Artem Prokhorov

University of Nottingham > Engineering > Departments > Mechanical, Materials and Manufacturing Engineering > PDM Exhibitions > 2025 > BEng students > Artem Prokhorov

The Performance Tracking E-Scooter project aims to revolutionise urban commuting with a sustainable, user-friendly, and convenient electric scooter tailored for city environments.

I’m a final-year BEng Product Design and Manufacture student at the University of Nottingham, graduating in June 2025. As an aspiring Machine Learning Engineer, I’m passionate about crafting AI-driven solutions that solve real-world problems with a customer-centric focus. My expertise in Machine Learning, coding, and product design, combined with hands-on experience in electrical engineering, makes me a versatile problem-solver ready to drive innovation in the AI industry.

As an ML Engineering Intern and AI Lead, I developed a Python-based web scraping algorithm that cut database update times by 99.6%, from 8 hours to 2 minutes. I built and deployed Neural Networks and Random Forests to enhance data integrity, led a team of engineers, and created a 24/7 Streamlit web app for seamless data analysis. At Baker Hughes, I upgraded a Condition Monitoring System with predictive maintenance using linear regression, automated reporting with Pandas and Matplotlib, and boosted fault analysis efficiency by 30% through intuitive dashboards. My ability to script in Python, Bash, and PowerShell, integrate projects via GitHub, and configure TCP/IP in Linux environments underscores my technical versatility.

My Performance Tracking E-Scooter project reflects my ability to blend AI with product design, featuring AI-driven obstacle detection and smart route optimization for urban commuters. Certified in Machine Learning by Stanford and Google, I’m proficient in PyTorch, scikit-learn, LLMs, and multi-agent systems, with skills in Azure, Streamlit, and data visualisation. I’m eager to join a forward-thinking company where I can leverage my interdisciplinary skills to create impactful, user-focused AI solutions.

Artem Prokhorov, BEng Product Design and Manufacture

Performance Tracking E-Scooter (inspired by CyberTruck, powered by AI)

The Performance Tracking E-Scooter project aims to revolutionise urban commuting with a sustainable, user-friendly, and convenient electric scooter tailored for city environments. Designed to enhance safety and efficiency, the e-scooter integrates advanced AI-driven features and innovative hardware to meet the needs of modern commuters. The core objective is to provide a reliable, eco-friendly transport solution that prioritises safety, comfort, and adaptability.

The e-scooter employs AI for real-time obstacle detection, utilising sensors such as an Inertial Measurement Unit (IMU), GPS, and environmental monitors for temperature and humidity. These sensors, powered by a Raspberry Pi, enable autonomous braking to prevent collisions, with alerts displayed on an intuitive Human-Machine Interface (HMI). The HMI provides a clear navigation map, leveraging AI for smart route optimisation to calculate the fastest and safest paths, enhancing both efficiency and safety for urban travel.

Additional sensors track speed and acceleration, providing performance data to optimize rider experience. By combining cutting-edge AI, robust hardware, and a user-centric design, this e-scooter addresses urban mobility challenges, offering a safe, efficient, and environmentally conscious solution for commuters. The project showcases innovation in sustainable transport, with potential to transform urban commuting.