Raed Al Kontar, Ph.D

Raed Al Kontar
Raed Al Kontar

Raed Al Kontar

Publications | Featured Papers | Latest News | Group | Code & Talks

I lead a data science group at the Industrial & Operations Engineering (IOE) department at the University of Michigan. I am also an affiliate with both the Michigan Institutes for Data Science and Computational Discovery and Engineering.

I work on developing data science methods for solving engineering problems. I also enjoy occasional theoretical endeavors beyond application. My focus is on personalized, collaborative and distributed data analytics, where knowledge from diverse data sources is effectively integrated. This approach allows sources to retain personalized models tailored to their unique features, distribute or decentralize model inference, and protect personal data when needed.

Currently, my research aims to answer three questions (see featured papers for examples of each):

  1. Descriptive: How to extract what is shared and unique across datasets?
  2. Predictive: How can multiple entities (such as hospitals or IoT devices) collaboratively improve the predictive power of their models while keeping their personal data private?
  3. Prescriptive: How to fast-track and improve optimal design by effectively distributing trial & error efforts across collaborating entities?

Check our paper “The Internet of Federated Things (IoFT)” which has inspired this research

Research Keywords: Personalization, Collaboration, Heterogeneity, Federated Learning, Uncertainty Quantification, Black-box optimization, Digital Twins

My outstanding student, Naichen Shi, is on the job seeking academic positions. For more information about his research and accomplishments, please visit his website and check out his two featured papers published in the Journal of Machine Learning Research and Technometrics.
Naichen and our team is a finalist in both the:

  • Best Paper competition within the Data Mining (DM) Section at INFORMS 2024
  • Best Refereed Paper competition within the Quality, Statistics, and Reliability (QSR) section at INFORMS 2024

Update: Naichen is joining Northwestern as an Assistant Professor !

Featured Papers

Personalized PCA: Decoupling Shared and Unique Features, Naichen Shi, Raed Kontar
Journal of Machine Learning Research (JMLR), 2024, [Link, Youtube Video, Code]

To understand heterogeneity across different data sources, we revisit one of the most fundamental and successful data analytics tools: principal component analysis (PCA). Specifically, we decompose heterogenous datasets into functions of global principal components (PCs) shared by all and local PCs unique to each dataset. At the heart of our approach is enforcing  orthogonality across the shared and unique PCs. The orthogonality of PCs implies that the shared and unique features span different subspaces, thus describing different patterns in the data sources.

Unlike traditional PCA, such a model is not always identifiable: take the simple case where all datasets are the same, then decoupling the shared and unique features is impossible. Here we provide an identifiability condition based on the misalignment of local components. Roughly speaking, misalignment characterizes heterogeneity through the “minimal difference” among the subspaces spanned by the local components. Turns out that if local subspaces are slightly misaligned, our model becomes identifiable. What’s more interesting is that the estimation error gets smaller as heterogeneity gets more significant, and finding the PCs gets easier.  This result lies in sharp contrast to all distributed and federated predictive theory, as it highlights that heterogeneity can be a blessing in disguise.

Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing, Naichen Shi, Raed Kontar
Technometrics, 2023, [Link, Youtube Video, Code]

Literature in Federated Learning (FL) has addressed heterogeneity across data sources/clients by allowing each client to retain an individualized set of model parameters (e.g., weights of a neural network). Such an approach accounts for changes in the input/output relationship (also known as a concept shift) across the clients. However, a fundamental challenge remains: the covariate shift, as the marginal input distribution across clients may differ. Therefore, a holistic personalization perspective in FL must comprehensively address both a concept and covariate shift. This paper takes on this exact challenge. We first, mathematically demonstrate the shortcomings of traditional FL in the presence of a covariate shift and then propose a model based on domain adaptation that is capable of modeling both sources of heterogeneity simultaneously.

Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design, Xubo Yue, Albert Berahas, Yang Liu, Blake N. Johnson, Raed Kontar IEEE TASE, 2024, [Link, Youtube Video, Code]

Black-box optimization (or simply trial & error), is a critical yet challenging task in many real-world applications. This challenge arises from the need for extensive trial & error, especially for complex systems with a moderate or high number of design parameters.

In this paper, we introduce a collaborative framework that fast-tracks the optimal design process through collaboration, that dynamically distributes trial & error efforts across (potentially heterogeneous) clients. Our work brings the notion of “consensus” to optimal design, whereby clients reach a statistical agreement on their subsequent trial & error locations to best optimize their black-box functions. The paper specifically addresses three challenges:

  • How to sequentially distribute trial and error efforts?
  • How to collaborate in the presence of heterogeneity?
  • How to preserve the privacy of all collaborators?

The paper concludes with a real-life experiment in which collaborating chemists showcase the power of collaboration in optimizing the material formulation of a hydrogel.

Youtube Channel

UM Data Science Lab

Check out our YouTube channel for talks on our recent work

Teaching

  • IOE 691: Modern Bayesian Data Science
  • STAT/IOE 570: Design of Experiments
  • IOE 265: Probability & Statistics for Engineers
  • ME/ISD 599: Smart Manufacturing Systems

Education

  • PhD, Industrial and Systems Engineering, University of Wisconsin-Madison, 2018
  • MS, Statistics, University of Wisconsin Madison, 2017
  • BE, Civil and Environmental Engineering (Mathematics Minor), American University of Beirut, 2014

Latest News

Jan 25: My PhD student Naichen Shi has will join Northwestern as an Assistant Professor

Aug 24: Our team is a finalist in both the QSR and DM section at the upcoming INFORMS 2024 (details below)

  • Best Paper competition within the Data Mining (DM) Section at INFORMS 2024
    • Time and Location: October 20, 2024, at 10:45 AM, Regency – 704
    • Paper: Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components. Minor Revision at JMLR [Link]
  • Best Refereed Paper competition within the Quality, Statistics, and Reliability (QSR) section at INFORMS 2024
    • Time and Location: October 20, 2024, at 8:00 AM, Summit – 312
    • Paper: Multi-physics Simulation Guided Generative Diffusion Models with Applications in Fluid and Heat Dynamics. Under submission [Preprint]

Sep 24: I was just promoted to associate professor with tenure

Sep 24: My first two PhD students just started their Assistant Professor jobs

  • Xubo (Max) Yue: PhD: 2018-2023
    • Placement: Assistant Professor in the Mechanical and Industrial Engineering Department
      at Northeastern University
    • Highlights:
      • Rackham Graduate School’s 2023 ProQuest Distinguished Dissertation Award Honor-
        able Mention
      • Four best paper recognitions at IISE, INFORMS, and ASA
  • Seokhyun Chung: PhD: 2018-2023
    • Placement: Assistant Professor in the Systems Engineering Department at the University of Virginia
    • Highlights:
      • Three best paper recognitions at IISE and INFORMS

Our group is a finalist for the Best Paper Award at INFORMS 2023 in both the Data Mining (DM) and Quality, Statistics & Reliability (QSR) sections for the two papers:

  • Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
  • Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data

Our recent NIH grant was featured on the National Library of Medicine’s Youtube channel: check out the video

Our Lab received an NSF CAREER award titled: “From the Cloud to the Crowd: An Enabling Solution for the Internet of Federated Things”

Publications

1PhD student advised by me, (C) Conference Paper, * Corresponding author
Note: Let us know if you need codes for the papers below

Preprints

[52] SEE-OoD: Supervised Exploration For Enhanced Out-of-Distribution Detection
Xiaoyang Song, Wenbo Sun*, Maher Nouiehed, Raed Kontar, Judy Jin
Technometrics, Submitted
[Preprint]

[51] The Traveling Bandit: A Framework for Gaussian Process Bandits with Metric Movement costs
Qiyuan Chen1, Raed Kontar*
Technometrics, Submitted
[Preprint]

[50] Heterogeneous Matrix Factorization: When Features Differ by Dataset
Naichen Shi1, Salar Fattahi, Raed Kontar*
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Submitted
[Preprint, Code]

Best refereed paper finalist, Quality, Statistics & Reliability (QSR) section, INFORMS Annual Meeting, 2023

[49] Multi-physics Simulation Guided Generative Diffusion Models with Applications in Fluid and Heat Dynamics
Naichen Shi1, Hao Yan, Shenghan Guo, Raed Kontar*
IEEE Transactions on Automation Science and Engineering (TASE), Major Revision
[Preprint, Code]

Best refereed paper finalist, Quality, Statistics \& Reliability (QSR) section, INFORMS Annual Meeting, 2024

Accepted/Published

[48] Effect of uncertainty-aware artificial intelligence models on human reaction time and decision-making: A randomized controlled trial
Corey Lester*, Brigid Rowell, Yifan Zheng, Zoe Co, Vincent Marshall, Jin Yong Kim, Qiyuan Chen1, Raed Kontar, X. Jessie Yang
Journal of Medical Internet Research (JMIR), Major Revision
[Preprint]

[47] Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
Jiuyun Hu, Naichen Shi1, Raed Kontar, Hao Yan*
Technometrics, 2025
[Link, Code]

Best refereed paper finalist, Quality, Statistics & Reliability (QSR) section, INFORMS Annual Meeting, 2023

[46] Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes
Seokhyun Chung1, Raed Kontar*
Reliability Engineering & System Safety, 2025
[Link]

Best track paper finalist, Quality, Control & Reliability Engineering (QCRE) division, IISE Annual Conference, 2024

[45] Suicide Risk Estimation in Bipolar Disorder Using N200 and P300 Event-related Potentials and Machine Learning: A Pilot Study
Chaewon Lee*, Kathleen Gates, Jinsoo Chun, Raed Al Kontar, Masoud Kamali, Melvin McInnis, Patricia Deldin.
Journal of Affective Disorders Reports, 2025
[Link]

[44] (C) FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
Tanapol Kosolwattana, Huazheng Wang, Raed Kontar, Ying Lin*
AAAI, 2025
[Link]

[43] (C) Collaborative and Federated Black-box Optimization: A Bayesian Optimization Perspective
Raed Al Kontar*
IEEE BigData, 2024
[Link, Youtube Video]

[42] Collaborative and Distributed Bayesian Optimization via Consensus
Xubo Yue1, Albert Berahas, Yang Liu, Blake N. Johnson, Raed Kontar*
IEEE Transactions on Automation Science and Engineering (TASE), 2024
[Link, Youtube Video, Code]

[41] The Effects of Presenting AI Uncertainty Information on Pharmacists’ Trust in Automated Pill Recognition Technology: an Exploratory Study
Jin Yong Kim, Vincent D. Marshall, Brigid Rowell, Qiyuan Chen1, Yifan Zheng, John D. Lee, Raed Kontar, Corey Lester, Jessie Yang*
JMIR Human Factors, 2024
[Link]

[40] Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: A Randomized Controlled Trial
Chuan-Ching Tsai, Jin Yong Kim, Qiyuan Chen1, Brigid Rowell, X Jessie Yang, Raed Kontar, Megan Whitaker, Corey Lester*
Journal of Medical Internet Research (JMIR), 2024
[Link]

[39] Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning
Yang Liu, Xubo Yue1, Junru Zhang, Zhenghao Zhai, Ali Moammeri, Kevin J. Edgar, Albert S. Berahas, Raed Al Kontar, Blake N. Johnson*
ACS Applied Materials & Interfaces, 2024
[Link]

[38] Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components
Naichen Shi1, Salar Fattahi, Raed Kontar*
Journal of Machine Learning Research (JMLR), 2024
[Link]

Best general track paper winner, Data Mining (DM) section, INFORMS Annual Meeting, 2024

[37] Multi-agent Collaborative Bayesian Optimization via Constrained Gaussian Processes
Qiyuan Chen1, Liangkui Jiang, Hantang Qin, Raed Kontar*
Technometrics, 2024
[Link, Code]

[36] Personalized PCA: Decoupling Shared and Unique Features
Naichen Shi1, Raed Kontar*
Journal of Machine Learning Research (JMLR), 2024
[Link, Youtube Video, Code]

[35] (C) Personalized Dictionary Learning for Heterogeneous Datasets
Geyu Liang, Naichen Shi1, Raed Kontar, Salar Fattahi*
Neural Information Processing Systems (NeurIPS), 2024
[Link, Code]

[34] Personalized feature extraction for manufacturing process signature characterization and anomaly detection
Naichen Shi1, Shenghan Guo, Raed Kontar*
Journal of Manufacturing Systems, 2024
[Link, Code]

[33] Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
Xubo Yue1, Raed Kontar*
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
[Link, Youtube Video, Code]

Best student paper finalist, DM section, INFORMS Annual Meeting, 2022
Best student paper finalist, physical and engineering sciences, Joint Statistical Meetings (JSM), 2023

[32] Federated Multi-output Gaussian Processes
Seokhyun Chung1, Raed Kontar*
Technometrics, 2024
[Link, Youtube Video, Code]

Best refereed paper finalist, QSR section, INFORMS Annual Meeting, 2022

[31] Rethinking Cost Sensitive Classification in Deep Learning via Adversarial Data Augmentation
Qiyuan Chen1, Raed Kontar*, Maher Noueihed, Jessie Yang, Corey Lester
Informs Journal on Data Science (IJDS), 2024
[Link, Code]

[30] Federated Data Analytics: A Study on Linear Models
Xubo Yue1, Raed Al Kontar*, Ana Maria Estrada Gomez
IISE Transactions, 2024
[Link, Youtube video, Code]

Featured Article in the December 2023 issue of the Industrial and Systems Engineering (ISE) Magazine

[29] Intelligent Feedrate Optimization using an Uncertainty-aware Digital Twin within a Model Predictive Control Framework
Heejin Kim, Raed Kontar, Chinedum E Okwudire*
IEEE Access, 2024
[Link]

[28] Optimize to Generalize in Gaussian Processes: An Alternative Objective Based on the R’enyi Divergence
Xubo Yue1, Raed Kontar*
IISE Transactions, 2024
[Link, Code]

Best theoretical paper finalist, DM section, INFORMS Annual Meeting, 2021

[27] Designing Human-Centered AI to Prevent Medication Dispensing Errors: Focus Group Study With Pharmacists
Yifan Zheng, Brigid Rowell, Qiyuan Chen1, Jin Yong Kim, Raed Kontar, Jessie Yang, Corey A Lester*
JMIR Formative Research, 2023
[Link]

[26] Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing
Naichen Shi1, Raed Kontar*
Technometrics, 2023
[Link, Youtube Video, Code]

[25] A Continual Learning Framework for Adaptive Defect Classification and Inspection
Wenbo Sun*, Raed Kontar, Jionghua Jin, Tzyy-Shuh Chang
Journal of Quality Technology (JQT), 2023
[Link]

[24] Federated Condition Monitoring Signal Prediction With Improved Generalization
Seokhyun Chung1, Raed Kontar*
IEEE Transactions on Reliability, 2023
[Link, Code]

[23] Fed-ensemble: Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification
Naichen Shi1, Fan Lai, Raed Kontar*, Mosharaf Chowdhury
IEEE Transactions on Automation Science and Engineering (TASE), 2023
[Link, Code]

[22] SALR: Sharpness-Aware Learning Rate Scheduler for Improved Generalization
Xubo Yue1, Maher Nouiehed, Raed Kontar*
IEEE Transactions on Neural Networks and Learning Systems, 2023
[Link, Code]

[21] GIFAIR-FL: An Approach for Group and Individual Fairness in Federated Learning
Xubo Yue1, Maher Nouiehed, Raed Kontar*
Informs Journal on Data Science (IJDS), 2022
[Link, Youtube video, Code]

Best refereed paper award finalist, QSR section, INFORMS annual meeting, 2021

[20] Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen1, Lili Zheng1, Raed Kontar*, Garvesh Raskutti
Journal of Machine learning Research (JMLR), 2022
[Link, Youtube video, Code ]

[19] On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes
Moyan Li1, Raed Kontar*
SIAM/ASA Journal on Uncertainty Quantification (JUQ), 2022
[Link]

[18] Weakly Supervised Multi-output Regression via Correlated Gaussian Processes
Seokhyun Chung1, Raed Kontar*, Zhenke Wu
Informs Journal on Data Science (IJDS), 2022
[Link, Youtube video, Code]

Best track paper finalist, QCRE division, IISE Annual Conference, 2021

[17] Parameter Calibration in Wake Effect Simulation Model with Stochastic Gradient Descent and Stratified Sampling
Bingjie Liu, Xubo Yue1, Eunshin Byon*, Raed Kontar
Annals of Applied Statistics, 2022
[Link]

Best track paper finalist, Data Analytics and Information Systems (DAIS) division, IISE Annual Conference, 2021

[16] A Multi-stage Approach for Knowledge-guided Predictions with Application to Additive Manufacturing
Seokhyun Chung1, Cheng-Hao Chou, Xiaozhu Fang, Raed Kontar*, Chinedum Okwudire
IEEE Transactions on Automation Science and Engineering (TASE), 2022
[Link, Youtube Video]

[15] The Internet of Federated Things (IoFT)
Raed Kontar*, Naichen Shi1, Xubo Yue1, Seokhyun Chung1, Eunshin Byon, Mosharaf Chowdhury, Jionghua Jin, Wissam Kontar1, Neda Masoud, Maher Noueihed, Chinedum E. Okwudire, Garvesh Raskutti, Romesh Saigal, Karandeep Singh, and Zhisheng Ye
IEEE Access, 2021
[Link, Youtube video, IoFT Dataset Directory]

Featured article of the year at IEEE access, 2022

Note: This paper is a joint effort across faculty from multiple universities and expertise were we provide a vision for a new IoT paradigm that exploits edge compute resources to distribute model learning and process more of the client’s data where it is created; at the edge. The paper features a data directory for IoFT-based datasets. Real-life datasets where distributed and federated analytics can be tested are provided.

[14] Joint Models for Event Prediction from Time Series and Survival Data
Xubo Yue1, Raed Kontar*
Technometrics, 2020
[Link, Code]

Best track student paper finalist, QCRE division, IISE Annual Conference, 2019

[13] Look-ahead Planning for Renewable Energy: A Dynamic “Predict and Store” Approach
Jingxing Wang, Seokhyun Chung1, Abdullah AlShelahi, Raed Kontar*, Eunshin Byon, Romesh Saigal
Applied Energy, 2021
[Link]

[12] Performance Evaluation of a Prescription Medication Image Classification Model: An Observational Cohort
Corey A. Lester*, Jiazhao Li, Yuting Ding, Brigid Rowell, Jessie ‘Xi’ Yang, Raed Kontar
Nature: NPJ Digital Medicine, 2021
[Link]

[11] (C) Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
Hao Chen1, Lili Zheng1, Raed Kontar*, Garvesh Raskutti
Neural Information Processing Systems (NeurIPS), 2020
[Link, Youtube video, Code ]

[10] Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data
Seokhyun Chung1, Raed Kontar*
IEEE Transactions on Reliability, 2020
[Link, Code]

[9] Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach
Raed Kontar*, Garvesh Raskutti, Shiyu Zhou
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
[Link]

Best refereed paper finalist, QSR section, INFORMS Annual Meeting, 2018

[8] (C) Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
Xubo Yue1, Raed Kontar*
Artificial Intelligence and Statistics Conference (AISTATS), 2020
[Link]

Note: Notational correction published at arXiv, 2022

[7] Remaining Useful Life Prediction Based on Degradation Signals Using Monotonic B-splines with Infinite Support
Salman Jahani, Raed Kontar, Shiyu Zhou*, Dhamaraj Veeramani
IISE Transactions, 2020
[Link]

Featured article in the April 2020 issue of the ISE magazine
Honorable mention for the best paper in the 2020 IISE transactions issue on Data Science, Quality & Reliability

2019 and Earlier (at UW Madison)

[6] A Simple Approach to Multivariate Monitoring of Production Processes with Non-Gaussian Data
Qianqian Dong, Raed Kontar, Min Li*, Gang Xua, Jinwu Xua
Journal of Manufacturing Systems, 2019
[Link]

[5] Statistical Monitoring of Multiple Profiles Simultaneously Using Gaussian Processes
Salman Jahani, Raed Kontar, Dharmaraj Veeramani, Shiyu Zhou*
Quality and Reliability Engineering International, 2018
[Link]

[4] Nonparametric Modeling and Prognosis of Condition Monitoring Signals Using Multivariate Gaussian Convolution Processes
Raed Kontar, Shiyu Zhou*,  Chaitanya Sankavaram, Xinyu Du,  Yilu Zhang
Technometrics, 2018
[Link]

Best student paper winner, QSR section, INFORMS annual meeting, 2017

[3] Nonparametric-Condition-Based Remaining Useful Life Prediction Incorporating External Factors
Raed Kontar, Shiyu Zhou*, Chaitanya Sankavaram, Xinyu Du, Yilu Zhang
IEEE Transactions on Reliability, 2018
[Link]

[2] Remaining Useful Life Prediction Based on the Mixed Effects Model with Mixture Prior Distribution
Raed Kontar, Shiyu Zhou*, Chaitanya Sankavaram, Yilu Zhang, Xinyu Du
IISE Transactions, 2017
[Link]

[1] Estimation and Monitoring of Key Performance Indicators of Manufacturing Systems Using the Multi-Output Gaussian Process
Raed Kontar, Shiyu Zhou*, John Horst
International Journal of Production Research, 2017
[Link]