About

Chrysafis Vogiatzis, Ph.D., is a professor in Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign. He received his Ph.D. degree in Industrial & Systems Engineering from the University of Florida under the supervision of Dr. Panos M. Pardalos. Prior to his current position, he served as an assistant professor at the Department of Industrial & Manufacturing Engineering at North Dakota State University and the Department of Industrial & Systems Engineering at North Carolina A&T State University. He also holds a M.Sc. degree from the University of Florida and a Dipl. Eng. degree in Electrical & Computer Engineering from the Aristotle University of Thessaloniki in Greece. His research focuses on network optimization and combinatorial optimization, with applications in modern socio-technical and biological systems. A significant aspect of his work involves studying centrality metrics in biological, social, and infrastructure networks to identify groups and persons of interest. His broader research interests encompass Operations Research, including network optimization, combinatorial optimization, decomposition techniques, and evacuation and disaster management. In addition to his research, Professor Vogiatzis has taught a wide range of courses in Industrial and Systems Engineering, covering topics such as production and inventory control, supply chain management, probabilities and statistics for engineers, analysis of data, analysis of network data, integer and network optimization, and quantitative modeling.

Research topics

• Computer Science
• Mathematics education
• Computer Security
• Business
• Engineering
• Sociology
• Psychology
• Operations management
• Marketing
• Computer network
• World Wide Web
• Management science
• Industrial organization
• Medicine
• Operations research
• Knowledge management
• Risk analysis (engineering)
• Medical education
• Economics
• Engineering management
• Multimedia

Selected publications

• Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach

  ArXiv.org · 2026-05-14

  articleOpen accessSenior author

  We propose a betweenness centrality measure and algorithms for stochastic networks, where edges can fail and weights vary across realizations, making the most central node random. Our approach models the sequence of reported central nodes as an absorbing Markov chain and measures node importance by the share of pre-absorption time spent at each node. This produces a way to study centrality under uncertainty, which can then be estimated with Monte Carlo simulation. We also analyze robustness when the transition kernel is only approximately known, using row-wise perturbations to assess sensitivity and potential ranking changes. The framework further admits extensions to weighted rewards and restricted candidate sets without altering the Markov chain formulation. Experiments on Erdős-Rényi, Watts-Strogatz, and Les Misérables networks with stochastic edges show that the method identifies a small set of dominant nodes, reveals stable versus sensitive rankings under perturbations, and supports reward-based and structure-constrained variants.

  PublisherOA PDF

• Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach

  arXiv (Cornell University) · 2026-05-14

  preprintOpen accessSenior author

  We propose a betweenness centrality measure and algorithms for stochastic networks, where edges can fail and weights vary across realizations, making the most central node random. Our approach models the sequence of reported central nodes as an absorbing Markov chain and measures node importance by the share of pre-absorption time spent at each node. This produces a way to study centrality under uncertainty, which can then be estimated with Monte Carlo simulation. We also analyze robustness when the transition kernel is only approximately known, using row-wise perturbations to assess sensitivity and potential ranking changes. The framework further admits extensions to weighted rewards and restricted candidate sets without altering the Markov chain formulation. Experiments on Erdős-Rényi, Watts-Strogatz, and Les Misérables networks with stochastic edges show that the method identifies a small set of dominant nodes, reveals stable versus sensitive rankings under perturbations, and supports reward-based and structure-constrained variants.

  PublisherDOI

• Preventing overdose deaths with effective naloxone inventory distribution

  IISE Transactions on Healthcare Systems Engineering · 2026-05-11

  articleSenior author
  PublisherDOI

• Risk-Averse Stochastic User Equilibrium on Uncertain Transportation Networks

  ArXiv.org · 2026-03-01

  articleOpen access

  Extreme weather events, like flooding, disrupt urban transportation networks by reducing speeds and capacities, and by closing roadways. These hazards create regime-dependent uncertainty in link performance and travel-time distribution tails, challenging conventional traffic assignment that relies on the expectation of cost or mean excess of cost summation. This study develops a risk- and ambiguity-aware traffic assignment framework coupling stochastic supply driven by hazard impacts, endogenous route choice with choice set truncation, and tail-risk management within a tractable convex truncated stochastic user equilibrium (TSUE) formulation. Travelers' perceived costs use a normalized mean-CVaR certainty equivalent encoding tail sensitivity into two interpretable parameters ($α$ and $λ$) while preserving convexity. We propose two complementary treatments. TSUE-Stochastic Programming (TSUE-SP) optimizes a nominal risk-aware TSUE balancing average performance and adverse-tail outcomes. TSUE-Distributionally Robust Optimization (TSUE-DRO) protects against calibration error and distributional misspecification by incorporating robustness over a $1$-Wasserstein ambiguity set, and when appropriate, over structured regime-dependent sets for piecewise-stationary hazards (non-stationary distribution case). Duality yields a scenario-based second-order cone program solved via Benders cuts. On a stylized grid network representing downtown Chicago, western corridor traffic increases $67.9\%$ with TSUE-SP and $100.9\%$ with TSUE-DRO relative to a baseline not impacted by the hazard. The formulations redistribute flows without large-scale rerouting, illustrating how tail weighting and distributional ambiguity fine-tune rather than subvert equilibrium choices in hazard-prone networks.

  PublisherOA PDF

• Improving the Accessibility of Mathematical and other STEM Content in Engineering courses through Machine Learning Models

  2025-08-21

  article
  PublisherDOI

• Understanding the needs of students to make Mathematics and other STEM Contents more accessible in college Engineering courses

  2025-08-21

  article
  PublisherDOI

• Minimizing passenger disruption and airline costs in hurricane recovery: A multimodal rescheduling approach

  Journal of the Air Transport Research Society · 2025-03-08 · 1 citations

  articleOpen accessCorresponding

  Air transportation is often affected by disruptions, both in the form of disasters that are caused by human error or natural phenomena. Such disruptions primarily affect passengers, in the form of travel delays and flight cancellations. Recovering airline schedules after disruptive events is particularly challenging for airlines due to unavailability of flights and/or their reduced capacity to address the demands of the affected passengers. In this work, we propose a multimodal rescheduling approach for airline passengers whose travel is disrupted by natural phenomena, such as hurricanes and tropical storms. The objective is to identify an optimal route, which may involve a combination of air and road travel, that minimizes system-level airline costs. The multimodal approach proposed here includes a multi-commodity network flow model where different origin–destination pairs for different travelers are treated as the different commodities. We incorporate transportation network risk in the form of a family of hop/risk side constraints. We test our approach on a simulated toy network and then proceed to use publicly available data to compare different airport network structures. The results from our computational study show that certain airline passengers may experience more inconvenience in comparison to others depending on the airport network topology of a specific airline.

  PublisherDOI

• A Survey on Optimization Studies of Group Centrality Metrics

  arXiv (Cornell University) · 2024-01-10 · 2 citations

  preprintOpen accessSenior author

  Centrality metrics have become a popular concept in network science and optimization. Over the years, centrality has been used to assign importance and identify influential elements in various settings, including transportation, infrastructure, biological, and social networks, among others. That said, most of the literature has focused on nodal versions of centrality. Recently, group counterparts of centrality have started attracting scientific and practitioner interest. The identification of sets of nodes that are influential within a network is becoming increasingly more important. This is even more pronounced when these sets of nodes are required to induce a certain motif or structure. In this study, we review group centrality metrics from an operations research and optimization perspective for the first time. This is particularly interesting due to the rapid evolution and development of this area in the operations research community over the last decade. We first present a historical overview of how we have reached this point in the study of group centrality. We then discuss the different structures and motifs that appear prominently in the literature, alongside the techniques and methodologies that are popular. We finally present possible avenues and directions for future work, mainly in three areas: (i) probabilistic metrics to account for randomness along with stochastic optimization techniques; (ii) structures and relaxations that have not been yet studied; and (iii) new emerging applications that can take advantage of group centrality. Our survey offers a concise review of group centrality and its intersection with network analysis and optimization.

  PublisherOA PDFDOI

• Research Engineer Network: A Network Analysis of Graduate Student Relationships

  2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024-02-20

  articleOpen access1st authorCorresponding

  versity, leads large-scale, mixed-methods projects that seek to address disparities through complex intervention implementation and evaluation.Dr. Teixeira-Poit has three primary research streams.First, she implements and evaluates interventions to address workforce shortages and improve the capacity of the workforce.Second, she leads health services studies that examine the impact of developing systems of care and transforming practices on health care access and utilization, delivery and quality of care, and health outcomes.Third, she assesses the effect of social determinants of health on access to care and patient outcomes.She evaluates the effectiveness of interventions designed to attenuate the effect of social determinants on patient outcomes.She has 15 years of experience leading research teams; designing and implementing research and evaluation; developing protocols for surveys, interviews, and focus groups; collecting and analyzing qualitative data, and programming advanced statistical analyses of quantitative data using Stata.

  PublisherOA PDFDOI

• Accessibility Nuggets, Video Vignettes, and Other Instructor Development Approaches to Foster UDL Adoption and Inclusive Engineering Education

  2024-08-03

  articleOpen access

  Students with disabilities need accessible courses.Universal Design for Learning (UDL) has become a well-known approach to creating inclusive and accessible education.However, despite general interest around UDL among instructors, previous studies have revealed the lack of resources to adequately educate instructors on UDL, accessibility principles, and best practices.This study details multiple methodologies at the University of Illinois Urbana Champaign used to educate and inspire instructors about UDL with the goal of creating more accessible engineering courses.We implemented multiple approaches to increase interest in, and utilization of, UDL by instructors: 1) Developed a Learning Management System (LMS) based training course which gave instructors "plug-and-play" practical examples of UDL design practices; 2) Created short tips ("Accessibility Nuggets") to inspire interest and show the starting points; 3) Provided handson help for non-UDL based engineering courses to make them more accessible; 4) Surveyed students and instructors about UDL practices

  PublisherOA PDFDOI

Frequent coauthors

• Sujit Varadhan

  28 shared

• Hongye Liu

  University of Illinois Urbana-Champaign

  28 shared

• David Dalpiaz

  University of Illinois Urbana-Champaign

  28 shared

• Jennifer Amos

  University of Illinois Urbana-Champaign

  26 shared

• Deepak Moparthi

  University of Illinois Urbana-Champaign

  18 shared

• Yun Huang

  Austral University of Chile

  17 shared

• Rebecca Reck

  Harvard University

  16 shared

• Lawrence Angrave

  University of Illinois Urbana-Champaign

  12 shared

Education

• Ph.D., Industrial and Systems Engineering

  University of Florida

  2014

• M.Sc., Industrial and Systems Engineering

  University of Florida

  2012

• Dipl. Eng., Electrical and Computer Engineering

  Aristotle University of Thessaloniki

  2009

Awards & honors

• 2026 Excellence in Undergraduate Teaching (University of Ill…
• 2025 IISE OR Division Teaching Award
• 2025 IISE Modeling and Simulation Division Teaching Award
• 2023 INFORMS Case Competition Runner up for "Racial bias in…
• 2023 ASEE IL/IN Teacher of the Year