Stochastic Networks Conference 2026
Learn the latest in stochastic networks at this prestigious event sponsored by Chicago Booth.
Held biennially, this five-day event carries on the tradition of Stochastic Networks conferences initiated in 1987. The conference brings together mathematicians and applied researchers that share an interest in stochastic network models. Explore past conferences here. All participants are welcome to present a poster and should indicate their desire to do so when registering.
Dates: June 15–19, 2026
Location: Gleacher Center
Lodging: Book early! We have secured a limited number of discounted rooms at The Intercontinental Chicago Magnificent Mile and the Sheraton Grand Chicago Riverwalk. Once those rooms have been sold, we encourage out-of-town attendees to choose from the many other hotels available in the downtown Chicago area. Use the following links to book in one of our discounted blocks.
The Intercontinental Chicago Magnificent Mile
The Sheraton Grand Chicago Riverwalk (Please call if you would like to book dates outside of June 14-19.)
Registration: Registration is open from now until May 15, 2026, with late registration fees going into effect March 1, 2026. Your registration gives you access to all talks, poster sessions, lunches, and the banquet session. Registered attendees will have the option to pay for one additional guest to attend the banquet dinner. We hope to see you all in-person, but for those who cannot make it to Chicago, there is a view-only virtual option. Registration is non-refundable. Please see below for this year’s rates.
Now Through February 28
| Student | $50 |
| Non-Student | $150 |
| Student Virtual | $25 |
| Non-Student Virtual | $75 |
| Guest Banquet | $50 |
March 1 and After
| Student | $75 |
| Non-Student | $200 |
| Student Virtual | $50 |
| Non-Student Virtual | $100 |
| Guest Banquet | $65 |
Scientific Program Committee:
- Mor Armony
- Francois Baccelli
- Mor Harchol-Balter
- Jim Dai
- Michel Mandjes
- Kavita Ramanan
- Rhonda Righter
- Rajesh Sundaresan
- Peter Taylor
- Neil Walton
- Ruth Williams
- Jiheng Zhang
For questions, contact the local organizing committee: Amy R. Ward (Chair), René Caldentey, John Birge, or Baris Ata.
We gratefully acknowledge outside funding from the Applied Probability Trust (see award program here). We are also very happy to be co-sponsored by IMS.
Speakers
Speaker: Opher Baron, Professor, University of Toronto
Title: Machine Learning, Casual Queueing, and SimLQ for Data Driven Simulation
Abstract: The objective of this talk is to expose researchers to the vast possibilities of using modern machinery and data for implementing effective management analytics for queueing processes. Such process are ubiquitous in modern economies, e.g., customers waiting to service, inventory waiting for processing/transportation, payments and invoices waiting to be generated/cleared, computing tasks waiting for resources.
I will discuss recent developments in queueing analysis based on several papers and our startup. We will first define management analytics along descriptive, predictive, comparative, i.e., comparing performance indicators under different interventions, and prescriptive analytics dimensions. We then shortly discuss ML solution for a G/G/1 based upon [1] and its extension to G(t)/G/1 based on [2].
Our main focus would be on structural causal queueing models (SCQM), based upon [3]. In this paper we suggest a data-driven representation of system building blocks to create a non-queueing simulator without prior knowledge of the system. We show that this approach is effective in comparative analytics, which, for simplicity, we demonstrate on analyzing expected waits for an M/M/1 with speed-ups.
The SCQM first requires to successfully refine the parent sets of queueing variables from data using, which we do using an off-the-shelf algorithm (even under a moderate sample size). We then use machine learning to estimate the causal structure in this queue, e.g., the Lindley's Recursion and use the G-computation to derive inference results of counterfactual interventions. We compare the performance of estimates obtained by a traditional closed form Queueing Theoretical (QT) analysis (that uses data driven estimates for the primitives of the queue) with SCQM based estimators. We find that the errors of the SCQM that assumes no knowledge of the system's dynamic and its features and these of the QT (which requires this knowledge) are comparable.
Our results suggest that SCQM would be effective for practical setting- where even experts QT analysis cannot provide closed-form results. We will finish with a demo of SiMLQ, see WWW.SiMLQ.COM. SiMLQ software uses Machine Learning to automate the visualization, Simulation, and optimization of Queueing processes. SiMLQ automatically constructs data-driven simulation models from event-log data collected by common information systems and enables users to improve processes resource management, increase efficiency, reduce cost, and manage risks. SiMLQ- from data to action!
References
- O Baron, D Krass, A Senderovich, E Sherzer, Supervised ML for Solving the GI/GI/1 Queue, INFORMS Journal on Computing 36 (3), 766-786 DOI: 10.1287/ijoc.2022.0263 Read Article
- Sherzer E., Baron O., Krass D. Reshef, H., (2025) Approximating G(t)/GI/1 queues with deep learning, European Journal of Operations Research, Volume 322, Issue 3, 1 May 2025, Pages 889-90) Read Article
- Baron, O., Krass, D., van der Laan, M., Senderovich, A,. Xu Z. Queueing Causal Models: Comparative Analytics in Service Systems. Forthcoming M&SOM) Read Article
Speaker: Jose Blanchet, Professor, Stanford University
Title: TBD
Abstract: TBD
Speaker: Anton Braverman, Associate Professor, Northwestern University
Title: On the Accuracy of Diffusion Approximations for Queueing Models with General Primitives
Abstract: Despite their widespread use, the accuracy of diffusion approximations for queueing systems with general primitives remains poorly understood. This talk explores this gap. A unifying message emerges: diffusion error decomposes naturally into interior and boundary terms. Interior terms are strikingly universal—they can be controlled using only low-order moments of the system primitives. Boundary terms, by contrast, are fundamentally more delicate: while crude bounds are relatively easy to obtain, achieving sharp (order-optimal) bounds requires deeper, model-specific insight.
The analysis hinges on extending the generator approach of Stein’s method beyond continuous-time Markov chains to piecewise-deterministic Markov processes (PDMPs). In this setting, the infinitesimal generator alone no longer suffices to characterize the stationary distribution; instead, we work with the basic adjoint relationship (BAR), which provides the appropriate stationary balance equation for PDMPs.
Speaker: Xinyun Chen, Associate Professor, The Chinese University of Hong Kong
Title: TBD
Abstract: TBD
Speaker: Celine Comte, Research Fellow, National Center for Scientific Research
Title: TBD
Abstract: TBD
Speaker: Bruce Hajek, Endowed Chair, University of Illinois at Urbana-Champaign
Title: TBD
Abstract: TBD
Speaker: Eva Locherbach
Title:
Abstract:
Speaker: Sarath Yasodharan, Assistant Professor, IIT Bombay
Title: A Sanov-type Theorem for Marked Sparse Random Graph and its Applications
Abstract:
We prove a Sanov-type large deviation principle for the component empirical measure of certain families of sparse random graphs whose vertices are marked with i.i.d. random variables. Specifically, we show that the rate function can be expressed in a fairly tractable form involving suitable relative entropies.
We illustrate two applications of this result:
- We quantify probabilities of rare events in stochastic networks on sparse random graphs.
- We characterize the annealed free energy density of a broad class of probabilistic graphical models.
Joint work with I-Hsun Chen and Kavita Ramanan.
