Engineering Projects

Mobility Rover for Landlocked Communities

Why this project: To construct a vehicle that can reach communities that lack road access or healthcare so that both medical staff and necessary supplies can get to the areas that need them the most.

• Created and visualized a rover to go over rough, hill-like terrains and carry local people the materials they need to survive.

• Did extensive research and identified materials which are both durable and cost-effective to provide maximum resistance.

• Constructed the mechanical stability systems for secure travel over difficult grounds

• Designed test plans of the conditions that would be encountered in the field to confirm the feasibility of the vehicle in real life.

• Impact: Made it possible to take medical attention, electricity, hygiene, and food to the less favored areas in a quicker way.

Keep space for photos of the Model (Sketches, Model Drawing, 3D Printed Model).

Ocean Clean-Up Rover (FASAL BOT)

Why this project: To fight water pollution by extracting floating wastes from oceans and rivers in a way that saves energy.

• Created during the Expo 2020 Napkin Challenge and went up to the Young Innovator Finalist stage.

• Made a small-sized rover with a mechanical system to gather floating waste and purify water at the same time.

• Worked out a 3D model, energy-saving and low-maintenance were the main optimizations.

• He presented the prototype at the Sustainability Pavilion during Expo 2020 to an international audience.

• Impact: Encouraged the public to consider sustainable engineering solutions that can be adapted for ocean sustainability.

AI Model for Pneumonia Detection

Why this project: To investigate whether AI could aid doctors in making quicker and more accurate decisions in the case of dangerous diseases.

• Gathered and cleansed medical imaging datasets (chest X-rays).

• Developed and trained a deep learning model by using Python to recognize pneumonia in chest X-rays.

• On the one hand, AI testing and performance measurement were held; on the other, the medical staff was informed about accuracy, fairness, and trustworthiness of the AI system in their clinical decision-making process.

• Looking into the issue of AI in medicine, the group considered privacy protection and common use, among other things.

• Impact: Shown the potential of AI to speed up diagnostic processes while simultaneously exposing its current limits.

Machine Learning System — Stanford Pre-Collegiate

Why this project: This project was made to adopt the real-world application of supervised, unsupervised, and reinforcement learning to tackle practical problems by deepening AI knowledge.

• Went through a two-week rigorous training program with Stanford tutors.

• Python was the programming language used to implement the algorithms, in which linear algebra, calculus, and optimization skills were applied.

• The author created a neural-network model that predicts the breakdown of machines by using real data.

• Investigated the presence of bias in AI systems and tried to find ways to reduce it.

• Impact: Improved knowledge of theory, practical application skills, and ethical issues in AI-related fields.

Extended Project Qualification — EPQ

Why this project: To delve into the effects that airfoil design had on the performance of aircraft, connecting physics, math, and engineering.

• Explored airfoil shape and its effects on lift and drag for subsonic vs supersonic conditions.

• Did mathematical modeling and aerodynamic simulations to illustrate differences in flight regimes.

• Started working on a research paper at the university level by applying core research and technical writing competences.

• Improved abilities in independent study, data interpretation, and research-related communication.

• The impact: The project provided ideas about the issues of design that the aerospace field facing which help the readiness of the advanced study by giving the hints based on this research.