Machine Learning in Operation Research
Dr. Adriana Gabor and Dr. Yingqian Zhang
- In recent years, many machine learning techniques have been developed, that are capable of capturing properties and patterns of a set of data. In this project, we investigate how these techniques can be used to lead to more realistic optimization models or to speed up optimization procedures for specific Operations Research problems.
Stochastic models for logistic problems
Dr. Adriana Gabor
- Due to rapid technological developments, global competition and increased customer expectations, many companies experience changes in the way they source, deliver and sale their projects. The optimization of their operations and supply chain is very complex. In this project we are interested in analyzing stochastic models appearing in these supply chains, as well as designing new algorithms to deal with the new complexity. Examples of problems studied are the optimization of inventories of retailers that sell their products both online and offline (omni channel retailers), designing algorithms for dual sourcing problems, as well as including customer oriented performance measures in inventory models.
Discrete optimization models in Logistics and Transportation
Dr. Adriana Gabor and Dr. Marko Mladenovic
- The goal of this project is to design algorithms for specific optimization problems in Logistics and Transportation. Examples of such problems are facility location models, optimal parking allocation, resource allocation problems.
Modeling and Inverse Problems in Geophysics
Dr. Mohammad Al-Khaleel (Co-Pi) and Dr. Mohamed Kamel Riahi(Co-Pi)
- In collaboration with our colleagues in the Geophysics department, this project aims to develop new subsurface-imaging techniques capable of dealing with the anisotropic viscoelastic rocks and producing high-resolution images of carbonate reservoirs.
Modeling and Inverse Problems Toward Electronic Circuit Bio-Signature
Dr. Mohammad Al-Khaleel /Mohamed Kamel Riahi
- One of the greatest challenges in today’s world is to develop new knowledge and technologies to improve the health of people. The present project is dedicated towards the development of technologies that will enable scientists to characterize different biological objects based on their electrical properties and introduce a new and robust algorithm for this classification based on solving inverse problems for a generalized mathematical representation of electronic circuits.
Modeling and Inverse Problems Toward Separation of Cancer Cells
Dr. Mohammad Al-Khaleel (Co-Pi)
- Cancer is a leading cause of death worldwide and the death toll by cancer is feared to increase by 2030. In order to combat the deadly disease, early diagnosis is essential. In collaboration with our colleagues from engineering, this project aims to propose a novel hybrid microfluidic Lab-on-a-Chip platform for early detection, isolation and characterization of circulating tumor cells from blood.