Projects - Center for Environmental Intelligence

Development of energy-efficient perovskite light-emitting diodes and solar cells

Principal Investigators & Key Members:

Le Van Quynh, PhD

This project is aimed at developing semiconductor technology of producing a new generation of light-emitting diodes for energy efficiency and flexible solar panels for remote areas. The project focus on making real devices that will be used in the renewable energy through real solar cells, light-emitting diodes (LEDs) based on emerging materials such as semiconductor nanocrystals, mxenes and perovskite semiconductors. The project allows VinUni students to expose with the latest technologies in semiconductor, electrical engineering, mechanical engineering, computer science and materials science.

Green Serverless Computing for Resource-Efficient AI Training

Principal Investigators & Key Members:

Kok-Seng Wong, PhD

With the increasing demand for artificial intelligence (AI) applications, there is a need for efficient, sustainable computation solutions for AI model training. Traditional server-based architectures often consume substantial energy and resources, leading to environmental concerns and high operational costs. Serverless computing offers a promising approach by providing on-demand computing resources and scaling capabilities. This research explores the feasibility of leveraging serverless computing for efficient AI training, focusing on minimizing energy consumption and resource utilization.

Smart Functional Textiles for the Environment

Principal Investigators & Key Members:

Nguyen Dang Tung, PhD

The core of this research lies in engineering fibers and textiles that can serve as portable, flexible sensing/energy generation/energy storage devices. A major objective is to create these solar textiles that will have the capability to power a wide range of devices, particularly those connected to the Internet of Things (IoT). IoT devices, which range from home appliances to healthcare monitors, require continuous power to collect and transmit data. By embedding solar energy harvesting and storage capabilities directly into the fabric, these textiles will provide a sustainable energy solution, reducing reliance on traditional power sources and batteries. In parallel, flexible sensing devices on fibers and textiles will used in environmental monitoring (e.g. water pollution and water level).

Advancing Sustainable Electric Vehicle Charging through Green Infrastructure and Smart Charging Techniques

Principal Investigators & Key Members:

Do Danh Cuong, PhD.

In today's world, increasing greenhouse gas emissions pose a threat due to global warming. To combat this, transitioning from fossil fuel vehicles to electric vehicles (EVs) is crucial. However, as EV numbers rise, so does energy demand on the grid, causing potential issues. This study proposes renewable EV charging stations to mitigate grid challenges.

Digital Twin Platform to Empower Communities towards an Eco-friendly and Heathy Future

Principal Investigators & Key Members:

Nguyen Ngoc Doanh, PhD

Urban development in Vietnam and other developing countries has led to increased greenhouse gas emissions. Smart transportation, enhanced by AI and ML, can reduce these emissions by implementing real-time congestion pricing based on sensor data. This research will create digital twins of transportation and air quality in Southeast Asian cities, simulating various policy scenarios to encourage sustainable transportation. The goal is to develop economic mechanisms and policies that promote shifts to electric vehicles, transit, and ridesharing, ultimately reducing pollution and congestion. Innovation lies in using AI/ML to design dynamic, real-time congestion pricing and understand public opinions on sustainable transportation.

Carbon Stock Estimation and Biodiversity Assessment in Vietnam Forests using Remotely Sensed Data and Deep Learning Neural Networks

Principal Investigators & Key Members:

Nidal Kamel, PhD.

Estimating carbon stocks is essential for understanding the Earth's carbon cycle, assessing the impact of human activities on the environment, and guiding efforts to mitigate climate change. By quantifying the amount of carbon stored in ecosystems like forests, soil, and oceans, scientists and policymakers can make informed decisions about land-use planning, conservation efforts, and carbon sequestration strategies. In the context of carbon credits, accurate carbon stock estimation is crucial for ensuring the credibility and effectiveness of carbon offset projects.