Current Projects

Project: Graph Neural Networks

Advisor(s): Akwum Onwunta 

Student(s): Aditi Sathe, Thaksheel Alleck

Student Prerequisites: 3rd or 4th year; coursework in ISE, Math, Finance/ Economics, or CSE preferred; interested students are required to have basic programming experience in either MATLAB, python, or R; deterministic optimization background (such as ISE 240) strongly preferred.

Description: Medical image datasets can have a prohibitively large number of images representing patients with different health conditions and various disease severity. When dealing with raw unlabeled image datasets, the large number of samples often makes it hard for experts and non-experts to understand the variety of images present in a dataset.

This project aims to develop efficient community detection algorithms based on nonnegative matrix factorization (NMF). NMF is an unsupervised machine learning technique for dimensionality reduction that can reveal hidden patterns and important relationships in the data without requiring labels. Furthermore, it exhibits good mathematical interpretability and natural applicability to detecting overlapping communities. In particular, the algorithms developed in the project will be used to identify and analyze the similarities of communities in unlabeled medical image datasets. Analyzing the similarities may be used to infer the severity and recommend appropriate treatment of the disease.

Project: Using Discrete Event Simulation in Industrial Production – What Does Research Have to Contribute to Current Challenges?

Advisor(s): Rafael Paredes

Student(s): Caroline Arnold

Student Prerequisites: 3rd or 4th year; coursework in work systems and/or simulation preferred; interest in modern manufacturing operations.

Description: This project aims to map and analyze the current state of the Manifold Machining and Assembly value streams and develop a baseline model using discrete simulation model (DES). By employing DES, we will gain insights into the system lead times, wait times , and identify critical bottlenecks. The simulation model, developed using ARENA (TM) software, will serve as the foundation of future “What if” scenarios , which aim to uncover potential opportunities to shorten lead times such as optimizing planning algorithms, adjusting capacity and rerouting jobs. At this phase, the project entails a thorough literature review to understand state of the art use of predictive and simulation modeling to support optimal decision making in manufacturing pathways.

Project : Sports Analytics

Advisor(s): Luis Nunes Vicente

Student(s): Ben Erickson, Max Jamie

Student Prerequisites: probability and statistics, Python, interest in sports.

Description: In this project we will try to build models for analyzing various topics of interest in sports, including underdog achievement, randomness, and various characteristics.

Project: Developing a User-Friendly Hospital Call Scheduling Tool 

Advisor(s): Frank Curtis, Karmel Shehadeh 

Student(s): Connor McDowell

Student Prerequisites: 3rd, 4th, or 5th year; Python programming experience necessary; background in optimization (ISE 230 or ISE 240) helpful but not necessary.

Description: The goal of this on-going project is to develop a user-friendly tool for optimizing the call schedule for a set of residents in a large academic hospital. This is a complex scheduling problem and suboptimal rotation scheduling can lead to burnout, unfair distribution of workload, and uneven coverage of needed medical areas. The tool should be understandable and usable by chief residents who are responsible for constructing the call schedules.  It is expected that real data from a large academic hospital can be obtained. This phase of the project is aiming to improve the user-facing tool.

Project: Quantum Computing for Optimization 

Advisor(s): Mohammadhossein Mohammadisiahroudi, Tamas Terlaky     

Student(s): Kate Slatovets, Jason Roberts

Student Prerequisites: Basic knowledge of Python. Familiarity with linear algebra is preferable.

Description: Quantum computing has attracted significant interest in many fields like optimization because it potentially can solve classes of hard problems faster than conventional supercomputers. Several researchers proposed quantum computing methods, especially one approach is using quantum linear algebra to speed up Interior Point Method which is one of the most powerful algorithms in optimization. In QCOL Lab, we are developing such hybrid quantum-classical algorithms to solve optimization problem.

Goals:

• Learning about linear and conic optimization problem and interior point method, which is the most
  popular algorithm to solve such problem.
• Learning about some quantum algorithms such as Quantum Linear System Algorithms (QLSAs)
  which can solve linear systems faster than classical counterparts, and Quantum Interior Point Methods
  (QIPMs).
• Coding Quantum Algorithm using IBM QISKIT package and do some experiment to evaluate the
  performance of quantum algorithm on quantum simulator and real quantum computers of IBM.

Project: Treatment Planning for Proton Therapy

Advisor(s): Mohammadhossein Mohammadisiahroudi, Tamas Terlaky        

Student(s): Aderet Barak

Student Prerequisites: Basic knowledge of Python. Familiarity with linear algebra is preferable.

Description: Radiation therapy (RT) has been well established as an essential tool to treat cancers. In radiotherapy, radiation treatment planning is the process in which a team consisting of radiation oncologists, radiation therapist, and medical physicists plan the appropriate external beam intensity and other factors to get the best result. In radiation treatment planning, the problem of finding appropriate machine configurations to get maximal coverage of tumor and limited damage to health organs is an important optimization problem. In this project, we try different formulations of radiotherapy optimization model for proton therapy and evaluate their performance.
Goals:

• Learning about optimization models for radiation therapy planing.
• Coding and solving optimization models with optimization solvers.