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๐Ÿ‘‹ Hi there!


class Petru:
  def __init__(self):
    self.name = 'Petru'
    self.surname = 'Potrimba'
    self.education = 'Master Degree in Artificial Intelligence and Bachelor Degree in Compuer Science and Engineering'
    self.experience = ['Roboflow', 'AIvolution', 'Sis.ter', 'Imola Informatica', 'Unibo']
    self.roles = ['Machine Learning Engineer', 'Researcher', 'Software Engineer']
    self.interests = ['Machine Learning', 'Deep Learning', 'Competitive Programming', 'Teaching', 'Communication']
    self.languages = ['English', 'Italian', 'Romanian', 'Spanish']
    self.hobbies = ['Tv series','Swimming', 'Fitness']

๐Ÿ”จ Most interesting projects


  • Personal Blog : Actively write blog posts about my research projects mainly focused on Software Engineering and Machine Learning.

  • Send Time Optmization : Send Time Optimization (STO) is an add-on feature that allows marketers the ability to send emails at the most optimal send time to each recipient. We used feature engineering to create meaningful features and then we used deep learning to address this problem. The implemented solution improved the ratio of opened emails by โˆผ 32% and the click ratio by โˆผ 12%.

  • Generate synthetic faces : Auxiliary classifier generative adversarial network (AC-GAN) model that can generate synthetic conditioned faces with a specific set of attributes (example: generate a face of a woman, with blond hair that smiles). The FID metric reached a score of 19.42 for the generation of conditioned synthetic faces and a score of 11.07 for the generation of random synthetic faces.

  • Math assistant : Implemented and deployed a custom Optical Character Recognition (OCR) within an iOS Mobile application and a Telegram bot for the recognition of handwritten mathematical expressions. Implemented a Machine Learning model that understands whether the mathematical expressions are correct or not.

  • Image captioning with Transformers : Implemented the Image Captioning model using deep learning with Transformers. The model has been trained both on COCO and Flickr8k datasets. The performance reached results comparable to the state-of-the-art of 2020 on the BLEU and METEOR metrics.



๐ŸŽ“ Academic achievements

  • Top 0.4 % student among all Masters students of the Engineering department of Bologna University.
  • Winner of Start Hub competitive programming coding challenge (first position among 200+ competitors) link.
  • Admitted to ETH University for Computer Science Masterโ€™s Degree (ETH engineering department is 4ยฐ ranked among all the engineering departments all over the world).

๐Ÿ“ซ Connect with me


LinkedIn Gmail Blog GitHub

Petru Potrimba's Projects

alchemist icon alchemist

Alchemist: a chemical inspired simulator for engineering complex ecosystems

backpropagation-nn icon backpropagation-nn

Classic implementation of the backpropagation algorithm for training a Neural Network with only one hidden layer.

connecting-rods icon connecting-rods

Computer vision system aimed at visual inspection of motorcycle connecting rods.

db-ufc icon db-ufc

Application for the management of fighting matches for UFC (Ultimate Fighting Championship).

ealchemistplugin icon ealchemistplugin

EAlchemistPlugin is an Eclipse plugin created in order to facilitate the creation of new Alchemist projects.

face-generation-gan-keras icon face-generation-gan-keras

Auxiliary classifier generative adversarial network (AC-GAN) model that can generate synthetic faces conditioned with a specific set of attributes. The FID metric reached a score of 19.42 for the generation of conditioned faces and a score of 11.07 for the gen- eration of random faces.

fruit-inspection icon fruit-inspection

Computer vision techniques applied to apple images to detect apple's defects.

globaleaks icon globaleaks

GlobaLeaks is free, open source software enabling anyone to easily set up and maintain a secure whistleblowing platform.

icevision icon icevision

An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come

image-captioning-keras icon image-captioning-keras

Image captioning using Bahdanau and Luong attention applied on COCO and Flickr8k datasets. The performances have been measured using BLEU and METEOR scores.

iot-env icon iot-env

IoT distributed system that allows, from your Android application, to checkout relevant information of a specific area where the system is installed. You can basically get the real-time temperature of the area, change intensity of the light and get information about the clients who have entered.

machine-learning-ng icon machine-learning-ng

My personal solutions in Octave/MATLAB and Python at the assignments of Machine Learning course on Coursera held by Mr. Andrew Ng. -> https://www.coursera.org/learn/machine-learning

math-assistant icon math-assistant

Mobile (iOS) application for analysis and recognition of handwritten mathematical operations. The goal is to understand if the operation is correct or not. If it is not, it shows you the correct result.

math-assistant-telegrambot icon math-assistant-telegrambot

Telegram bot for analysis and recognition of handwritten mathematical operations. The goal is to understand if the operation is correct or not. If it isnโ€™t, shows you the correct result. Check the iOS application version: https://github.com/Pptr95/math-assistant

polygons-vs-bbox icon polygons-vs-bbox

Here I experimented whether is better to directly train an object detection model on bounding boxes or train it on polygons and then convert the prediction to bounding boxes.

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