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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
PyTorch Project Template Permalink
Published:
Deep Learning projects sometimes look messy and don’t follow a certain structure. People get into the problem and compete over getting good results. Usually, given the nature of the task, we don’t get the time to think much about the project structure or the code modularity. After working with different deep learning projects and facing problems with files organization and code repetition, we came up with a simple project structure built with PyTorch.
portfolio
Policy Gradient vs. Action-value based methods Permalink
An empirical analysis on policy-gradient methods vs. action-value based methods for control
Reinforcemenet learning with Robotics Permalink
A real-time learning of an off-policy RL control algorithm on a real robot(Create2).
publications
CIFAR-10: KNN-based Ensemble of Classifiers
Published in 2016 International Conference on Computational Science and Computational Intelligence, 2016
This paper studies the performance of different classifiers on the CIFAR-10 dataset, and builds an ensemble of classifiers to reach a better performance.
Download here
VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view
Published in ICML 2019 - Workshop on AI for Autonomous Driving, 2019
This paper introduces a real-time approach for 3D LiDAR semantic segmentation
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Multi-Sensor Fusion for Radar Cocoon object detection
Published in Germany, 2019
DE Patent DE102019127283.1 A
Safe Evaluation For Offline Learning: Are We Ready To Deploy?
Published in NeurIPS 2021 Workshop on Deployable Decision Making in Embodied Systems [Spotlight], 2021
This paper proposes using high-confidence off-policy evaluation for Offline Reinforcement Learning
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A Framework for Safe Evaluation of Offline Learning
Published in MSc Thesis, University of Alberta, Canada, 2022
My MSc thesis presented a framework for performing safe evaluation of offline learning methods.
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talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
CS 106: Fundamentals of Computer Science
Teaching Assistant, The American University in Cairo, Department of Computer Science, 2014
This course covers fundamentals of computer science and basics of programming taught in C++ . My role included helping students through labs and office hours.
Coursera’s Deep Learning Specialization
Instructor, Alama Initiative, Egypt, 2018
I volunteered to teach deep learning concepts for a group of 20 undergrad and grad students taking Coursera’s deep-learning specialization following up with their progress throughout the courses. This was done through a student-run initiative called ‘Alama’.
CMPUT 328: Visual Recognition
Teaching Assistant, University of Alberta, Department of Computing Science, 2019
This course covers computer vision tasks with deep learning, taught with PyTorch. My role included teaching a weekly lab on computer vision with deep learning bascis, and preparing the assignments/ quizzes.
CMPUT 296: Basics of Machine Learning
Teaching Assistant, University of Alberta, Department of Computer Science, 2020
The course tackles the basics of machine learning: material can be found on the website
CMPUT 656: Interactive Machine Learning
Teaching Assistant, University of Alberta, Department of Computer Science, 2020
The course tackles basics of AI and machine learning systems interacting with one or more humans: material can be found on the website
CMPUT 296: Basics of Machine Learning
Teaching Assistant, University of Alberta, Department of Computer Science, 2021
The course tackles the basics of machine learning: material can be found on the website