About

Guodong Pang is a Professor of Computational Applied Mathematics and Operations Research at Rice University. He is a member of the Ken Kennedy Institute and works on applied probability, stochastic control, stochastic networks, queueing systems, and epidemic models. His research involves applications in service systems, healthcare, energy systems, telecommunications, and epidemiology. Additionally, he has broad interests in financial engineering, econometrics, simulation, and numerical methods. Dr. Pang earned his Ph.D. in Operations Research from the Department of Industrial Engineering and Operations Research at Columbia University in 2010.

Research topics

• Sociology
• Mathematics
• Statistics
• Mathematical analysis
• Applied mathematics
• Statistical physics
• Medicine
• Demography

Selected publications

• Ergodic risk-sensitive admission control problem for a Markovian multi-server queueing system with abandonment

  Queueing Systems · 2026-03-29

  articleOpen accessSenior authorCorresponding

  We consider the ergodic risk-sensitive admission control problem for a Markovian multi-server queueing system with abandonment, where costs are incurred for server idleness, customer abandonment, and rejecting incoming arrivals. We first derive the Bellman optimality equation for this problem and show that a threshold policy—one that rejects incoming arrivals whenever the system-size exceeds a threshold—is optimal among all admissible control policies. We then propose a policy iteration algorithm to identify the optimal threshold, where we prove, under certain conditions on the problem’s parameters, that the algorithm will terminate at the optimal threshold level. We also characterize the effect of risk sensitivity on the optimal threshold, proving that this threshold monotonically decreases with respect to the sensitivity parameter and converges to the average cost optimal threshold from below as the sensitivity parameter tends to zero.

  PublisherOA PDFDOI

• Single-Server Queues with State-Dependent Hawkes Arrivals

  Mathematics of Operations Research · 2026-04-02

  articleSenior author

  We study single-server queues with Hawkes arrivals whose intensity process depends on the queue length through the self-exciting function, and with independent and identically distributed general service times, under the first-come first-served discipline. We prove the functional law of large numbers and functional central limit theorems (FCLT) for the joint processes of the arrivals, queue-length, and workload processes, in the heavy traffic regime. The fluid limit is given by a set of nonlinear integral equations such that the fluid queue or workload has a reflection at zero. We analyze the transient and equilibrium behaviors of the fluid limit, in particular, identifying the equilibrium points for the queue or workload fluid model. We assume that the fluid limit is at an equilibrium point in order to establish the FCLT for the joint diffusion-scaled arrival, queue-length, and workload processes in the critically loaded regime. When the equilibrium point of the queue is at zero, the limit for the joint processes satisfies a stochastic differential equation such that the queue-length or workload limit has a reflection at zero. In particular, the queue-length or workload process is equivalent in distribution to a generalized (possibly nonlinear drift) Ornstein-Uhlenbeck (OU) diffusion with reflection at zero. When the equilibrium point is positive, the limit for the joint processes is a diffusion process without reflection; in particular, the queue-length or workload process is a generalized OU diffusion. Because of the interacting effects between the Hawkes arrivals and queue-length or workload processes, the standard approaches to prove functional limit theorems for Hawkes processes and for single-server queues cannot be applied directly. We develop a new method to prove the functional limit theorems for the joint Hawkes and queueing dynamics, by using a localization argument and exploiting convergence of martingales and stochastic integrals. Funding: G. Pang was partly supported by the National Science Foundation [Grants DMS 2216765 and CMMI 2452829]. Supplemental Material: The online companion is available at https://doi.org/10.1287/moor.2024.0855 .

  PublisherDOI

• Growing open Markovian Jackson networks: Fluid limit and infinite-dimensional Skorokhod problem

  ArXiv.org · 2026-05-16

  articleOpen access

  We study growing open Jackson networks where each station is a single-server queue that follows the first-come first-served discipline with Poisson arrivals and exponentially distributed service times, characterized by node-specific rates. In applying a fluid scaling to the queue-length process, we show that under certain conditions the queueing system can be approximated by an infinite-dimensional fluid limit with a kernel function in place of the transition matrix. This limiting process can be characterized by an infinite-dimensional Skorokhod problem, for which we develop a new theory by considering a broader class of reflection operators and general infinite-dimensional processes. We establish existence and uniqueness of a solution along with Lipschitz continuity provided the reflecting operator has a spectral radius less than 1.By introducing an intermediate process in which the compensated Poisson components are removed, and then lifting this to an infinite-dimensional process, we exploit the new Lipschitz property of the infinite-dimensional Skorokhod mapping to prove convergence of the intermediate process. We then prove the necessary estimates for the difference between the original and intermediate processes by using martingale properties. Finally, we consider the empirical measure of the queueing processes, for which we show convergence to the measure associated with the path of the infinite-dimensional fluid limit, extending to the convergence of specific performance-related functionals.

  PublisherOA PDF

• Growing open Markovian Jackson networks: Fluid limit and infinite-dimensional Skorokhod problem

  arXiv (Cornell University) · 2026-05-16

  preprintOpen access

  We study growing open Jackson networks where each station is a single-server queue that follows the first-come first-served discipline with Poisson arrivals and exponentially distributed service times, characterized by node-specific rates. In applying a fluid scaling to the queue-length process, we show that under certain conditions the queueing system can be approximated by an infinite-dimensional fluid limit with a kernel function in place of the transition matrix. This limiting process can be characterized by an infinite-dimensional Skorokhod problem, for which we develop a new theory by considering a broader class of reflection operators and general infinite-dimensional processes. We establish existence and uniqueness of a solution along with Lipschitz continuity provided the reflecting operator has a spectral radius less than 1.By introducing an intermediate process in which the compensated Poisson components are removed, and then lifting this to an infinite-dimensional process, we exploit the new Lipschitz property of the infinite-dimensional Skorokhod mapping to prove convergence of the intermediate process. We then prove the necessary estimates for the difference between the original and intermediate processes by using martingale properties. Finally, we consider the empirical measure of the queueing processes, for which we show convergence to the measure associated with the path of the infinite-dimensional fluid limit, extending to the convergence of specific performance-related functionals.

  PublisherDOI

• Spatially dense stochastic epidemic models with infection-age dependent infectivity

  Stochastic Analysis and Applications · 2026-01-02

  article1st authorCorresponding
  PublisherDOI

• BESTish: A Diffusion-Approximation Framework for Inferring Selection and Mutation in Clonal Hematopoiesis

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-29

  articleOpen access

  Abstract Clonal hematopoiesis (CH) arises when hematopoietic stem cells (HSCs) gain a fitness advantage from somatic mutations and expand, resulting in an increase in variant allele frequency (VAF) over time. To analyze CH trajectories, we develop a state-dependent stochastic model of wild-type and mutant HSCs, in which an environmental parameter α ∈ [0, 1] regulates death rates and interpolates between homeostatic (Moran-like, α = 1) and growth-facilitating ( α < 1) regimes. Using functional law of large numbers and central limit theorems, we derive explicit mean-field dynamics and a Gaussian–Markov approximation for VAF fluctuations. We show that the mean VAF trajectory has an explicit logistic form determined by selective advantage, while environmental effects affect only the variance and autocovariance structure. Building on these results, we introduce BESTish (Bayesian estimate for selection incorporating scaling-limit to detect mutant heterogeneity), a novel, efficient and accurate Bayesian inference method that can be applied to analyze both cohort-level and longitudinal VAF datasets. BESTish implements the closed-form finite-dimensional distributions that we derive to estimate mutation fitness, mutation rate, and environmental strength for individual CH drivers. When applied to existing CH datasets, BESTish produces consistent mutation fitness inferences across different studies, and estimates CH driver mutation rates in agreement with independent experimental studies. Furthermore, BESTish reveals patient-specific heterogeneity in the selective behavior of recurrent mutations, and identifies variants whose dynamics are compatible with non-homeostatic, growth-facilitating environments. BESTish provides a unified and mechanistic framework for quantifying CH evolution, with potential applications for other biological systems where clonal expansions can be measured.

  PublisherDOI

• Sample-path moderate deviation principle for GI/GI/1+GI queues in the nearly critically loaded regime

  Queueing Systems · 2025-03-13 · 1 citations

  articleSenior author
  PublisherDOI

• On the Ergodic Properties and Invariant Measure of a Two-Dimensional Reflected Ornstein–Uhlenbeck Process

  Stochastic Systems · 2025-11-24

  articleOpen accessSenior author

  We consider a two-dimensional reflected Ornstein–Uhlenbeck (ROU) process that arises as the diffusion approximation for a parallel server network with a randomly split Hawkes arrival process (or a multivariate Hawkes arrival process) in heavy traffic. We study the ergodic properties of the process, including the positive recurrence and rate of convergence in total variation distance and in Wasserstein distance. We also provide a numerical scheme based on a Monte Carlo method to approximate the invariant measure of the process. Funding: G. Pang is partly supported by the National Science Foundation [Grants DMS 2216765 and CMMI 2452829].

  PublisherDOI

• Ergodic risk sensitive control of Markovian multiclass many-server queues with abandonment

  The Annals of Applied Probability · 2025-06-01 · 1 citations

  articleSenior author

  We study the optimal scheduling problem for a Markovian multiclass queueing network with abandonment in the Halfin–Whitt regime, under the long run average (ergodic) risk sensitive cost criterion. The objective is to prove asymptotic optimality for the optimal control arising from the corresponding ergodic risk sensitive control (ERSC) problem for the limiting diffusion. In particular, we show that the optimal ERSC value associated with the diffusion-scaled queueing process converges to that of the limiting diffusion in the asymptotic regime. The challenge that ERSC poses is that one cannot express the ERSC cost as an expectation over the mean empirical measure associated with the queueing process, unlike in the usual case of a long run average (ergodic) cost. We develop a novel approach by exploiting the variational representations of the limiting diffusion and the Poisson-driven queueing dynamics, which both involve certain auxiliary controls. The ERSC costs for both the diffusion-scaled queueing process and the limiting diffusion can be represented as the integrals of an extended running cost over a mean empirical measure associated with the corresponding extended processes using these auxiliary controls. For the lower bound proof, we exploit the connections of the ERSC problem for the limiting diffusion with a two-person zero-sum stochastic differential game. We also make use of the mean empirical measures associated with the extended limiting diffusion and diffusion-scaled processes with the auxiliary controls. One major technical challenge in both lower and upper bound proofs, is to establish the tightness of the aforementioned mean empirical measures for the extended processes. We identify nearly optimal controls appropriately in both cases so that the existing ergodicity properties of the limiting diffusion and diffusion-scaled queueing processes can be used.

  PublisherDOI

• Ergodic Risk Sensitive Control of Diffusions under a General Structural Hypothesis

  ArXiv.org · 2025-11-02

  preprintOpen accessSenior author

  We study the infinite-horizon average (ergodic) risk sensitive control problem for diffusion processes under a general structural hypothesis: there is a partition of state space into two subsets, where the controlled diffusion process satisfies a Foster-Lyapunov type drift condition in one subset, under any stationary Markov control, while the near-monotonicity condition is satisfied with the running cost function being inf-compact in its complement. Under these conditions, we completely characterize the optimal stationary Markov controls. To prove this, we consider an inf-compact perturbation to the running cost over the entire space such that the resulting ergodic risk sensitive control problem is well-defined and then use the corresponding existing results. The heart of the analysis lies in exploiting the variational formula of exponential functionals of Brownian motion and applying it to the objective exponential cost function of the controlled diffusion. This representation facilitates us to view the risk sensitive cost for any stationary Markov control as the optimal value of a control problem of an extended diffusion involving a new auxiliary control where the optimal criterion is to maximize the associated long-run average cost criterion that is a difference of the original running cost and an extra term that is quadratic in the auxiliary control. The main difficulty in using this approach lies in the fact that tightness of mean empirical measures of the extended diffusion is not a priori implied by the analogous tightness property of the original diffusion. We overcome this by establishing a priori estimates for the extended diffusion associated with the nearly optimal auxiliary controls.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Physiologically Based Optimization of ICU Management

  NSF · $150k · 2016–2020

• Collaborative Research: Infinite horizon risk-sensitive control of diffusions with applications in stochastic networks

  NSF · $215k · 2022–2025

• Large-Scale Fork-Join Networks with Synchronization Constraints

  NSF · $250k · 2015–2018

• Collaborative Research: Ergodic Control of Stochastic Differential Equations Driven By a Class of Pure-Jump Levy Processes, and Applications to Stochastic Networks

  NSF · $212k · 2017–2022

• Collaborative Research: Infinite horizon risk-sensitive control of diffusions with applications in stochastic networks

  NSF · $221k · 2021–2022

Frequent coauthors

• Étienne Pardoux

  Institut Polytechnique de Bordeaux

  41 shared

• Ari Arapostathis

  The University of Texas at Austin

  32 shared

• Ward Whitt

  11 shared

• Hongyuan Lu

  Pennsylvania State University

  11 shared

• Hassan Hmedi

  The University of Texas at Austin

  11 shared

• Raphaël Forien

  10 shared

• Yi Zheng

  Pennsylvania State University

  9 shared

• Nikola Sandrić

  9 shared