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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.
Pages
About me
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
This page is still in proccess..
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CTFs for fun ;)
🌟 Recently, I had the opportunity to attend the CodeFest meetup organized by #Baot. The event featured a workshop on Capture the Flag (CTF) challenges, which introduced me to this concept. These challenges tested my creativity, problem-solving skills, and knowledge of various technologies. The CTF challenges covered a wide range of subjects, including cryptography, steganography, web exploitation, reverse engineering, and network analysis. Each challenge presented a unique puzzle to solve, pushing me to sharpen my scripting, debugging, network analysis, and vulnerability exploitation skills. Participating in these challenges was a captivating experience that allowed me to apply my knowledge in real-world scenarios. Even if it is not my main interest area it was good to discover new technologies and to deepen my understanding of cybersecurity and the technologies involved. 
publications
A Geometric Method for Improved Uncertainty Estimation in Real-time
Published in Uncertainty in Artificial Intelligence (UAI), 2022
Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk management. Post-hoc model calibrations can improve models’ uncertainty estimations without the need for retraining, and without changing the model. Our work puts forward a geometric-based approach for uncertainty estimation. Roughly speaking, we use the geometric distance of the current input from the existing training inputs as a signal for estimating uncertainty and then calibrate that signal (instead of the model’s estimation) using standard post-hoc calibration techniques. We show that our method yields better uncertainty estimations than recently proposed approaches by extensively evaluating multiple datasets and models.
Recommended citation: Chouraqui, G., Cohen, L., Einziger, G., & Leman, L. (2022, August). A geometric method for improved uncertainty estimation in real-time. In Uncertainty in Artificial Intelligence (pp. 422-432). PMLR. https://arxiv.org/abs/2206.11562
Uncertainty Estimation based on Geometric Separation
Published in Under review, 2023
In machine learning, accurately predicting the probability that a specific input is correct is crucial for risk management. This process, known as uncertainty (or confidence) estimation, is particularly important in mission-critical applications such as autonomous driving. In this work, we put forward a novel geometric-based approach for improving uncertainty estimations in machine learning models. Our approach involves using the geometric distance of the current input from existing training inputs as a signal for estimating uncertainty, and then calibrating this signal using standard post-hoc techniques. We demonstrate that our method leads to more accurate uncertainty estimations than recently proposed approaches through extensive evaluation on a variety of datasets and models. Additionally, we optimize our approach so that it can be implemented on large datasets in near real-time applications, making it suitable for time-sensitive scenarios.
Recommended citation: Chouraqui, G., Cohen, L., Einziger, G., & Leman, L. (2023). Uncertainty Estimation based on Geometric Separation. arXiv preprint arXiv:2301.04452. https://arxiv.org/abs/2301.04452
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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