About

Georgios Fellouris is an Associate Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign, a position he has held since 2020. He also serves as Associate Chair of the Statistics Department and holds affiliate faculty positions at the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering at the same university. Prior to his current role, he was an Assistant Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign from 2013 to 2020 and a Postdoctoral Research Associate in the Department of Mathematics at the University of Southern California from 2010 to 2013. He earned his PhD, MPhil, and MA in Statistics from Columbia University, completing his doctoral degree in 2010. He also holds a Diploma in Applied Mathematics and an MS in Mathematical Modeling in Modern Technologies & the Economy from the National Technical University of Athens, Greece. Professor Fellouris's research interests focus on sequential analysis, including sequential hypothesis testing, quickest change detection, sequential parameter estimation, and sequential design. His work also addresses decision making under communication constraints as well as educational measurement and cognitive assessment. His scholarly contributions include developing asymptotically optimal procedures for multi-hypothesis testing and sequential multiple testing, with applications in areas such as anomaly identification and quickest change detection in multiple data streams. His research integrates probability theory, asymptotic methods, and statistical hypothesis testing to advance methodologies in sequential decision making and statistical inference.

Research topics

• Computer Science
• Data Mining
• Algorithm
• Mathematics
• Statistics
• Mathematical optimization
• Artificial Intelligence
• Applied mathematics
• Telecommunications

Selected publications

• Active Sequential Signal Detection with Asynchronous Decisions

  arXiv (Cornell University) · 2026-04-06

  articleOpen accessSenior author

  This work considers the problem of detecting signals from multiple sequentially observed data streams, where only one stream can be observed at every time instant. The goal is to detect signals as quickly as possible while controlling the global probabilities of false alarm and missed detection. In this active sampling setup, it is impossible to minimize the expected detection time simultaneously for every signal, so we formulate a novel set of performance criteria that aim to minimize the expectations of the order statistics of the detection times. A novel procedure is proposed, which incorporates an exploration mechanism to a "follow-the-leader" procedure, and is shown to optimize all the criteria asymptotically as the global error probabilities go to zero. Its finite-sample performance is compared with existing and oracle procedures in simulation studies.

  PublisherOA PDF

• Active Sequential Signal Detection with Asynchronous Decisions

  arXiv (Cornell University) · 2026-04-06

  preprintOpen accessSenior author

  This work considers the problem of detecting signals from multiple sequentially observed data streams, where only one stream can be observed at every time instant. The goal is to detect signals as quickly as possible while controlling the global probabilities of false alarm and missed detection. In this active sampling setup, it is impossible to minimize the expected detection time simultaneously for every signal, so we formulate a novel set of performance criteria that aim to minimize the expectations of the order statistics of the detection times. A novel procedure is proposed, which incorporates an exploration mechanism to a "follow-the-leader" procedure, and is shown to optimize all the criteria asymptotically as the global error probabilities go to zero. Its finite-sample performance is compared with existing and oracle procedures in simulation studies.

  PublisherDOI

• High-dimensional Quickest Change Detection in Multiple Data Streams with Adaptive Window-Based Subset Estimation

  2025-06-22

  articleSenior author

  A large-scale multichannel sequential detection problem is considered, where an event occurs at some unknown time and affects the distributions of an unknown subset of data streams, possibly at a different time each of them. The goal is to detect this change as quickly as possible, while controlling the false alarm rate. An adaptive CuSum procedure is proposed, whose number of computations at each time instant is linear in the number of streams. Its performance is analyzed in various asymptotic regimes where the number of streams, the unknown number of affected streams, and the unknown delays in the emergence of the change all go to infinity as the false alarm rate goes to zero. The proposed scheme is shown to be asymptotically optimal in sparse and moderately high-dimensional regimes, and to enjoy a superior asymptotic performance to existing procedures in non-sparse or very high-dimensional regimes. Finally, it is compared with existing schemes in the literature in a simulation study.

  PublisherDOI

• Efficient Importance Sampling for Wrong Exit Probabilities over Combinatorially Many Rare Regions

  ArXiv.org · 2025-09-18

  preprintOpen accessSenior author

  We consider importance sampling for estimating the probability that a light-tailed $d$-dimensional random walk exits through one of many disjoint rare-event regions before reaching an anticipated target. This problem arises in sequential multiple hypothesis testing, where the number of such regions may grow combinatorially and in some cases exponentially with the dimension. While mixtures over all associated exponential tilts are asymptotically efficient, they become computationally infeasible even for moderate values of $d$. We develop a method for constructing asymptotically efficient mixtures with substantially fewer components by combining optimal tilts for a small number of regions with additional proposals that control variance across a large collection of regions. The approach is applied to the estimation of three probabilities that arise in sequential multiple testing, including a multidimensional extension of Siegmund's classical exit problem, and is supported by both theoretical analysis and numerical experiments.

  PublisherOA PDFDOI

• Adaptive 3-Stage Procedures for Multi-Hypothesis Testing

  2025-06-22 · 1 citations

  articleSenior author

  The problem of testing a finite number of possibly composite hypotheses is considered, when it is required to control the probability of every type of wrong decision below a user-specified level. A general strategy is proposed for constructing a 3-stage test, in which the size of the second stage depends on the data collected in the first stage. Sufficient conditions are established for such an adaptive 3-stage test to achieve the optimal expected sample size, among all admissible sequential tests, to a first-order asymptotic approximation as the error probabilities go to zero at relatively symmetric rates. This general framework is applied to the case of general simple hypotheses, where the data are not necessarily i.i.d., as well as to the case of composite hypotheses of i.i.d. data coming from a one-parameter exponential family.

  PublisherDOI

• Worst-Case Misidentification Control in Sequential Change Diagnosis Using the Min-CuSum

  IEEE Transactions on Information Theory · 2024-08-08 · 2 citations

  articleOpen accessSenior author

  The problem of sequential change diagnosis is considered, where a sequence of independent random elements is accessed sequentially, there is an abrupt change in its distribution at some unknown time, and there are two main operational goals: to quickly detect the change, and to accurately identify upon stopping the post-change distribution among a finite set of alternatives. The focus is on the min-CuSum algorithm, which raises an alarm as soon as a CuSum statistic that corresponds to one of the post-change alternatives exceeds a certain threshold. We obtain, under certain assumptions, non-asymptotic upper bounds on its conditional probability of misidentification given that a false alarm did not occur. When, in particular, the data are generated over independent channels and the change can occur in only one of them, its worst-case—with respect to the change point—conditional probability of misidentification given that there was not a false alarm is shown to decay exponentially fast in the threshold. As a corollary, in this setup, the min-CuSum is shown to asymptotically minimize Lorden’s detection delay criterion, simultaneously for every post-change scenario, within the class of schemes that satisfy prescribed bounds on both the false alarm rate and the worst-case conditional probability of misidentification, in a regime where the latter does not go to zero faster than the former. Finally, these theoretical results are also illustrated in simulation studies.

  PublisherDOI

• Sequential change diagnosis revisited and the Adaptive Matrix CuSum

  Bernoulli · 2024-05-15 · 1 citations

  articleSenior author

  The problem of sequential change diagnosis is considered, where observations are obtained on-line, an abrupt change occurs in their distribution, and the goal is to quickly detect the change and accurately identify the post-change distribution, while controlling the false alarm rate. A finite set of alternatives is postulated for the post-change regime, but no prior information is assumed for the unknown change point. A drawback of many algorithms that have been proposed for this problem is the implicit use of pre-change data for determining the post-change distribution. This can lead to very large conditional probabilities of misidentification, given that there was no false alarm, unless the change occurs soon after monitoring begins. A novel, recursive algorithm is proposed and shown to resolve this issue without the use of additional tuning parameters and without sacrificing control of the worst-case delay in Lorden’s sense. A theoretical analysis is conducted for a general family of sequential change diagnosis procedures, which supports the proposed algorithm and revises certain state-of-the-art results. Additionally, a novel, comprehensive method is proposed for the design and evaluation of sequential change diagnosis algorithms. This method is illustrated with simulation studies, where existing procedures are compared to the proposed.

  PublisherDOI

• Change acceleration and detection

  The Annals of Statistics · 2024-06-01 · 1 citations

  articleSenior author

  A novel sequential change detection problem is proposed, in which the goal is to not only detect but also accelerate the change. Specifically, it is assumed that the sequentially collected observations are responses to treatments selected in real time. The assigned treatments determine the pre-change and post-change distributions of the responses and also influence when the change happens. The goal is to find a treatment assignment rule and a stopping rule that minimize the expected total number of observations subject to a user-specified bound on the false alarm probability. The optimal solution is obtained under a general Markovian change-point model. Moreover, an alternative procedure is proposed, whose applicability is not restricted to Markovian change-point models and whose design requires minimal computation. For a large class of change-point models, the proposed procedure is shown to achieve the optimal performance in an asymptotic sense. Finally, its performance is found in simulation studies to be comparable to the optimal, uniformly with respect to the error probability.

  PublisherDOI

• Joint sequential detection and isolation for dependent data streams

  The Annals of Statistics · 2024-10-01 · 5 citations

  articleSenior author
  PublisherDOI

• Asymptotically optimal sequential multiple testing with asynchronous decisions

  Bernoulli · 2024-10-30 · 6 citations

  articleSenior author

  The problem of simultaneously testing the marginal distributions of sequentially monitored, independent data streams is considered. The decisions for the various testing problems can be made at different times, using data from all streams, which can be monitored until all decisions have been made. Moreover, arbitrary a priori bounds are assumed on the number of signals, i.e., data streams in which the alternative hypothesis is correct. A novel sequential multiple testing procedure is proposed and it is shown to achieve the minimum expected decision time, simultaneously in every data stream and under every signal configuration, asymptotically as certain metrics of global error rates go to zero. This optimality property is established under general parametric composite hypotheses, various error metrics, and weak distributional assumptions that allow for temporal dependence. Furthermore, the limit of the factor by which the expected decision time in a data stream increases when one is limited to synchronous or decentralized procedures is evaluated. Finally, two existing sequential multiple testing procedures in the literature are compared with the proposed one in various simulation studies.

  PublisherDOI

Recent grants

• ATD: Collaborative Research: Efficient sampling for real-time detection and isolation of threats in networks

  NSF · $131k · 2017–2022

Frequent coauthors

• Venugopal V. Veeravalli

  University of Illinois Urbana-Champaign

  48 shared

• Subhonmesh Bose

  University of Illinois Urbana-Champaign

  41 shared

• N. Minh

  VinUniversity

  41 shared

• Daniel Liberzon

  University of Illinois Urbana-Champaign

  41 shared

• Ivan Dokmanić

  41 shared

• R. Srikant

  University of Illinois Urbana-Champaign

  41 shared

• Bronwyn H. Bradshaw‐Hajek

  University of South Australia

  41 shared

• Mr Peterson

  University of Illinois Urbana-Champaign

  41 shared