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. I also enjoy occasional theoretical endeavors beyond application.
My methodological focus is on distributed and federated data analytics where entities, such as IoT devices, hospitals, and autonomous vehicles, collaboratively extract knowledge and distribute model learning efforts while keeping their personal data stored locally.
My application focus is on the “Internet of Federated Things (IoFT)”. IoFT was recently enabled with the huge increase in computing power at the edge. This change opens a new paradigm of distributed and federated data analytics where one exploits local compute resources to process more of the users’ data where it’s created. Check out further details, a data directory and a talk on IoFT here.
Outside of work, I enjoy soccer and mountain hikes.
Youtube Channel
Check out our youtube channel for talks on our recent work on federated analytics
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
* I am looking to hire 2 Ph.D. students. If interested, apply to IOE and let me know via email.
Latest News
- PhD students Xubo Yue and Seokhyun Chung are on the job market. Both will be presenting in the Quality, Statistics & Reliability (QSR) best paper and Data mining (DM) best student paper at INFORMS annual meeting 2022. Details for their talks below
- Xubo Yue and Raed Al Kontar: Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling. Session SD01, Data Mining Paper Competition II
- Seokhyun CHung and Raed Al Kontar:F ederated Multi-output Gaussian Process. Session MB57, QSR Best Refereed Paper Competition
- Our Paper, led by Ph.D. student Xubo Yue “Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling” is the best paper finalist in the Data mining section of INFORMS 2022.
- Our Paper, led by Ph.D. student Seokhyun Chung Federated Multi-output Gaussian Processes” is the best paper finalist in the Quality, Statistics & Reliability section of INFORMS 2022.
- Our Lab just received the CAREER Award from the national science foundation (NSF) on IoFT. The award is titled: From the Cloud to the Crowd: An Enabling Solution for the Internet of Federated Things.
- Check out our new youtube channel: UM Data Science Lab. Talks on various topics can be found there!
- Our recent NIH grant was featured on the National Library of Medicine’s Youtube Channel: Check out the Video
Group
- Xubo (Max) Yue: maxyxb@umich.edu
- Seokhyun Chung: seokhc@umich.edu
- Naichen Shi: naichens@umich.edu
- Qiyuan Chen: cqiyuan@umich.edu
IoFT
- A critical change is happening in today’s Internet of Things (IoT). The computational power at the edge device is steadily increasing. AI chips are rapidly infiltrating the market. Mobile phones’ processing power is becoming comparable to laptops available for everyday use. Tesla’s autopilot system has 150 million times more computing power than Apollo 11, and small local computers such as Raspberry Pis have become commonplace in manufacturing systems. This change opens a new paradigm of data analytics within IoT. More specifically, with the availability of some computing resources at each client, clients can execute small computations locally, instead of sharing all raw data to a central cloud, and then only share the minimum information needed to collaboratively extract knowledge and build smart analytics while keeping their personal data stored locally. This paradigm shift sets forth many intrinsic advantages, including privacy, cost-effectiveness, diversity, fairness, and reduced computation and latency, among many others.
- The Internet of Federated Things (IoFT) paper is a joint effort across faculty from multiple universities and expertise. We provide a vision for IoFT along with a systematic overview of current efforts toward realizing this vision.
- The paper features a data directory for IoFT-based datasets. Real-life datasets where distributed and federated analytics can be tested are provided.
- A comprehensive talk on IoFT can be found on YouTube Video
Publications
1PhD Student
*Note: We are now building our Github repository and Youtube channel. As such, some videos and codes are pending.
Personalized PCA: Decoupling Shared and Unique Features
Naichen Shi1, Raed Al Kontar
[Preprint, Video pending, Code]
Federated Data Analytics: A Study on Linear Models
Xubo Yue1, Raed Al Kontar, Ana Maria Estrada Gomez
IISE Transactions
[Preprint, Youtube Video, Code Pending]
Rethinking Cost Sensitive Classification in Deep Learning via Adversarial Data Augmentation
Qiyuan Chen1, Raed Al Kontar, Maher Noueihed, Jessie Yang, Corey Lester
[Preprint, Code Pending]
Federated Multi-output Gaussian Processes
Seokhyun Chung1, Raed Al Kontar
[Preprint Pending, Video pending, Code Pending]
GIFAIR-FL: An Approach for Group and Individual Fairness in Federated Learning
Xubo Yue1, Maher Nouiehed, Raed Kontar
Informs Journal on Data Science
[Preprint, Youtube Video, Code Pending]
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
[Preprint, Video Pending, Code Pending]
Finalist for INFORMS 2022 Data Mining Best Paper Competition Award
Fed-ensemble: Ensemble Models in Federated Learning for Improved Generalization and Uncertainty Quantification
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
IEEE Transactions on Neural Networks and Learning Systems
[Preprint, Code Pending]
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 Continual Learning Framework for Adaptive Defect Classification and Inspection
Wenbo Sun, Raed Kontar, Judy Jin, Tzyy-Shuh Chang
[Preprint]
Federated Condition Monitoring Signal Prediction with Improved Generalization
Seokhyun Chung1, Raed Kontar
[Preprint Pending]
Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D Printing
Naichen Shi1, Raed Al Kontar
Technometrics
[Preprint Pending, Video pending, Code]
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
[Link, YouTube video, IoFT Dataset Directory]
Weakly Supervised Multi-output Regression via Correlated Gaussian Processes
Seokhyun Chung1, Raed Kontar, Zhenke Wu
Informs Journal on Data Science
[Link, Code Pending, Video]
Best student paper finalist, Quality Control and Reliability Engineering (QCRE), IISE annual conference, 2020
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
Xubo Yue1, Raed Kontar
AISTATS
[Link, Code Pending]
A Multi-stage Approach for Knowledge-guided Predictions with Application to Additive Manufacturing
Seokhyun Chung1, Cheng-Hao Chou1, Xiaozhu Fang1, Raed Kontar, Chinedum Okwudire
IEEE Transactions on Automation Science and Engineering
[Link, Code Pending, Video Pending]
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen1, Lili Zheng1, Raed Kontar, Garvesh Raskutti
Journal of Machine learning Research
[Preprint, Github Code, Youtube video]
On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes
Moyan Li1, Raed Kontar
SIAM Journal on Uncertainty Quantification
[Preprint]
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
[Link]
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
[Link, Github Code, Youtube video]
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
[Link]
Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data
Seokhyun Chung1, Raed Kontar
IEEE Transactions on Reliability
[Link]
Joint Models for Event Prediction from Time Series and Survival Data
Xubo Yue1, Raed Kontar
Technometrics
[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
Digital Medicine
[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
[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
[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
[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]