Raed Al Kontar

About

Dr. Raed Al Kontar is an associate professor in the Industrial and Operations Engineering department at the University of Michigan. He is also an affiliate with the Michigan Institutes for Data Science and Computational Discovery and Engineering. His research focuses on developing data science methods for solving engineering problems, with an emphasis on personalized and distributed data analytics. His work aims to effectively integrate knowledge from diverse data sources while maintaining data privacy and personalization, enabling sources to retain tailored models and decentralize inference. Dr. Al Kontar leads the Data Science Lab, which concentrates on probabilistic models and precision data science. His research has been highly recognized, with his group winning 12 best paper awards since 2022 across prominent organizations such as INFORMS, ASA, and IISE. His research is supported by notable agencies including the NSF, NIH, and NLM, as well as industry collaborators. His expertise encompasses areas such as energy and sustainability, health and human safety, and optimization, with a focus on federated learning, uncertainty quantification, digital twins, and heterogeneity in data sources.

Research topics

• Artificial Intelligence
• Computer Science
• Data Mining
• Mathematics
• Algorithm
• Machine Learning
• Computer Security
• Statistics
• Mathematical optimization
• Physics

Selected publications

• The Internet of Federated Things (IoFT)

  IEEE Access · 2021 · 54 citations

  1st authorCorresponding

  The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privac…

  DOI

• GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning

  INFORMS Journal on Data Science · 2022 · 37 citations

  Senior authorCorresponding

  In this paper, we propose GIFAIR-FL, a framework that imposes group and individual fairness (GIFAIR) to federated learning (FL) settings. By adding a regularization term, our algorithm penalizes the spread in the loss of client groups to drive the optimizer to fair solutions. Our framework GIFAIR-FL can accommodate both global and personalized settings. Theoretically, we show convergence in nonconvex and strongly convex settings. Our convergence guarantees hold for both independent and identical…

  PublisherDOI

• Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach

  IEEE Transactions on Pattern Analysis and Machine Intelligence · 2020-04-15 · 33 citations

  article1st author

  Recently there has been an increasing interest in the multivariate Gaussian process (MGP) which extends the Gaussian process (GP) to deal with multiple outputs. One approach to construct the MGP and account for non-trivial commonalities amongst outputs employs a convolution process (CP). The CP is based on the idea of sharing latent functions across several convolutions. Despite the elegance of the CP construction, it provides new challenges that need yet to be tackled. First, even with a modera…

  PublisherDOI

• Joint Models for Event Prediction From Time Series and Survival Data

  Technometrics · 2020 · 21 citations

  Senior authorCorresponding

  We present a nonparametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously est…

  DOI

• Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes

  Neural Information Processing Systems · 2020-01-01 · 18 citations

  article

  Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to their generalization performance and intrinsic computational advantage. However, the fact that the stochastic gradient is a biased estimator of the full gradient with correlated samples has led to the lack of theoretical understanding of how SGD behaves under correlated settings and hindered its use in such cases. In this…

  Publisher

Recent grants

• CAREER: From the Cloud to the Crowd: An Enabling Solution for the Internet of Federated Things

  NSF · $500k · 2022–2027

Frequent coauthors

• Xubo Yue

  26 shared

• Naichen Shi

  University of Michigan–Ann Arbor

  16 shared

• Seokhyun Chung

  14 shared

• Qiyuan Chen

  University of Michigan–Ann Arbor

  12 shared

• Corey A. Lester

  University of Michigan–Ann Arbor

  11 shared

• X. Jessie Yang

  11 shared

• Shiyu Zhou

  10 shared

• Maher Nouiehed

  American University of Beirut

  9 shared

Education

• PhD, Industrial & Systems Engineering

  University of Wisconsin-Madison

  2018

• MS, Statistics

  University of Wisconsin–Madison

  2017

• BE, Civil & Environmental Engineering (Math Minor)

  American University of Beirut

  2014

Awards & honors

• IISE Transactions Service Award (2024)
• NSF CAREER Award (2022)
• Best Refereed Paper Recognition, Quality, Statistics & Relia…
• Featured Article in the December 2023 Issue of the Industria…
• Best Paper Recognition, Data Mining (DM) section, INFORMS An…

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