About

Christos Alexopoulos is the Associate Chair for Graduate Studies, Kiplinger Faculty Fellow, and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research interests center on applied probability, simulation analysis methodology, statistics, and optimization under uncertainty. His recent work involves the design and implementation of efficient sequential methods for estimating measures of risk and error in simulation experiments, as well as the optimization of production and inventory systems using machine learning methods. Dr. Alexopoulos has been a core contributor to simulation education in ISyE since 1988. In 1999, he co-founded the cross-disciplinary Modeling & Simulation Research & Education Center, which served as a springboard for the creation of the School of Computational Science and Engineering. He has served as the unit coordinator for the graduate programs in CSE since their inception. In addition to his instructional contributions, he has co-developed instructional games and the Sequest Software for estimating measures of statistical risk and error. His work has been recognized with awards such as the Best Paper Award at the 24th ACM/IEEE/SCS Workshop on Principles of Advanced and Distributed Simulation in 2010, the Best Paper Award in Operations Engineering and Analysis of IIE Transactions in 2010, and the INFORMS Simulation Society Outstanding Simulation Publication Award in 2007.

Research topics

• Computer Science
• Applied mathematics
• Mathematics
• Statistics
• Algorithm

Selected publications

• Estimating Confidence Intervals and Regions for Quantiles in Steady‐State Simulations

  Naval Research Logistics (NRL) · 2026-01-06

  article

  ABSTRACT We propose methods based on batching, sectioning, and generalized likelihood ratios (GLRs) for computing confidence intervals (CIs) and confidence regions (CRs) for quantiles in steady‐state simulations. Based on central limit theorems for quantile estimators, CIs and CRs can be computed from a single batch of simulated output using a GLR method to consistently estimate the unknown density function. This paper makes the following contributions: (1) We derive a GLR estimator for distribution sensitivities in the steady‐state setting and, under the geometric‐moment contraction (GMC) conditions, we establish the uniform consistency of the GLR estimators and the asymptotic validity of the respective CIs and CRs for quantiles. (2) We also establish the asymptotic validity of CIs and CRs for quantiles by batching and sectioning methods for steady‐state simulations. Numerical experiments demonstrate the validity of the aforementioned methods as the coverage rates of the CIs and CRs approach the target levels for appropriately large sample sizes. In the steady‐state setting, the sectioning and GLR methods demonstrate their respective advantages in different examples.

  PublisherDOI

• Production Planning with Markovian Production Relationships

  Production and Operations Management · 2026-04-13

  article

  We study a production planning problem with linear, nonlinear, deterministic, and/or stochastic production relationships between the production plans and actual production quantities. We start by introducing a stochastic dynamic programing formulation of the problem with a Markovian assumption on the production relationships. Under specific conditions, we establish the convexity of the optimal cost-to-go function and closed forms of optimal policies. To solve the original problem in the general case, we propose a solution framework based on sequential policy optimization and deep reinforcement learning. We discuss the theoretical properties of the framework and evaluate its numerical performance with linear and nonlinear production relationships. In the linear case, our framework performs in line with the state-of-the-art optimization-based methods with improved computational efficiency. In the nonlinear case, our framework achieves an optimality gap near 10%–20%. We also illustrate that the proposed methodology can also be extended to the problem of joint production planning and scheduling.

  PublisherDOI

• Analysis of risk factors for disease progression in oligometastatic prostate cancer patients in a developing country

  The European Physical Journal Special Topics · 2026-03-18

  article
  PublisherDOI

• A comparison of fusion policies for local detection statistics in federated monitoring

  Sequential Analysis · 2026-04-06

  article
  PublisherDOI

• Multi-period Data-driven Newsvendor Problem: a Deep Reinforcement Learning Solution Approach with Theoretical Convergence Guarantees

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Gender Representations and Language Teaching: Analyzing the Language text Book for the Second Grade of the Greek Primary School

  International Journal of English Language and Linguistics Research · 2025-02-15 · 1 citations

  articleOpen access1st authorCorresponding

  School textbooks are a basic source of knowledge, but also an important means of socializing individuals, since various messages are mainly transmitted through language, part of which are gender representations. This paper focuses on the analysis of gender representations that are presented in the language textbook taught in the second grade of primary school in Greece. For this purpose, I utilized combined tools from Content Analysis and Critical Discourse Analysis, in order to examine gender references and highlight the ideology and discourses that construct them. The results showed an attempt to quantitatively balance the number of references to men and women, which, however, is not reflected in the qualitative analysis, as men are shown to dominate the workplace and more broadly in public life, with women being underrepresented and limited to actions related to family responsibilities.

  PublisherOA PDFDOI

• A simulation-driven machine learning framework for large-scale inventory management

  Annals of Operations Research · 2025-10-06 · 1 citations

  article
  PublisherDOI

• The Link Between Serum Levels of Dehydroepiandrosterone and Alzheimer’s Disease: A Pilot Study in the Serbian Population

  Cureus · 2025-10-13

  articleOpen access

  Background and aim Alzheimer's disease (AD) is the most prevalent type of dementia and a major health concern among the elderly population. Considering the role of neurosteroids in neurodegenerative processes, this pilot study aimed to investigate the association between serum dehydroepiandrosterone sulfate (DHEA-S) and cortisol concentrations, as well as their ratio, in patients with AD compared with cognitively intact individuals. Methods An observational case-control study was conducted involving 45 patients with clinically probable AD, diagnosed according to the 2011 National Institute on Aging-Alzheimer's Association criteria, and 40 healthy control participants matched for age and sex. Fasting morning serum concentrations of DHEA-S and cortisol were measured after a 12-hour overnight fast in all participants, and the cortisol/DHEA-S ratio was calculated. Cognitive status was assessed using the Montreal Cognitive Assessment and Clinical Dementia Rating scales. Results No statistically significant difference in serum DHEA-S concentrations was found between patients with AD and controls. However, patients with AD had significantly higher cortisol levels (398.85 vs. 337.40 nmol/L; p = 0.026) and a higher cortisol/DHEA-S ratio, showing a trend toward statistical significance (p = 0.078). Among participants aged 65-75 years, the cortisol/DHEA-S ratio was significantly higher in patients with AD than in controls (p = 0.031). In the control group, males had significantly higher DHEA-S levels than females (p = 0.020), whereas no sex difference was observed in the AD group. Conclusions The findings of this pilot study suggest that elevated cortisol levels and an imbalance in the cortisol/DHEA-S ratio may contribute to AD pathophysiology. DHEA-S alone did not show a significant association with disease presence, but the observed age- and sex-related differences indicate that this neurosteroid may play a differential role in the development and progression of AD.

  PublisherOA PDFDOI

• A Fixed-Sample-Size Procedure for Estimating Steady-State Quantiles Based on Independent Replications

  2025-12-07

  article
  PublisherDOI

• Production Planning with Generalized Production Relationships

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

Recent grants

• Collaborative Research: Effective Sequential Procedures for Risk and Error Estimation in Steady-state Simulation

  NSF · $234k · 2012–2016

Frequent coauthors

• David Goldsman

  Georgia Institute of Technology

  66 shared

• James R. Wilson

  North Carolina State University

  44 shared

• Anup C. Mokashi

  Memorial Sloan Kettering Cancer Center

  14 shared

• Kemal Dinçer Dingeç

  Gebze Technical University

  10 shared

• Nilay Tanık Argon

  University of North Carolina at Chapel Hill

  8 shared

• Melike Meterelliyoz

  8 shared

• Seong‐Hee Kim

  Sungkyunkwan University

  7 shared

• Tûba Aktaran‐Kalaycı

  AT&T (United States)

  7 shared

Education

• PhD, Operations Research

  University of North Carolina System

  1988

• BS, Mathematics

  Aristotle University of Thessaloniki

  1982

Awards & honors

• Best Paper Award, 24th ACM/IEEE/SCS Workshop on Principles o…
• Best Paper Award in Operations Engineering and Analysis of I…
• INFORMS Simulation Society Outstanding Simulation Publicatio…
• Honorable Mention in IIE Transactions Focused Issue on Opera…
• Winner of Best Paper Award at 24th ACM/IEEE/SCS Workshop on…