Current Projects

Applied Systems Engineering Track

Project:     Nonnegative Matrix Factorization: Computational Algorithms and Applications

Advisor(s):     Akwum Onwunta 

Student(s): Hannah Li

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.

In the era of data science, the extraction of meaningful features in datasets is a crucial challenge. To do so, a fundamental class of unsupervised linear dimensionality reduction methods is low-rank matrix factorizations (LRMF).  Nonnegative matrix factorization (NMF) is an LRMF for analyzing  high-dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data.  It has gained application in hyperspectral imaging and audio source separation.  Other applications of NMF include extracting parts of faces from sets of facial images, identifying topics in a collection of documents, learning hidden Markov models,  etc.  This project aims at developing efficient  NMF algorithms for analyzing medical images and detecting communities in large networks.

Mathematics and Statistics in ISE Track

Project : Is soccer so popular because it is the most random ball sport?    

Advisor(s):         Luis Nunes Vicente

Student(s): Thaksheel Alleck, Roman Mitchell, Ori Remen

Student Prerequisites: interest in soccer; 2nd, 3rd, or 4th year; coursework in engineering, math, with particular emphasis on probability and statistics.

Description: In this project we will try to explain the popularity of soccer through a study of randomness in ball sports. A hypothesis to be validated is that popularity, when expressed by the likelihood of weaker teams beating stronger ones, is highly correlated with chance. We will derive a model of randomness for ball sports and validate it with historical data.

Software & Computing in ISE Track

Project:     Developing a User-Friendly Hospital Call Scheduling Tool 

Advisor(s):     Frank Curtis, Karmel Shehadeh 

Student(s): Josie Charles

Student Prerequisites: 3rd, 4th, or 5th year; programming experience necessary; coursework in ISE (ISE 240 and ISE 230), Math, or CSE preferred.

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.

Project:     FlexSim Healthcare Simulation Competition 

Advisor(s):     Ana Alexandrescu

Student(s): Evan Chen, Matthew Hayden

Student Prerequisites: ISE 305 or equivalent

Description: This project aligns with the FlexSim Healthcare 2023 Simulation Competition. Students will be given the competition problem and we will work to solve it. Top three solutions are invited to present at the Annual Society for Health Systems Conference in February 2024.

Project:     Quantum Computing for Optimization 

Advisor(s): Mohammadhossein Mohammadisiahroudi, Tamas Terlaky     

Student(s): Kate Saltovets

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.


  • 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
  • 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): Layan Suleiman, Ying Wu

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.

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