Aresh Dadlani

Assistant Professor

Office: B 113B
Calgary, AB, Canada T3E 6K6
E-mail: adadlani[AT]mtroyal.ca

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NEWS

News Apr.'26: Received NSERC DDG
News Jan.'26: Adjunct at U of A.
News Jul.'24: Joined MRU.
News Feb.'22: Joined U of A.
News Feb.'21: Elevated to SMIEEE.

I am an Assistant Professor in the School of Computing Sciences and Mathematics at Mount Royal University (MRU), where I lead the INtelligent COmmunications and DEcision netwoRks (INCODER) Lab. My research focuses on data-driven decision-making and the stochastic modeling and control of large-scale networked systems. My current research interests include:

  • Age of Information (AoI) in wireless networks
  • Machine learning for emerging communication networks
  • Projection and control of spreading processes over complex networks
  • Learning-based resource allocation in cyber-physical systems
  • Modeling and performance evaluation

Before joining MRU in 2024, I was a Postdoctoral Fellow in the Department of Computing Science, University of Alberta. From 2017 to 2022, I was an Assistant Professor in the School of Engineering and Digital Sciences at Nazarbayev University, where I led the Complex Networks and Systems Laboratory (CNSL). I received my PhD in Information and Communications from the Gwangju Institute of Science and Technology (GIST), South Korea, under the supervision of Kiseon Kim and Khosrow Sohraby. I also hold MSc and BSc degrees in Computer Engineering from the University of Tehran, Iran.

** Actively seeking motivated students with a passion for research. E-mail me if interested.


Recent Publications: (Full List)

  • Age of Information in Unreliable Tandem Queues
    IEEE Communications Letters, July 2025. [Impact factor: 4.4]

  • Age Analysis of Correlated Information in Multi-Source Updating Systems with MAP Arrivals
    IEEE Communications Letters, May 2024. [Impact factor: 4.1]

  • Cost-Effective Activity Control of Asymptomatic Carriers in Layered Temporal Social Networks
    IEEE Transactions on Computational Social Systems, April 2024. [Impact factor: 5]