Raed Al Kontar, Ph.D

Raed Al Kontar

Publications | 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.

My methodological focus is on probabilistic modeling. I aim to develop models that excel in generalizing to unseen data. I am currently working on uncertainty quantification for federated learning, real-time distributed predictive analytics, and Bayesian methods in deep learning.

My application focus is on data analytics for Internet of Things (IoT) enabled systems. In particular, my focus is on the “Internet of Federated Things (IoFT)”. IoFT is a new paradigm for IoT whereby different entities collaboratively extract knowledge and build smart analytics while keeping their personal data stored locally. IoFT sets forth many intrinsic advantages including privacy, cost-effectiveness, diversity, fairness, and reduced computation among many others. I envision that IoFT will soon infiltrate all industries that are to benefit from knowledge sharing, data analytics, and decision making. Check our IoFT paper and corresponding data repository.

Outside of work, I enjoy soccer and mountain hikes.

Teaching

  • IOE 691: Modern Bayesian Data Science
  • 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

  • In our new paper “The Internet of Federated Things (IoFT)” we provide a vision for IoFT along with a systematic overview on current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and survey FL data-driven approaches, opportunities and challenges. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts.

  • The paper features a data directory for IoFT based datasets. Both real-life and simulated datasets are given. 

Publications

1PhD Student

Under revision

GIFAIR-FL: An Approach for Group and Individual Fairness in Federated Learning
Xubo Yue1, Maher Nouiehed, Raed Kontar
[Arxiv Preprint]
Best Paper Award Finalist, Quality, Statistics, and Reliability (QSR) Section, INFORMS 2021

Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
Xubo Yue1, Raed Al Kontar
[Arxiv Preprint]

Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
Naichen Shi1, Fan Lai1, Raed Al Kontar, Mosharaf Chowdhury
[Arxiv Preprint]

SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
Xubo Yue1, Maher Nouiehed, Raed Kontar
[Arxiv Preprint]

Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen1, Lili Zheng1, Raed Kontar, Garvesh Raskutti
[Arxiv Preprint]

An Alternative Gaussian Process Objective Based on the R’enyi Divergence
Xubo Yue1, Raed Kontar
[Preprint]
Best student paper finalist, Data Mining Section, INFORMS, 2020

A Multi-stage Approach for Knowledge-guided Predictions with Application to Additive Manufacturing
Seokhyun Chung1, Cheng-Hao Chou1, Xiaozhu Fang1, Raed Kontar, Chinedum Okwudire
[Preprint]

Personalized Federated Learning via Domain Adaptation
Naichen Shi1, Raed Al Kontar
[Available upon request]

A Continual Learning Framework for Adaptive Defect Classification and Inspection
Wenbo Sun, Raed Kontar, Judy Jin, Tzyy-Shuh Chang
[Available upon request]

Federated Predictive Analytics for Condition Monitoring Signals
Seokhyun Chung1, Raed Kontar
[Available upon request]

On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes
Moyan Li1, Raed Kontar
[Arxiv Preprint]

Published

The Internet of Federated Things (IoFT)
Raed Kontar, Naichen Shi1, Xubo Yue1, Seokhyun Chung1, Eunshin Byon, Mosharaf Chowdhury, Judy Jin, Wissam Kontar1, Neda Masoud, Maher Noueihed, Chinedum E. Okwudire, Garvesh Raskutti, Romesh Saigal, Karandeep Singh, and Zhisheng Ye
[IEEE Access + Arxiv]

Weakly Supervised Multi-output Regression via Correlated Gaussian Processes
Seokhyun Chung1, Raed Kontar, Zhenke Wu
[Minor Revision, Informs Journal on Data Science]
Best student paper finalist, Quality Control and Reliability Engineering (QCRE), IISE annual conference, 2020

Parameter Calibration in wake effect simulation model with Stochastic Gradient Descent and stratified sampling
Bingjie Liu1, Xubo Yue1, Eunshin Byon, Raed Kontar
Annals of Applied Statistics
Best Paper Award finalist, Data Analytics & Information Systems Division (DAIS), IISE annual conference, 2020

Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes
Hao Chen1, Lili Zheng1, Raed Kontar, Garvesh Raskutti
NeurIPS, 2020
[Link, Github Code, Youtube video]

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

Look-ahead Planning for Renewable Energy: A Dynamic “Predict and Store” Approach
Jingxing Wang1, Seokhyun Chung1, Abdullah AlShelahi1, Raed Kontar, Eunshin Byon, Romesh Saigal
Applied Energy, 2020
[Link]

Joint Models for Event Prediction from Time Series and Survival Data
Xubo Yue1, Raed Kontar
Technometrics, 2020
[Link]
Best student paper finalist, Quality Control and Reliability Engineering (QCRE), IISE annual conference 2019

Performance Evaluation of a Prescription Medication Image Classification Model: An Observational Cohort
Corey Lester, Jiazhao Li, Yuting Ding, Brigid Rowell, Jessi Yang, Raed Kontar
Accepted at npj Digital Medicine, 2020

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]

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, 2020
[Link]
Best Paper Award Finalist, Quality, Statistics, and Reliability (QSR) Section, INFORMS 2019

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, IISE Transactions 2019

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]

2018, 17, 16 (At UW Madison)

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]
Best Student Paper Award Winner, Quality, Statistics, and Reliability (QSR) Section, 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]

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]