Omid Ardakanian

Assistant Professor

University of Alberta

Canada

Research Interests

Explore related searches

Professors in Canada

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Dr. Omid Ardakanian is an Assistant Professor at the University of Alberta, Canada. His research areas include federated learning, privacy protection in sensing systems, and machine learning applications in smart homes. Recent publications highlight his work on robust frameworks for non-intrusive load monitoring and human activity recognition, as well as techniques for effective time series forecasting.

Articles (20)

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Collaborators (3)

Gesche Huebner

Professor

University of Exeter

UNITED KINGDOM

View details

Mohammad Saiedur Rahaman

RMIT University

AUSTRALIA

View details

Hamzeh Khazaei

Associate Professor of Computer Science

York University

CANADA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Omid Ardakanian

Research Interests

Explore related searches

Contact this professor

About

Articles (20)

Robust peer-to-peer federated learning for non-intrusive load monitoring in smart homes

Age Analysis of Correlated Information in Multi-Source Updating Systems With MAP Arrivals

Age Analysis of Correlated Information in Multi-Source Updating Systems with MAP Arrivals

Centaur: Robust Multimodal Fusion for Human Activity Recognition

Centaur: Robust Multimodal Fusion for Human Activity Recognition

Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning

A Robust and Privacy-Aware Federated Learning Framework for Non-Intrusive Load Monitoring

A Robust and Privacy-Aware Federated Learning Framework for Non-Intrusive Load Monitoring

<span style="font-variant: small-caps">PePTM</span>: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess

Efficacy of temporal and spatial abstraction for training accurate machine learning models: A case study in smart thermostats

Addressing data inadequacy challenges in personal comfort models by combining pretrained comfort models

Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

Flexible, decentralised access control for smart buildings with smart contracts

Adaptive Congestion Control for Electric Vehicle Charging in the Smart Grid

Adaptive Congestion Control for Electric Vehicle Charging in the Smart Grid

Identifying grey-box thermal models with Bayesian neural networks

Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Guest Editorial Theory and Application of PMUs in Power Distribution Systems

Bayesian Learning-Based Harmonic State Estimation in Distribution Systems With Smart Meter and DPMU Data

Collaborators (3)

Gesche Huebner

Mohammad Saiedur Rahaman

Hamzeh Khazaei

Make your next move.