Tiny ML and Edge AI : My research interest lies in the development of energy-efficient and low-latency machine learning models deployable on resource-constrained edge devices. TinyML and Edge AI represent a transformative shift towards intelligent, real-time decision-making at the edge, enabling applications in healthcare, IoT, agriculture, and smart cities. I am particularly focused on model optimization techniques such as quantization, pruning, and neural architecture search, as well as secure and privacy-preserving edge inference.
IoT – HCI and education : I am also interested in the integration of IoT with Augmented Reality (AR) and Virtual Reality (VR) to create immersive, interactive, and data-driven educational environments. By leveraging real-time data from IoT sensors and combining it with AR/VR interfaces, I aim to design adaptive learning systems that respond dynamically to learners’ behaviour, environments, and needs. This includes applications such as smart classrooms, virtual labs, and remote learning setups where physical-digital interactions enhance engagement, contextual understanding, and experiential learning.
Awards
President’s Scholarship – SFU (2021)
Memberships
No data available
Publications
Patents
Projects
Scholars
Interests
Edge AI
IOT Edge Computing
Mobile Applications (Cross platform)
Thought Leaderships
There are no Thought Leaderships associated with this faculty.
Tiny ML and Edge AI : My research interest lies in the development of energy-efficient and low-latency machine learning models deployable on resource-constrained edge devices. TinyML and Edge AI represent a transformative shift towards intelligent, real-time decision-making at the edge, enabling applications in healthcare, IoT, agriculture, and smart cities. I am particularly focused on model optimization techniques such as quantization, pruning, and neural architecture search, as well as secure and privacy-preserving edge inference.
IoT – HCI and education : I am also interested in the integration of IoT with Augmented Reality (AR) and Virtual Reality (VR) to create immersive, interactive, and data-driven educational environments. By leveraging real-time data from IoT sensors and combining it with AR/VR interfaces, I aim to design adaptive learning systems that respond dynamically to learners’ behaviour, environments, and needs. This includes applications such as smart classrooms, virtual labs, and remote learning setups where physical-digital interactions enhance engagement, contextual understanding, and experiential learning.
Tiny ML and Edge AI : My research interest lies in the development of energy-efficient and low-latency machine learning models deployable on resource-constrained edge devices. TinyML and Edge AI represent a transformative shift towards intelligent, real-time decision-making at the edge, enabling applications in healthcare, IoT, agriculture, and smart cities. I am particularly focused on model optimization techniques such as quantization, pruning, and neural architecture search, as well as secure and privacy-preserving edge inference.
IoT – HCI and education : I am also interested in the integration of IoT with Augmented Reality (AR) and Virtual Reality (VR) to create immersive, interactive, and data-driven educational environments. By leveraging real-time data from IoT sensors and combining it with AR/VR interfaces, I aim to design adaptive learning systems that respond dynamically to learners’ behaviour, environments, and needs. This includes applications such as smart classrooms, virtual labs, and remote learning setups where physical-digital interactions enhance engagement, contextual understanding, and experiential learning.
