Raed Al Kontar, Ph.D

Raed Al Kontar

I lead a data science lab in the industrial & operations engineering (IOE) department at the University of Michigan. I am also an affiliate with both the Michigan Institutes for Data science (MIDAS) and Computational Discovery and Engineering (MICDE).  

My main research interest is data science using probabilistic models. I aim to understand the foundations of such models in extracting interpretable knowledge and generalizing to new data. I am currently working on Bayesian deep learning, rethinking kernels methods specifically Gaussian processes, Bayesian optimization and approximate inference than can help generalization.

I focus on both the algorithmic side of probabilistic models and their applications specifically in Internet of Things (IoT) enabled systems. I envision that most (if not all) engineering systems will eventually become connected systems in the future. Therefore, my work has been applied to various real-time connected systems including smart manufacturing, teleservice systems, personalized patient monitoring and online pricing. Check my Applications page for further details.

You can find recent research updates on my twitter account @raedkontar 

Teaching

  • STAT/IOE 570: Experimental Design
  • IOE 265: Probability and Statistics

Education

  • Ph.D., Industrial and Systems Engineering, University of Wisconsin-Madison, Sept. 2018.
  • M.S., Statistics, University of Wisconsin-Madison, Feb. 2017.
  • B.E., Civil and Environmental Engineering, American University of Beirut, May 2014.

News

Wonder whether to use exact or approximate inference for GPs? check out the Renyi Gaussian Process which unifies both inference methods and provides improved generalization power.  Paper:The Renyi Gaussian Process: Towards Improved Generalization” [Link]

New Paper by Seokhyun Chung tackles Weak Supervision: “Weakly-supervised Multi-output Regression via Correlated Gaussian Processes”, 2019.

Xubo Yue recent paper: “Joint Models for Event Prediction from Time Series and Survival Data”, has been selected as a Best Student Paper Award Finalist at IISE 2019. 

Publications

1Student is advised by me
*Corresponding Author

2019

The Renyi Gaussian Process: Towards Improved Generalization
Xubo Yue1, Raed Kontar*
arXiv pre-print, 2019
[Link]

On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes
Moyan Li1, Raed Kontar*
2019

Weakly-supervised Multi-output Regression via Correlated Gaussian Processes
Seokhyun Chung1, Raed Kontar*
arXiv pre-print, 2019
[Link]

Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data
Seokhyun Chung1, Raed Kontar*
arXiv pre-print, 2019
[Link, Supplementary/Code]

Look-ahead Planning for Renewable Energy: A Dynamic “Predict and Store” Approach
Jingxing Wang, Seokhyun Chung1, Abdullah AlShelahi, Raed Kontar*, Eunshin Byon, Romesh Saigal
2019

Joint Models for Event Prediction from Time Series and Survival Data
Xubo Yue1, Raed Kontar*
arXiv pre-print, 2019
[Link, Supplementary/Code]
Best student paper finalist, QCRE, IISE annual conference

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, Minor Revision, 2019
[Link]
Best Paper Award Finalist in Quality, Statistics, and Reliability (QSR) Section of INFORMS

Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
Xubo Yue1, Raed Kontar*
AISTATS, 2019
[Link]

Remaining Useful Life Prediction Based on Degradation Signals Using Monotonic B-splines with Infinite Support
Salman Jahani, Raed Kontar, Shiyu Zhou
IISE Transactions, 2019
[Link]
Featured article from IISE Transactions

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
[Link]

2018, 17, 16

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

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, Supplementary/proofs]
Best Student Paper Award Winner in QSR Section of INFORMS, 2017

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]

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]
Most Innovative Approach in the 6Th ISYE Research Symposium, UW Madison, 2017

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, 2016
[Link]