca-sum icon ca-sum

A PyTorch Implementation of CA-SUM from "Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames", Proc. ACM ICMR 2022

f1_dataanalysis icon f1_dataanalysis

Data analysis of Formula 1 seasons, uncovering trends, statistics, and key insights from the pinnacle of motorsports.

multimedia icon multimedia

Assignment for "Multimedia Systems and Virtual Reality" course of THMMY in AUTh.

pgl-sum icon pgl-sum

A PyTorch Implementation of PGL-SUM from "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. IEEE ISM 2021

rtes_finaltask icon rtes_finaltask

Final Assignment for "Real Time Embedded Systems" course of THMMY in AUTh.

rtes_task1 icon rtes_task1

First Assignment for "Real Time Embedded Systems" course of THMMY in AUTh.

systemmodeling icon systemmodeling

Assignments for "System Simulation and Modeling" course of THMMY in AUTh.

thesis icon thesis

Data Collection and Analysis of Energy Consumption of Mobile Phones using Machine Learning Techniques

xai-sum icon xai-sum

A PyTorch implementation of our attention-based method for explainable video summarization, presented in "Explaining video summarization based on the focus of attention" (IEEE ISM 2022)

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