About

Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University and a professor of Operations at the Kellogg School of Management. She is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications. Dr. Smilowitz has been instrumental in promoting the use of operations research within the humanitarian and nonprofit sectors through various organizations including the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering. She is the Editor-in-Chief of Transportation Science and a Fellow of the INFORMS society.

Research topics

• Computer Science
• Political Science
• Artificial Intelligence
• Psychology
• Sociology
• Public relations
• Machine Learning
• Engineering
• Geography
• Cartography
• Public administration
• Transport engineering
• Computer network
• Marketing
• Knowledge management
• Business
• Social psychology
• Mathematics
• Mathematical optimization
• Operations research

Selected publications

• Sharing the Schedule: Nonprofit Staffing for Volunteers and Employees

  Springer series in supply chain management · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Community-Engaged School District Design: A Stream-Based Approach

  Operations Research · 2025-01-23 · 2 citations

  article

  School district design decisions are a crucial part of public education systems, and operations research has played an important role in informing such decisions since the landmark United States Supreme Court decision on school desegregation in 1954. Design processes today place significant importance on comprehensive community engagement to create equitable and effective enrollment policies reflective of community needs and values. In “Community-Engaged School District Design: A Stream-Based Approach,” Ozel, Smilowitz, and Goldstein revisit the school district design problem with a focus on codesigning with community partners. The authors introduce a new compact formulation that makes multiple interrelated assignment decisions simultaneously with composite decision variables, called “streams.” This formulation is computationally efficient and easily reconfigurable for evolving problem specifications that are endemic to community codesign. These features were essential in the district redesign process in an Illinois public school district, described in the paper, allowing the community to iteratively develop proposals to address inequities in access to education and improve the student assignment process.

  PublisherDOI

• Context-rich data sets for school operations models and methods

  Socio-Economic Planning Sciences · 2025-12-08

  articleSenior author
  PublisherDOI

• Dried Blood Spots to Assess Cardiovascular‐Kidney‐Metabolic Health

  Journal of the American Heart Association · 2025-03-13 · 8 citations

  reviewOpen access

  Dried blood spot sampling offers a scalable strategy to close diagnostic gaps and improve global surveillance for cardiovascular-kidney-metabolic syndrome. However, assay performance and the extent of validity vary widely between biomarkers used in cardiovascular-kidney-metabolic health assessment under different settings and have not been well described. To fill this gap, we conducted a systematic search of the literature and a narrative synthesis through April 2024 and included reports with laboratory or field validation measuring biomarkers that can be used in cardiovascular-kidney-metabolic health assessment. We categorized assays into categories based on laboratory validation: excellent performance (r>0.95 with gold standard methods and coefficients of variation <5%), very good performance (r>0.90 and coefficients of variation <10%), reasonable performance (r>0.80 and coefficients of variation <15%), and poor performance (r<0.80 or coefficients of variation >15%). The extent of validation was determined by the total number of field validation studies with strong agreement. Hemoglobin A1c has strong laboratory and field validation and should be considered for expansion into clinical testing in low-resource settings. Traditional lipid biomarkers showed poor performance in field validation studies, but apoB (apolipoprotein B), creatinine, cystatin C, and NT-proBNP (N-terminal prohormone of brain natriuretic peptide) showed promising initial laboratory validation results and deserve greater attention in field validation studies. High-sensitivity C-reactive protein has strong laboratory and field validation but has limited clinical utility. Dried blood spot assays have been developed for biomarkers that offer mechanistic insights including inflammatory and vascular injury markers, fatty acids, malondialdehyde, asymmetric dimethylarginine, trimethylamine N-oxide, carnitines, and omics.

  PublisherDOI

• Measuring sampling plan utility in post-marketing surveillance of medical products

  Journal of Quality Technology · 2025-10-20

  article
  PublisherDOI

• Editorial Statement

  Transportation Science · 2025-01-01

  article1st authorCorresponding
  PublisherDOI

• Editorial Statement

  Transportation Science · 2025-11-01

  article1st authorCorresponding
  PublisherDOI

• In Memoriam: Hani Sobhi Mahmassani (1956–2025)

  Transportation Science · 2025-10-16

  article1st authorCorresponding
  PublisherDOI

• Optimal policies for nutrition administration to very low birth weight infants

  Decision Sciences · 2024-05-08 · 1 citations

  articleSenior author

  Abstract Very low birth weight (VLBW) infants (birth weight 1500 grams) are at risk of postnatal growth restriction. Understanding how nutrition is associated with growth and how these associations vary based on infant characteristics and comorbidities is important to reduce postnatal growth restriction. We propose a three‐step analytical framework: (i) We use unsupervised Clustering techniques to identify subgroups within a cohort of VLBW infants based on infant characteristics, diagnoses, and treatments. (ii) For each cluster, we use Multilevel Modeling to explore the associations between calorie or protein intake and growth velocity (GV) for varying time windows. (iii) We build Mixed‐Integer Programming Models to achieve simple rule‐based policies that physicians can use to classify infants into one of the identified subgroups. We use electronic health records from VLBW infants at Lurie Children's Hospital in Chicago, IL, born between 2011 and 2014. We find that clustering separates infants into two clusters, with Cluster 1 having smaller infants with more comorbidities than Cluster 2. Initial clustering on only sex and birth weight provides results similar to clustering on later‐life diagnoses and treatments. Multilevel models with Clustering provide better model fit than models without clustering. For Cluster 1, there is a significant association between GV and protein but not calories. For Cluster 2, both protein and calories are individually associated with growth. We develop accurate and sparse scoring systems to help clinicians identify infants at higher risk of growth restriction and consider nutrition regimens accordingly.

  PublisherDOI

• Analyzing Country-Level Vaccination Rates and Determinants of Practical Capacity to Administer COVID-19 Vaccines

  arXiv (Cornell University) · 2024-12-30

  preprintOpen accessSenior author

  The COVID-19 vaccine development, manufacturing, transportation, and administration proved an extreme logistics operation of global magnitude. Global vaccination levels, however, remain a key concern in preventing the emergence of new strains and minimizing the impact of the pandemic's disruption of daily life. In this paper, country-level vaccination rates are analyzed through a queuing framework to extract service rates that represent the practical capacity of a country to administer vaccines. These rates are further characterized through regression and interpretable machine learning methods with country-level demographic, governmental, and socio-economic variates. Model results show that participation in multi-governmental collaborations such as COVAX may improve the ability to vaccinate. Similarly, improved transportation and accessibility variates such as roads per area for low-income countries and rail lines per area for high-income countries can improve rates. It was also found that for low-income countries specifically, improvements in basic and health infrastructure (as measured through spending on healthcare, number of doctors and hospital beds per 100k, population percent with access to electricity, life expectancy, and vehicles per 1000 people) resulted in higher vaccination rates. Of the high-income countries, those with larger 65-plus populations struggled to vaccinate at high rates, indicating potential accessibility issues for the elderly. This study finds that improving basic and health infrastructure, focusing on accessibility in the last mile, particularly for the elderly, and fostering global partnerships can improve logistical operations of such a scale. Such structural impediments and inequities in global health care must be addressed in preparation for future global public health crises.

  PublisherOA PDFDOI

Recent grants

• Exploiting Network Structure in Routing Problems: Applications to School Bus Routing

  NSF · $619k · 2017–2022

• GOALI: Improving Medical Preparedness, Public Safety and Security at Mass Events

  NSF · $355k · 2014–2018

• Advancing Dynamic Relief Response: Integration of New Data Streams and Routing Models

  NSF · $260k · 2013–2018

• INSPIRE Track 1: Primary School Organizations as Open Systems: Strategic External Relationship Development to Promote Student Engagement in STEM Topics

  NSF · $715k · 2013–2018

• PFI:AIR - TT: SAFE (Situational Awareness for Events): A Data Visualization System

  NSF · $215k · 2016–2020

Frequent coauthors

• Carlos F. Daganzo

  University of California, Berkeley

  15 shared

• Samantha Keppler

  12 shared

• Sunil Chopra

  Northwestern University

  10 shared

• George Chiampas

  Northwestern University

  9 shared

• Seyed M. R. Iravani

  Northwestern University

  9 shared

• Mehmet Başdere

  Northwestern University

  9 shared

• Hani S. Mahmassani

  Northwestern University

  9 shared

• Peter Francis

  8 shared

Awards & honors

• INFORMS Fellow, Class of 2022