1 citations · 4 across the 8 of their papers we have counts for
8 papers
EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression
Motahare Mounesan, Irfan Khan
Recent advances in multimodal large language mod- els (MLLMs) have opened new opportunities for edge intelligence by enabling reasoning across heterogeneous sensor modalities, such…
Reinforcement Learning-Driven Edge Management for Reliable Multi-view 3D Reconstruction
Motahare Mounesan, Sourya Saha, Houchao Gan +2
Real-time multi-view 3D reconstruction is a mission-critical application for key edge-native use cases, such as fire rescue, where timely and accurate 3D scene modeling enables sit…
Variational Autoencoder-Based Black-Box Adversarial Attack on Collaborative DNN Inference
Shima Yousefi, Motahare Mounesan, Saptarshi Debroy
In recent years, Deep Neural Networks (DNNs) have become increasingly integral to IoT-based environments, enabling realtime visual computing. However, the limited computational cap…
Infer-EDGE: Dynamic DNN Inference Optimization in 'Just-in-time' Edge-AI Implementations
Motahare Mounesan, Xiaojie Zhang, Saptarshi Debroy
Balancing mutually diverging performance metrics, such as end-to-end latency, accuracy, and device energy consumption, is a challenging undertaking for deep neural network (DNN) in…
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge
Motahare Mounesan, Xiaojie Zhang, Saptarshi Debroy
Balancing mutually diverging performance metrics, such as, processing latency, outcome accuracy, and end device energy consumption is a challenging undertaking for deep learning mo…
VECA: Reliable and Confidential Resource Clustering for Volunteer Edge-Cloud Computing
Hemanth Sai Yeddulapalli, Mauro Lemus Alarcon, Upasana Roy +5
Volunteer Edge-Cloud (VEC) computing has a significant potential to support scientific workflows in user communities contributing volunteer edge nodes. However, managing heterogene…
Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud
Motahare Mounesan, Mauro Lemus, Hemanth Yeddulapalli +2
In recent times, Volunteer Edge-Cloud (VEC) has gained traction as a cost-effective, community computing paradigm to support data-intensive scientific workflows. However, due to th…
Fleet management for ride-pooling with meeting points at scale: a case study in the five boroughs of New York City
Motahare Mounesan, Vindula Jayawardana, Yaocheng Wu +2
Introducing meeting points to ride-pooling (RP) services has been shown to increase the satisfaction level of both riders and service providers. Passengers may choose to walk to a…