Xubo Yue

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

Selected publications

• Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks

  IEEE Transactions on Pattern Analysis and Machine Intelligence · 2023-08-23 · 19 citations

  article

  Differential equations are fundamental in modeling numerous physical systems, including thermal, manufacturing, and meteorological systems. Traditionally, numerical methods often approximate the solutions of complex systems modeled by differential equations. With the advent of modern deep learning, Physics-informed Neural Networks (PINNs) are evolving as a new paradigm for solving differential equations with a pseudo-closed form solution. Unlike numerical methods, the PINNs can solve the differe…

  PublisherDOI

• 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 con…

  PublisherDOI

• Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling

  arXiv (Cornell University) · 2021-11-28 · 4 citations

  preprintOpen access1st authorCorresponding

  In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations. Notably, the resulting global model excels in personalization as \texttt{FGPR} jointly learns a global $\mathcal{GP}$ prior across all clients. The predictive posterior then is obtained by exploiting this prior and conditioning on local data which encodes personalized featur…

  PublisherOA PDFDOI

• Scalable Accelerated Materials Discovery of Sustainable Polysaccharide-Based Hydrogels by Autonomous Experimentation and Collaborative Learning

  ACS Applied Materials & Interfaces · 2024-12-11 · 3 citations

  articleOpen access

  While some materials can be discovered and engineered using standalone self-driving workflows, coordinating multiple stakeholders and workflows toward a common goal could advance autonomous experimentation (AE) for accelerated materials discovery (AMD). Here, we describe a scalable AMD paradigm based on AE and "collaborative learning". Collaborative learning using a novel consensus Bayesian optimization (BO) model enabled the rapid discovery of mechanically optimized composite polysaccharide hyd…

  PublisherDOI

• Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design

  arXiv (Cornell University) · 2023-06-25 · 2 citations

  preprintOpen access1st 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 con…

  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…

Similar researchers at Northeastern University

• Nian X. Sunh-75
• Yongmin Liuh-63
• Hanumant Singhh-56