{"title":"Momiao Xiong","description":null,"products":[{"product_id":"artificial-intelligence-and-causal-inference-book-momiao-xiong-9780367859404","title":"Artificial Intelligence and Causal Inference","description":"Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":50698095886609,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":50698097787153,"sku":"NGR9780367859404","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0367859408.jpg?v=1750697365"},{"product_id":"artificial-intelligence-and-causal-inference-book-momiao-xiong-9781032193281","title":"Artificial Intelligence and Causal Inference","description":"\u003cp\u003e\u003cb\u003eArtificial Intelligence and Causal Inference \u003c\/b\u003eaddress the recent development of relationships between artificial intelligence\u003cb\u003e (AI) \u003c\/b\u003eand causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine. \u003c\/p\u003e\u003cp\u003eKey Features:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCover three types of neural networks, formulate deep learning as an optimal control problem and use Pontryagin’s Maximum Principle for network training.\u003c\/li\u003e\n\u003cli\u003eDeep learning for nonlinear mediation and instrumental variable causal analysis.\u003c\/li\u003e\n\u003cli\u003eConstruction of causal networks is formulated as a continuous optimization problem.\u003c\/li\u003e\n\u003cli\u003eTransformer and attention are used to encode-decode graphics. RL is used to infer large causal networks.\u003c\/li\u003e\n\u003cli\u003eUse VAE, GAN, neural differential equations, recurrent neural network (RNN) and RL to estimate counterfactual outcomes.\u003c\/li\u003e\n\u003cli\u003eAI-based methods for estimation of individualized treatment effect in the presence of network interference.\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"WoB","offers":[{"title":"US \/ NEW \/ INGRAM","offer_id":51017333571857,"sku":"NIN9781032193281","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":51215858401553,"sku":"NGR9781032193281","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52591683895569,"sku":"NLS9781032193281","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/103219328X.jpg?v=1750886564"},{"product_id":"mathematical-foundations-of-artificial-intelligence-book-momiao-xiong-9781041076254","title":"Mathematical Foundations of Artificial Intelligence","description":"\u003cp\u003e\u003cem\u003eMathematical Foundations of Artificial Intelligence: Basics of Manifold Theory\u003c\/em\u003e is the first volume in a two‑part series. Together, they establish a unifying mathematical framework based on smooth manifold theory and Riemannian geometry・essential tools for representing, analyzing, and integrating the growing complexity of modern artificial intelligence (AI) systems and scientific models.\u003c\/p\u003e\u003cp\u003eDifferential geometry now plays a central role across AI, biology, physics, and medicine. From deep learning, generative modeling, and manifold learning to reasoning algorithms and physical AI, manifolds offer a coherent geometric language that bridges theory and practice. This volume introduces key concepts・topological and smooth manifolds, Riemannian metrics, differential forms, Lie derivatives, and statistical geometry・alongside illustrative applications to data science, genomics, drug discovery, and AI‑driven systems.\u003c\/p\u003e\u003cp\u003eUnlike traditional texts, this book combines rigor with intuition, integrating formal theory, computational methods, and interdisciplinary insights, and is ideal for graduate students and professionals in mathematics, statistics, computer science, AI, physics, bioinformatics, and biomedical sciences. It also serves as a foundational reference for researchers developing AI systems grounded in geometry, scientific modeling, and data‑driven discovery.\u003c\/p\u003e\u003cp\u003eKey Features\u003c\/p\u003e\u003cp\u003e• Unifies core manifold concepts to support integrated thinking across disciplines\u003c\/p\u003e\u003cp\u003e• Treats manifolds as natural geometric domains for data representation in AI and the sciences\u003c\/p\u003e\u003cp\u003e• Bridges abstract theory with practical algorithms and real‑world applications\u003c\/p\u003e\u003cp\u003e• Develops Lie derivative aware graphical neural networks for adaptive‑AI and molecular property prediction\u003c\/p\u003e\u003cp\u003e• Develops Lie derivative enhanced reaction‑diffusion equations for disease gene identification and treatment design\u003c\/p\u003e\u003cp\u003e• Develops probabilistic modeling and information geometry for modern learning systems\u003c\/p\u003e\u003cp\u003e• Applies geometric insight to AI fields, including generative models, graph learning, and reasoning\u003c\/p\u003e\u003cp\u003e• Applies the Gauss map and Chen-Gauss-Bonnet theorem to physical AI incorporating geometric constraints for robotics and tumor cell location and range identification\u003c\/p\u003e\u003cp\u003e• Features step‑by‑step examples, case studies, and visual explanations to support understanding\u003c\/p\u003e\u003cp\u003e• Serves as an advanced educational and skill‑building resource in the age of AI, leveraging the capabilities of emerging AI tools for automatic programming and self‑study\u003c\/p\u003e","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51851011326225,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ GARDNERS","offer_id":51851011457297,"sku":"NGR9781041076254","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"US \/ NEW \/ INGRAM","offer_id":53004502728977,"sku":"NIN9781041076254","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":53113656606993,"sku":"NLS9781041076254","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9781041076254.jpg?v=1768040155"},{"product_id":"big-data-in-omics-and-imaging-two-volume-set-book-momiao-xiong-9780367002183","title":"Big Data in Omics and Imaging, Two Volume Set","description":"FEATURES  Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data  Provides tools for high dimensional data reduction  Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection  Provides real-world examples and case studies  Will have an accompanying website with R code  Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently.   Introduce causal inference theory to genomic, epigenomic and imaging data analysis   Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies.   Bridge the gap between the traditional association analysis and modern causation analysis   Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks   Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease   Develop causal machine learning methods integrating causal inference and machine learning   Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks   The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ GARDNERS","offer_id":53333214298385,"sku":"NGR9780367002183","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9780367002183.jpg?v=1782819449"}],"url":"https:\/\/www.worldofbooks.com\/collections\/author-books-by-momiao-xiong.oembed","provider":"World of Books ","version":"1.0","type":"link"}