Youssef M. Hussein

PhD Student, Department of Computer Science and Engineering
University of Minnesota
4-204B Kenneth H. Keller Hall
200 Union Street SE
Minneapolis, MN 55455
husse408 [at] umn.edu, youssefhussien [at] aucegypt.edu
Google scholar | | LinkedIn || Github || Resume

I am currently a second-year Ph.D. student at the Database Management Lab at the University of Minnesota, under the supervision of Prof. Mohamed Mokbel. I completed my Bachelor of Science degree in Computer Engineering at the American University in Cairo (AUC) in 2023, with double minors in Mathematics and Arabic Studies.

My Thesis “GPU Accelrated Dataflow analysis” revolved around GPU-accelerated dataflow analysis. This project, supervised by Dr. Karim Aly and Dr. Cherif Salama, proposed a GPU-compatible implementation based on matrix representation to accelrelate the process of dataflow analysis. Our work achieved remarkable speedup of up to 250% compared to CPU implementations, offering a more efficient solution for real-time development environments and enhancing bug detection in software development.

During my time at AUC, I served as a Junior Research Assistant at the Computer Science Department under the guidance of Dr. Nouri Sakr. In this position, I contributed to a research project addressing the dual-criticality scheduling problem using various Reinforcement Learning (RL) models. Additionally, I worked as a Research Engineer in the Construction Engineering Department at AUC, where I led the development of an embedded application for a multidisciplinary research project. My responsibilities included proposing a multi-objective optimization model for sustainable farming while minimizing water consumption. I utilized Python libraries such as Beautiful Soup and Pandas to scrape real-time data for the app database using Excel VBA.

In terms of industry experience, I had the opportunity to work as a Platforms Software Engineer Intern at Dell Technologies. There, I actively contributed to the 5G team, utilizing C++ to develop multiple modules. I conducted unit testing using GoogleTest, worked with containers using Docker and Podman, and deployed continuous code changes using GitHub, following an Agile working framework. Furthermore, I served as a Computer Fundamentals Nanodegree Session Lead at Udacity, mentoring and supervising a cohort of 200 students aged 12-17. I guided them through various concepts, including HTTP, TCP/IP, HTML, CLIs, algorithmic problem-solving, and data encoding, as part of the Digital Egypt Cubs Initiative. We achieved an impressive graduation rate of 97%. Additionally, I interned at Silicon Arena LLC, collaborating as a Front-End Engineer Intern within an Agile framework. I contributed to the development of multiple components using React.js for an open-source eCommerce website based on documented software requirements specifications (SRS).

Overall, my academic and industry experiences have equipped me with a diverse skill set and a strong foundation in computer science and engineering. I am passionate about pushing the boundaries of technology and contributing to innovative research and development projects.

Research interest: Spatial data, data management systems, and software engineering. These days, I’m particularly interested in Large Language Models (LLMs) evaluation and their applications in spatial data.

Announcement: This is a new website, it’s still under construction. You might find some inc :)

news

Sep 1, 2023	Joined the Data Management Lab at UMN as new Ph.D. student, supervised by Prof. Mohamed Mokbel
Jun 1, 2023	Garduated from the American University in Cairo with high honors. I recieved the Dean’s List award (notable academic honors degree) in Spring 2021, Fall 2021, Spring 2022, Fall 2022 and Spring 2023 semesters.
Sep 1, 2019	Received a fully funded merit scholarship from the AGFE foundation with a 3.5% acceptance rate to pursue my undergraduate degree at AUC.

latest posts

Jul 12, 2023	a post with bibliography
Jul 4, 2023	a post with jupyter notebook
May 12, 2023	a post with custom blockquotes

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selected publications

2023

Economic Land Utilization Optimization Model

Ossama A. Hosny, Elkhayam M. Dorra, Youssef Hussein, and 8 more authors

Sustainability, 2023

Abs PDF

Recently, population growth and resource depletion have been matched by a growing demand for self-sustaining communities. Numerous studies promote sustainable solutions to the concerns of climate change and food scarcity. This study aims at creating an automated Economic Land Utilization Optimization Model (ELUOM) that identifies sustainable and cost-effective agricultural practices. Soil, water & climatic characteristics of over 400 crops are gathered in a relational database to build the model. Evolutionary algorithms are utilized to filter the database based on user input. Optimization process is then performed on all possible utilization plans of the filtered crops to maximize the 20-year return while minimizing water consumption. The model is verified on a case study in Giza, Egypt where it shows the potential of increasing the return/m3 of water by 370% versus current practices. This research also studies the application of ELOUM on a vacant plot in the American university in Cairo, Egypt.

2021

Dual-criticality scheduling on non-preemptive, dynamic processors using RL agents

Nourhan Sakr, Youssef Hussein, and Karim Farid

Jul 2021

Abs PDF

Real-time embedded systems have stringent non-functional requirements on cost, weight, and energy that give rise to the study of mixed-criticality (mc) systems, where functionalities of dierent criticality levels are consolidated into a shared hardware platform (Barhorst et al. [1], Burns and Davis [6]). The literature discusses the schedulability of mc systems under various conditions and objectives (Baruah et al. [3], Gu et al. [7], Baruah et al. [4]). In this work, we study a dual-criticality, non-preemptive system with a varying-speed uniprocessor. This varying-speed processor makes mc scheduling dynamic: In real-time systems, it cannot be predetermined if, when or for how long the processor would degrade, i.e. its speed would drop. Under speed stochasticity, it is critical to guarantee the running of high (hi) criticality jobs by their deadlines, sometimes even at the cost of not running low (lo) criticality jobs at all, when operating under degradation. Scheduling mc systems non-preemptively (even without degradation) is N P-hard (Lenstra et al. [9]). Baruah and Guo [5] model an LP to preemptively schedule dualcriticality jobs on a varying-speed processor. Agarwal and Baruah [2] further discuss the online nature of the problem and its intractability. We agree with the authors that mc scheduling is inherently an online problem, as it better depicts real-time scheduling and the dynamic nature of this problem. Therefore, we devise deep reinforcement learning (deep rl or drl) to tackle the problem presented by Baruah and Guo [5], under both the oine and online setting.