Ermin Wei

About

Ermin Wei is an Associate Professor of Electrical and Computer Engineering, Industrial Engineering and Management Sciences, and (by courtesy) Computer Science at Northwestern University. His research interests focus on the control and operation of networked systems, with particular emphasis on market analysis of smart grids and energy networks. His work also includes the development of large-scale distributed optimization algorithms and theory, with a focus on nonlinear convex optimization, network optimization, asynchronous algorithms, and their applications. Wei's contributions aim to advance the understanding and efficiency of complex networked systems, impacting areas such as energy management and distributed computing.

Research topics

• Computer Science
• Computer Security
• Mathematical optimization
• Economics
• Artificial Intelligence
• Data Mining
• Mathematics
• Computer network
• Operations research
• Database

Selected publications

• Age-Dependent Differential Privacy

  IEEE Transactions on Information Theory · 2023-12-13 · 120 citations

  article

  The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, which may provide a new tool for time-varying databases. In this work, we introduce age-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new fram…

  PublisherDOI

• Faithful Edge Federated Learning: Scalability and Privacy

  IEEE Journal on Selected Areas in Communications · 2021 · 58 citations

  Federated learning enables machine learning algorithms to be trained over decentralized edge devices without requiring the exchange of local datasets. Successfully deploying federated learning requires ensuring that agents (e.g., mobile devices) faithfully execute the intended algorithm, which has been largely overlooked in the literature. In this study, we first use risk bounds to analyze how the key feature of federated learning, unbalanced and non-i.i.d. data, affects agents’ incentives to vo…

  DOI

• A Two-Stage Decomposition Approach for AC Optimal Power Flow

  IEEE Transactions on Power Systems · 2020 · 31 citations

  Senior authorCorresponding

  The alternating current optimal power flow (AC-OPF) problem is critical to power system operations and planning, but it is generally hard to solve due to its nonconvex and large-scale nature. This paper proposes a scalable decomposition approach in which the power network is decomposed into a master network and a number of subnetworks, where each network has its own AC-OPF subproblem. This formulates a two-stage optimization problem and requires only a small amount of communication between the m…

  DOI

• Optimal and Quantized Mechanism Design for Fresh Data Acquisition

  IEEE Journal on Selected Areas in Communications · 2021 · 24 citations

  The proliferation of real-time applications has spurred much interest in data freshness, captured by the age-of-information (AoI) metric. When strategic data sources have private market information, a fundamental economic challenge is how to incentivize them to acquire fresh data and optimize the age-related performance. In this work, we consider an information update system in which a destination acquires, and pays for, fresh data updates from multiple sources. The destination incurs an age-rel…

  DOI

• On the Convergence of Nested Decentralized Gradient Methods with Multiple Consensus and Gradient Steps

  IEEE Transactions on Signal Processing · 2020 · 13 citations

  Senior authorCorresponding

  In this paper, we consider minimizing a sum of local convex objective functions in a distributed setting, where the cost of communication and/or computation can be expensive. We extend and generalize the analysis for a class of nested gradient-based distributed algorithms (NEAR-DGD; Berahas, Bollapragada, Keskar and Wei, 2018) to account for multiple gradient steps at every iteration. We show the effect of performing multiple gradient steps on the rate of convergence and on the size of the neigh…

  DOI

Recent grants

• NRI: INT: Robotic Shepherding for Flow Control in Uncertain Dynamic Environments

  NSF · $1.4M · 2020–2025

Frequent coauthors

• Randall A. Berry

  30 shared

• Meng Zhang

  12 shared

• Haoran Yu

  Beijing Institute of Technology

  10 shared

• Asuman Ozdaglar

  10 shared

• Charikleia Iakovidou

  9 shared

• Fatemeh Mansoori

  Northwestern University

  8 shared

• Shenyinying Tu

  Northwestern University

  6 shared

• Binnan Zhuang

  6 shared

Labs

• Communications and Networking LaboratoryPI

Education

• MS, PhD, Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2014

• BS, Computer Engineering, Math, Finance

  University of Maryland

  2008

Similar researchers at Northwestern University

• Tobin J. Marksh-176
• Joseph Hupph-171
• James D Thomash-160