About

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University College of Engineering, having joined the faculty in August 2023. His research focuses on artificial intelligence and statistical learning for smart manufacturing, with particular attention to data analytics, AI, and operations research. Yue's work includes developing federated learning techniques to protect privacy and promote fairness in advanced genomic research, enabling large-scale, privacy-preserving analysis of biological data across multiple institutions without data sharing. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor, earned in 2023. Yue is actively involved in professional organizations such as the Institute of Industrial and Systems Engineers (IISE), the Institute for Operations Research and Management Science (INFORMS), and the American Statistical Association (ASA). His contributions include research on federated Gaussian processes, group and individual fairness in federated learning, and federated data analytics, among others. Notably, he has received recognition for his work, including a $1 million NSF grant for a project on protecting privacy and promoting fairness in genomic research.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Computer Security
• Algorithm
• Machine Learning
• Data Mining
• Statistics
• Data science
• Mathematical optimization
• Physics

Selected publications

• Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models

  arXiv (Cornell University) · 2026-02-03

  articleOpen accessSenior author

  Causal discovery is a data-driven paradigm for analyzing complex systems, while physics-based models, such as ordinary differential equations (ODEs), provide mechanistic structure for real-world dynamical processes. Integrating these paradigms can improve identifiability, stability, and robustness. However, real dynamical systems often exhibit cyclic interactions and nonstationarity, whereas many causal discovery methods rely on acyclicity, stationarity, or equilibrium assumptions. We propose an integrative causal discovery framework for dynamical systems that leverages partial physical knowledge through stochastic differential equations (SDEs). The drift term encodes known ODE dynamics, while the diffusion term captures unknown causal couplings beyond the prescribed physics. We develop a scalable sparsity-inducing maximum quasi-likelihood estimator with a theoretically justified stabilization technique to improve the optimization landscape. Under mild conditions, we establish causal graph recovery guarantees for both stable and unstable SDEs. We also analyze robustness of our causal graph estimate to ODE misspecification and clarify how the introduced stabilization technique balances numerical stability and statistical recoverability. Experiments on linear SDEs and nonlinear benchmarks, including Lotka-Volterra and Lorenz dynamics with acyclic and cyclic structures, show improved graph recovery and robustness over data-driven baselines. We also demonstrate practical utility on real-world epidemic data by reconstructing stochastic SIR dynamics within our causal discovery framework.

  PublisherOA PDF

• Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models

  Open MIND · 2026-02-03

  preprintSenior author

  Causal discovery is a data-driven paradigm for analyzing complex systems, while physics-based models, such as ordinary differential equations (ODEs), provide mechanistic structure for real-world dynamical processes. Integrating these paradigms can improve identifiability, stability, and robustness. However, real dynamical systems often exhibit cyclic interactions and nonstationarity, whereas many causal discovery methods rely on acyclicity, stationarity, or equilibrium assumptions. We propose an integrative causal discovery framework for dynamical systems that leverages partial physical knowledge through stochastic differential equations (SDEs). The drift term encodes known ODE dynamics, while the diffusion term captures unknown causal couplings beyond the prescribed physics. We develop a scalable sparsity-inducing maximum quasi-likelihood estimator with a theoretically justified stabilization technique to improve the optimization landscape. Under mild conditions, we establish causal graph recovery guarantees for both stable and unstable SDEs. We also analyze robustness of our causal graph estimate to ODE misspecification and clarify how the introduced stabilization technique balances numerical stability and statistical recoverability. Experiments on linear SDEs and nonlinear benchmarks, including Lotka-Volterra and Lorenz dynamics with acyclic and cyclic structures, show improved graph recovery and robustness over data-driven baselines. We also demonstrate practical utility on real-world epidemic data by reconstructing stochastic SIR dynamics within our causal discovery framework.

  DOI

• Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing

  arXiv (Cornell University) · 2025-01-10

  preprintOpen accessSenior author

  Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the causal relationships among multiple variables and outcomes. Yet, it has not been fully recognized and deployed in the manufacturing systems. In this paper, we introduce an explainable, scalable, and flexible federated Bayesian learning framework, \texttt{xFBCI}, designed to explore causality through treatment effect estimation in distributed manufacturing systems. By leveraging federated Bayesian learning, we efficiently estimate posterior of local parameters to derive the propensity score for each client without accessing local private data. These scores are then used to estimate the treatment effect using propensity score matching (PSM). Through simulations on various datasets and a real-world Electrohydrodynamic (EHD) printing data, we demonstrate that our approach outperforms standard Bayesian causal inference methods and several state-of-the-art federated learning benchmarks.

  PublisherOA PDFDOI

• Bayesian federated causal inference and its application in manufacturing

  Journal of Intelligent Manufacturing · 2025-08-02 · 1 citations

  articleOpen accessSenior author

  Abstract Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the causal relationships among multiple variables and outcomes. Yet, it has not been fully recognized and deployed in the manufacturing systems. In this paper, we introduce a scalable, and flexible Bayesian federated learning framework, xFBCI, designed to explore causality through treatment effect estimation in distributed manufacturing systems. By leveraging federated Bayesian learning, we efficiently estimate posterior of local parameters to derive the propensity score for each client without accessing local private data. These scores are then used to estimate the treatment effect using propensity score matching (PSM). Through simulations on various datasets and real-world Electrohydrodynamic (EHD) printing data and smart manufacturing maintenance data, we demonstrate that our approach outperforms standard Bayesian causal inference methods and several state-of-the-art federated learning benchmarks.

  PublisherOA PDFDOI

• Causality-informed Anomaly Detection in Partially Observable Sensor Networks: Moving beyond Correlations

  ArXiv.org · 2025-07-13

  preprintOpen accessSenior author

  Nowadays, as AI-driven manufacturing becomes increasingly popular, the volume of data streams requiring real-time monitoring continues to grow. However, due to limited resources, it is impractical to place sensors at every location to detect unexpected shifts. Therefore, it is necessary to develop an optimal sensor placement strategy that enables partial observability of the system while detecting anomalies as quickly as possible. Numerous approaches have been proposed to address this challenge; however, most existing methods consider only variable correlations and neglect a crucial factor: Causality. Moreover, although a few techniques incorporate causal analysis, they rely on interventions-artificially creating anomalies-to identify causal effects, which is impractical and might lead to catastrophic losses. In this paper, we introduce a causality-informed deep Q-network (Causal DQ) approach for partially observable sensor placement in anomaly detection. By integrating causal information at each stage of Q-network training, our method achieves faster convergence and tighter theoretical error bounds. Furthermore, the trained causal-informed Q-network significantly reduces the detection time for anomalies under various settings, demonstrating its effectiveness for sensor placement in large-scale, real-world data streams. Beyond the current implementation, our technique's fundamental insights can be applied to various reinforcement learning problems, opening up new possibilities for real-world causality-informed machine learning methods in engineering applications.

  PublisherOA PDFDOI

• Collaborative and Distributed Bayesian Optimization via Consensus

  IEEE Transactions on Automation Science and Engineering · 2025-01-01 · 5 citations

  article1st authorCorresponding

  Optimal design is a critical yet challenging task within many applications. This challenge arises from the need for extensive trial and error, often done through simulations or running field experiments. Fortunately, sequential optimal design, also referred to as Bayesian optimization when using surrogates with a Bayesian flavor, has played a key role in accelerating the design process through efficient sequential sampling strategies. However, a key opportunity exists nowadays. The increased connectivity of edge devices sets forth a new collaborative paradigm for Bayesian optimization. A paradigm whereby different clients collaboratively borrow strength from each other by effectively distributing their experimentation efforts to improve and fast-track their optimal design process. To this end, we bring the notion of consensus to Bayesian optimization, where clients agree (i.e., reach a consensus) on their next-to-sample designs. Our approach provides a generic and flexible framework that can incorporate different collaboration mechanisms. In lieu of this, we propose transitional collaborative mechanisms where clients initially rely more on each other to maneuver through the early stages with scant data, then, at the late stages, focus on their own objectives to get client-specific solutions. Theoretically, we show the sub-linear growth in regret for our proposed framework. Empirically, through simulated datasets and a real-world collaborative sensor design experiment, we show that our framework can effectively accelerate and improve the optimal design process and benefit all participants. Note to Practitioners—The proposed algorithm allows multiple clients to collaboratively distribute their trial-and-error efforts to fast-track and improve the optimal design process. In the algorithm, each client performs a test locally and then shares the results with an orchestrator. Using the information from all clients, the orchestrator then finds the best new experiment that each client should undertake and sends those back for the next round of experiments. Through this process, all clients can leverage each other’s strengths and optimize their designs with far fewer experiments than each client operating in isolation. This is confirmed through many simulation examples, along with a real-life sensor design experiment where multiple collaborating agents seqeuntially coordinate their experimentation efforts. The goal is to rapidly discover the biosensor design and measurement format parameters that find the maximum amount of captured target analyte.

  PublisherDOI

• Federated Learning of Dynamic Bayesian Network via Continuous Optimization From Time Series Data

  IEEE Transactions on Artificial Intelligence · 2025-10-16

  articleSenior author

  Traditionally, learning the structure of a Dynamic Bayesian Network has been centralized, requiring all data to be pooled in one location. However, in real-world scenarios, data are often distributed across multiple entities (e.g., companies, devices) that seek to collaboratively learn a Dynamic Bayesian Network while preserving data privacy and security. More importantly, due to the presence of diverse clients, the data may follow different distributions, resulting in data heterogeneity. This heterogeneity poses additional challenges for centralized approaches. In this study, we first introduce a federated learning approach for estimating the structure of a Dynamic Bayesian Network (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FDBNL</monospace>) from homogeneous time series data that are horizontally distributed across different parties. We then extend this approach to heterogeneous time series data by incorporating a proximal operator as a regularization term in a personalized federated learning framework (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PFDBNL</monospace>). Moreover, we prove that our federated learning framework converges to a stationary point, addressing the lack of theoretical guarantees for continuous optimization based structure learning in federated settings. To the best of our knowledge, this is the first rigorous convergence result for structure learning under this paradigm. Experimental results on synthetic and real-world datasets demonstrate that our method outperforms state-of-the-art techniques, particularly in scenarios with many clients and limited individual sample sizes.

  PublisherDOI

• Toward Temporal Causal Representation Learning with Tensor Decomposition: A Study on EHR Data

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Fed-Joint: Joint modeling of nonlinear degradation signals and failure events for remaining useful life prediction using federated learning

  Reliability Engineering & System Safety · 2025-10-31 · 1 citations

  article
  PublisherDOI

• Toward Temporal Causal Representation Learning with Tensor Decomposition

  ArXiv.org · 2025-07-18

  preprintOpen accessSenior author

  Temporal causal representation learning is a powerful tool for uncovering complex patterns in observational studies, which are often represented as low-dimensional time series. However, in many real-world applications, data are high-dimensional with varying input lengths and naturally take the form of irregular tensors. To analyze such data, irregular tensor decomposition is critical for extracting meaningful clusters that capture essential information. In this paper, we focus on modeling causal representation learning based on the transformed information. First, we present a novel causal formulation for a set of latent clusters. We then propose CaRTeD, a joint learning framework that integrates temporal causal representation learning with irregular tensor decomposition. Notably, our framework provides a blueprint for downstream tasks using the learned tensor factors, such as modeling latent structures and extracting causal information, and offers a more flexible regularization design to enhance tensor decomposition. Theoretically, we show that our algorithm converges to a stationary point. More importantly, our results fill the gap in theoretical guarantees for the convergence of state-of-the-art irregular tensor decomposition. Experimental results on synthetic and real-world electronic health record (EHR) datasets (MIMIC-III), with extensive benchmarks from both phenotyping and network recovery perspectives, demonstrate that our proposed method outperforms state-of-the-art techniques and enhances the explainability of causal representations.

  PublisherOA PDFDOI

Frequent coauthors

• Raed Al Kontar

  26 shared

• Maher Nouiehed

  American University of Beirut

  6 shared

• Eunshin Byon

  University of Michigan–Ann Arbor

  3 shared

• Blake N. Johnson

  Virginia Tech

  2 shared

• Ana María Estrada Gómez

  Purdue University West Lafayette

  2 shared

• Wissam Kontar

  2 shared

• Naichen Shi

  University of Michigan–Ann Arbor

  2 shared

• Zhi‐Sheng Ye

  National University of Singapore

  2 shared

Awards & honors

• NSF grant for the project titled “SCH: Protecting Privacy an…