Andrew J. Schaefer

About

Andrew J. Schaefer is the Noah Harding Chair and Professor of Computational Applied Mathematics and Operations Research at Rice University. He joined the Rice faculty in 2015 after spending 15 years with the Department of Industrial Engineering at the University of Pittsburgh, where he held positions including the John A. Swanson Chair in Engineering and the William Kepler Whiteford Professor of Industrial Engineering. His research interests are in the broad area of operations research and industrial engineering, specifically focusing on mixed-integer programming, stochastic optimization, and large-scale optimization. He earned a Ph.D. in industrial and systems engineering from Georgia Tech in 2000, a master’s degree in computational and applied mathematics from Rice University in 1994, and a B.A. in the same discipline from Rice University in 1994. Throughout his career, Schaefer has received numerous awards, including the NSF CAREER Award in 2006, the Outstanding Young Industrial Engineering Education Award from the Institute of Industrial Engineers in 2007, and the Outstanding Young Engineer by the Rice Engineering Alumni Association in 2013. He has also received a Best Paper Award from IIE Transactions on Operations Engineering in 2016.

Research topics

• Computer Science
• Medicine
• Machine Learning
• Surgery
• Artificial Intelligence
• Internal medicine
• Nuclear medicine
• Mathematical optimization
• Radiology
• Medical physics

Selected publications

• MR-Guided Adaptive Radiotherapy for OAR Sparing in Head and Neck Cancers

  Cancers · 2022 · 28 citations

  MR-linac devices offer the potential for advancements in radiotherapy (RT) treatment of head and neck cancer (HNC) by using daily MR imaging performed at the time and setup of treatment delivery. This article aims to present a review of current adaptive RT (ART) methods on MR-Linac devices directed towards the sparing of organs at risk (OAR) and a view of future adaptive techniques seeking to improve the therapeutic ratio. This ratio expresses the relationship between the probability of tumor co…

  DOI

• Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification

  International Journal of Radiation Oncology*Biology*Physics · 2024-03-10 · 13 citations

  articleOpen access

  PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible. METHODS AND MATERIALS: The analysis was conducted on retrospective…

  PublisherOA PDFDOI

• Objective Selection for Cancer Treatment: An Inverse Optimization Approach

  Operations Research · 2022 · 11 citations

  Senior authorCorresponding

  The quality of radiation therapy treatment plans and the efficiency of the planning process are heavily affected by the choice of planning objectives. Although simple objectives enable efficient treatment planning, the resulting treatment quality might not be clinically acceptable; complex objectives can generate high-quality treatment, yet the planning process becomes computationally prohibitive. In “Objective Selection for Cancer Treatment: An Inverse Optimization Approach,” by integrating inv…

  DOI

• Optimized decision support for selection of transoral robotic surgery or (chemo)radiation therapy based on <scp>posttreatment</scp> swallowing toxicity

  Cancer Medicine · 2022 · 8 citations

  BACKGROUND: A primary goal in transoral robotic surgery (TORS) for oropharyngeal squamous cell cancer (OPSCC) survivors is to optimize swallowing function. However, the uncertainty in the outcomes of TORS including postoperative residual positive margin (PM) and extranodal extension (ENE), may necessitate adjuvant therapy, which may cause significant swallowing toxicity to survivors. METHODS: A secondary analysis was performed on a prospective registry data with low- to intermediate-risk human p…

  DOI

• Markov decision process design: A framework for integrating strategic and operational decisions

  Operations Research Letters · 2024-02-21 · 7 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Optimization of the Design and Operation of Surgery Delivery Systems

  NSF · $120k · 2006–2010

• Optimizing the Regional Distribution of Organ Procurement Organizations

  NSF · $318k · 2004–2008

• Collaborative Research: Physiologically Based Optimization of ICU Management

  NSF · $224k · 2016–2020

Frequent coauthors

• Mark S. Roberts

  59 shared

• Clifton D. Fuller

  The University of Texas MD Anderson Cancer Center

  25 shared

• Temitayo Ajayi

  23 shared

• Oleg A. Prokopyev

  20 shared

• Steven M. Shechter

  University of British Columbia

  18 shared

• R. Scott Braithwaite

  17 shared

• Lisa M. Maillart

  University of Pittsburgh

  16 shared

• Abdallah Mohamed

  16 shared

Labs

• Andrew J. Schaefer LabPI

Education

• PhD, Industrial and Systems Engineering

  Georgia Institute of Technology

  2000

Awards & honors

• Outstanding Young Engineer by the Rice Engineering Alumni as…
• Outstanding Young Industrial Engineering Education Award fro…
• NSF CAREER Award (2006)
• Best Paper Award, IIE Transactions on Operations Engineering…
• Outstanding Young Engineering Alumni Award, Rice University…

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