About

Jean-Francois Chamberland is an Associate Dean for Faculty Success and a Professor in the Department of Electrical & Computer Engineering at Texas A&M University. He holds the Albers Family Endowed Faculty Fellowship and is an affiliated faculty member in Multidisciplinary Engineering. His educational background includes a Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign obtained in 2004, an M.S. from Cornell University in 2000, and a B.Eng. from McGill University in 1998. His research interests focus on probability theory, statistical methods, and their applications to control and communication system excitation.

Research topics

• Computer Science
• Theoretical computer science
• Computer network
• Algorithm
• Data Mining
• Political Science
• Pedagogy
• Telecommunications
• Mathematics
• Psychology
• Virology
• Statistics
• Social psychology
• Medicine

Selected publications

• Complex Approximate Message Passing with Non-separable Denoising

  arXiv (Cornell University) · 2026-04-22

  articleOpen access

  Approximate Message Passing (AMP) is a general framework for iterative algorithms, originally developed for compressed sensing and later extended to a wide range of high-dimensional inference problems. Although recent work has advanced matrix AMP, complex AMP, and AMP for non-separable functions independently, a unified state evolution theory for complex AMP with non-separable denoisers has been lacking. This article fills that gap by establishing state evolution in the setting of complex, non-separable denoising functions. The proposed approach constructs an augmented real-valued system that lifts the problem to a higher-dimensional space, then recovers the complex domain through a many-to-one canonical transformation. Under this construction, the Onsager correction naturally involves Wirtinger derivatives, and the resulting state evolution reduces to scalar complex recursions despite the non-separable structure of the denoisers. The framework extends to the matrix-valued setting, accommodating multiple feature vectors simultaneously. This generalization enables AMP to exploit joint structural constraints, such as simultaneous group and element sparsity, in complex-valued recovery problems. The complex sparse group least absolute shrinkage and selection operator (LASSO) serves as a key instantiation, motivated by preamble detection in Orthogonal Time-Frequency Space (OTFS)-based unsourced random access. Numerical experiments confirm that state evolution accurately predicts performance and show that complex non-separable denoising can produce significant gains over separable and real-valued alternatives.

  PublisherOA PDF

• Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels

  ArXiv.org · 2026-05-03

  articleOpen access

  Learning, prediction, and compression are intimately connected: a model that accurately predicts the next symbol in a sequence can be coupled with a source coder to compress that sequence near its information-theoretic limit. When tokenized characters arriving at a fixed reading pace are encoded into variable-length codewords and streamed over a fixed-rate channel, a queue forms whose per-token delay depends on the mean and variance of the bit lengths and on the coder's algorithmic latency. This paper investigates the compression--delay tradeoff that arises when a causal language model serves as the sequential predictor within a predict-then-code architecture for real-time text transmission. Several coding schemes are compared: Shannon (ideal), Huffman, arithmetic coding, rANS at various block sizes, and gzip. The analysis separates algorithmic delay, inherent to the coder, from computational delay, which shrinks as hardware improves. Huffman is the practical choice for over-provisioned channels, with zero algorithmic delay and modest compression overhead. Arithmetic coding achieves near-optimal compression at the cost of decodability delay. Findings are validated across two scales: GPT-2 (124M) and Llama~3.2 (3B), a twenty-five-fold parameter range. This scaling yields an approximately 38\% reduction in bits per character, effectively over-provisioning the channel and thereby changing which coder is optimal.

  PublisherOA PDF

• Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages

  arXiv (Cornell University) · 2026-03-13

  preprintOpen accessSenior author

  Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence-level likelihoods. Existing approaches therefore rely on surrogate likelihoods or heuristic approximations, which can introduce bias and obscure the sequential structure of denoising. We formulate diffusion-based sequence generation as a finite-horizon Markov decision process over the denoising trajectory and derive an exact, unbiased policy gradient that decomposes over denoising steps and is expressed in terms of intermediate advantages, without requiring explicit evaluation of the sequence likelihood. To obtain a practical and compute-efficient estimator, we (i) select denoising steps for policy updates via an entropy-guided approximation bound, and (ii) estimate intermediate advantages using a one-step denoising reward naturally provided by the diffusion model, avoiding costly multi-step rollouts. Experiments on coding and logical reasoning benchmarks demonstrate state-of-the-art results, with strong competitive performance on mathematical reasoning, outperforming existing RL post-training approaches for DLMs. Code is available at https://github.com/vishnutez/egspo-dllm-rl.

  PublisherDOI

• Bipartite matching under communication constraints

  arXiv (Cornell University) · 2026-04-12

  articleOpen access

  In modern data center networks, thousands of hosts contend for shared link capacity; the scale of these systems makes centralized scheduling impractical. This article models such scheduling as a bipartite matching problem under communication constraints: senders express interest in forming connections, and receivers respond using only locally available information. A class of single-round probabilistic matching algorithms is proposed, built on two key ideas: degree-biased sampling, in which senders use receiver degrees to inform their random selection, and random thinning, in which senders report only a random subset of their connections. Analytical performance guarantees are established for random graph models. In sparse regimes, degree-biased sampling yields a higher expected matching size than prior communication-constrained algorithms; in denser settings, a counterintuitive phenomenon emerges where deliberately restricting available connections through thinning increases the expected number of matches. Combining thinning to degree two with greedy selection produces an algorithm that requires no parameter tuning and, in packet-level simulations with production traffic traces, significantly extends the network stability region. Although motivated by data center network scheduling, the underlying framework of bipartite matching under local information constraints is portable to other resource allocation settings.

  PublisherOA PDF

• Bipartite matching under communication constraints

  arXiv (Cornell University) · 2026-04-12

  preprintOpen access

  In modern data center networks, thousands of hosts contend for shared link capacity; the scale of these systems makes centralized scheduling impractical. This article models such scheduling as a bipartite matching problem under communication constraints: senders express interest in forming connections, and receivers respond using only locally available information. A class of single-round probabilistic matching algorithms is proposed, built on two key ideas: degree-biased sampling, in which senders use receiver degrees to inform their random selection, and random thinning, in which senders report only a random subset of their connections. Analytical performance guarantees are established for random graph models. In sparse regimes, degree-biased sampling yields a higher expected matching size than prior communication-constrained algorithms; in denser settings, a counterintuitive phenomenon emerges where deliberately restricting available connections through thinning increases the expected number of matches. Combining thinning to degree two with greedy selection produces an algorithm that requires no parameter tuning and, in packet-level simulations with production traffic traces, significantly extends the network stability region. Although motivated by data center network scheduling, the underlying framework of bipartite matching under local information constraints is portable to other resource allocation settings.

  PublisherDOI

• Approximate Message Passing for Multi-Preamble Detection in OTFS Random Access

  2026-04-21

  articleSenior author
  PublisherDOI

• Complex Approximate Message Passing with Non-separable Denoising

  arXiv (Cornell University) · 2026-04-22

  preprintOpen access

  Approximate Message Passing (AMP) is a general framework for iterative algorithms, originally developed for compressed sensing and later extended to a wide range of high-dimensional inference problems. Although recent work has advanced matrix AMP, complex AMP, and AMP for non-separable functions independently, a unified state evolution theory for complex AMP with non-separable denoisers has been lacking. This article fills that gap by establishing state evolution in the setting of complex, non-separable denoising functions. The proposed approach constructs an augmented real-valued system that lifts the problem to a higher-dimensional space, then recovers the complex domain through a many-to-one canonical transformation. Under this construction, the Onsager correction naturally involves Wirtinger derivatives, and the resulting state evolution reduces to scalar complex recursions despite the non-separable structure of the denoisers. The framework extends to the matrix-valued setting, accommodating multiple feature vectors simultaneously. This generalization enables AMP to exploit joint structural constraints, such as simultaneous group and element sparsity, in complex-valued recovery problems. The complex sparse group least absolute shrinkage and selection operator (LASSO) serves as a key instantiation, motivated by preamble detection in Orthogonal Time-Frequency Space (OTFS)-based unsourced random access. Numerical experiments confirm that state evolution accurately predicts performance and show that complex non-separable denoising can produce significant gains over separable and real-valued alternatives.

  PublisherDOI

• Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages

  ArXiv.org · 2026-03-13

  articleOpen accessSenior author

  Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging due to intractable sequence-level likelihoods. Existing approaches therefore rely on surrogate likelihoods or heuristic approximations, which can introduce bias and obscure the sequential structure of denoising. We formulate diffusion-based sequence generation as a finite-horizon Markov decision process over the denoising trajectory and derive an exact, unbiased policy gradient that decomposes over denoising steps and is expressed in terms of intermediate advantages, without requiring explicit evaluation of the sequence likelihood. To obtain a practical and compute-efficient estimator, we (i) select denoising steps for policy updates via an entropy-guided approximation bound, and (ii) estimate intermediate advantages using a one-step denoising reward naturally provided by the diffusion model, avoiding costly multi-step rollouts. Experiments on coding and logical reasoning benchmarks demonstrate state-of-the-art results, with strong competitive performance on mathematical reasoning, outperforming existing RL post-training approaches for DLMs. Code is available at https://github.com/vishnutez/egspo-dllm-rl.

  PublisherOA PDF

• Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels

  arXiv (Cornell University) · 2026-05-03

  preprintOpen access

  Learning, prediction, and compression are intimately connected: a model that accurately predicts the next symbol in a sequence can be coupled with a source coder to compress that sequence near its information-theoretic limit. When tokenized characters arriving at a fixed reading pace are encoded into variable-length codewords and streamed over a fixed-rate channel, a queue forms whose per-token delay depends on the mean and variance of the bit lengths and on the coder's algorithmic latency. This paper investigates the compression--delay tradeoff that arises when a causal language model serves as the sequential predictor within a predict-then-code architecture for real-time text transmission. Several coding schemes are compared: Shannon (ideal), Huffman, arithmetic coding, rANS at various block sizes, and gzip. The analysis separates algorithmic delay, inherent to the coder, from computational delay, which shrinks as hardware improves. Huffman is the practical choice for over-provisioned channels, with zero algorithmic delay and modest compression overhead. Arithmetic coding achieves near-optimal compression at the cost of decodability delay. Findings are validated across two scales: GPT-2 (124M) and Llama~3.2 (3B), a twenty-five-fold parameter range. This scaling yields an approximately 38\% reduction in bits per character, effectively over-provisioning the channel and thereby changing which coder is optimal.

  PublisherDOI

• Reed--Muller Codes Achieve the Symmetric Capacity on Finite-State Channels

  ArXiv.org · 2026-04-16

  articleOpen access

  We study reliable communication over finite-state channels (FSCs) using Reed--Muller (RM) codes. Building on recent symmetry-based analyses for memoryless channels, we show that a sequence of binary RM codes (with some random scrambling) can achieve the symmetric capacity (or uniform-input information rate) of a binary-input indecomposable FSC. Our approach has three components. First, we establish a capacity-via-symmetry theorem for doubly-transitive group codes on discrete memoryless channels (DMCs) with non-binary inputs, under some symmetry and puncturing conditions. Then, we reduce a binary-input FSC to an almost memoryless non-binary channel by grouping adjacent input bits into blocks and interleaving non-binary codes onto the channel. Finally, we show that the interleaved non-binary codes can be constructed from a single binary RM code.

  PublisherOA PDF

Recent grants

• Collaborative Research: CIF: Small: Low-Complexity Algorithms for Unsourced Multiple Access and Compressed Sensing in Large Dimensions

  NSF · $325k · 2021–2025

• CAREER: Information-Aware Wireless Sensor Networks

  NSF · $400k · 2008–2013

• Fundamental Limits in Delay-Constrained Wireless Communication

  NSF · $350k · 2008–2012

Frequent coauthors

• Karen Hawkins

  Xi'an Jiaotong University

  736 shared

• Prasad Narayana

  Rutgers, The State University of New Jersey

  736 shared

• Donna Hourican

  Middle East Technical University

  736 shared

• Jeffrey Cichocki

  Institute of Electrical and Electronics Engineers

  736 shared

• Radu Bălan

  736 shared

• Gregory W. Wornell

  736 shared

• Ioannis Kontoyiannis

  736 shared

• Peter Tuohy

  University of Memphis

  736 shared

Education

• Ph.D., Electrical and Computer Engineering

  University of Illinois at Urbana-Champaign

  2004

• M.S., School of Electrical and Computer Engineering

  Cornell University

  2000

• B.Eng., Electrical and Computer Engineering

  McGill University

  1998

Awards & honors

• Albers Family Endowed Faculty Fellow