Chirag Shah – författare

“This is a high quality book with directions and guidelines on how to generate valid treatment plans in the modern era of radiation oncology. It is very useful for any student (dosimetry, therapy, physicist, or physician) who is entering a practical treatment planning rotation…It is written as a companion to the Handbook of Treatment Planning in Radiation Oncology, 2nd edition, Videtic et al. (Demos Medical Publishing, 2015), and pairs very well with it.”Score: 88, 3 Stars, Doody’s Medical Reviews“Comparing with earlier published books about radiotherapy treatment planning, which are prone to the pedagogical side as textbooks, this new book serves an unmet need as a pocket-sized book with details and up to date information for user’s quick resource for treatment planning knowledge… “Strategies for Radiation Therapy Treatment Planning” is a handy and essential reference for modern treatment planning. It is therefore recommended as a valuable book for the bookshelf and pocket of everyone involved in radiotherapy treatment planning.”-- Dr. Chengyu Shi of Memorial Sloan Kettering Cancer Center for Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc.Strategies for Radiation Therapy Treatment Planning provides radiation oncologists, physicists, and dosimetrists with a step-by-step guide to implementing external beam treatment plans that meet clinical requirements for each major disease site. As a companion book to the Handbook of Treatment Planning in Radiation Oncology Second Edition, this book focuses on the technical aspects of treatment planning and the major challenges in creating highly conformal dose distributions, referenced to as treatment plans, for external beam radiotherapy. To overcome challenges associated with each step, leading experts at the Cleveland Clinic have consolidated their knowledge and experience of treatment planning techniques, potential pitfalls, and other difficulties to develop quality plans across the gamut of clinical scenarios in radiation therapy.The book begins with an overview of external beam treatment planning principles, inverse planning and advanced planning tools, and descriptions of all components in simulation and verification. Following these introductory chapters are disease-site examples, including central nervous system, head and neck, breast, thoracic, gastrointestinal, genitourinary, gynecologic, lymphoma, and soft tissue sarcoma. The book concludes with expert guidance on planning for pediatric cancers and how to tailor palliative plans. Essential for all radiation therapy team members, including trainees, this book is for those who wish to learn or improve their treatment planning skills and understand the different treatment planning processes, plan evaluation, and patient setup.KEY FEATURES:Provides basic principles of treatment planningContains step-by-step, illustrated descriptions of the treatment planning processDiscusses the pros and cons of advanced treatment planning tools, such as auto-planning, knowledge-based planning, and multi-criteria based planningDescribes each primary treatment site from simulation, patient immobilization, and creation of various treatment plans to plan evaluationsIncludes instructive sample plans to highlight best practicesComes with access to the fully downloadable eBook

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.

Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool Python, a new chapter on using Python for statistical analysis, and a new chapter that demonstrates how to use Python within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Students will develop a practical understanding of data science with this hands-on textbook for introductory courses. This new edition is fully revised and updated, with numerous exercises and examples in the popular data science tool R, a new chapter on using R for statistical analysis, and a new chapter that demonstrates how to use R within a range of cloud platforms. The many practice examples, drawn from real-life applications, range from small to big data and come to life in a new end-to-end project in Chapter 11. New 'Data Science in Practice' boxes highlight how concepts introduced work within an industry context and many chapters include new sections on AI and Generative AI. A suite of online material for instructors provides a strong supplement to the book, including lecture slides, solutions, additional assessment material and curriculum suggestions. Datasets and code are available for students online. This entry-level textbook is ideal for readers from a range of disciplines wishing to build a practical, working knowledge of data science.

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

"The best physician-specific general financial book published in 2021." - James M. Dahle, MD, author of The White Coat InvestorA step-by-step guidebook for doctors and other medical professionals about growing and preserving wealth, Financial Freedom Rx: The Physician’s Guide to Achieving Financial Independence gives physicians all the tools necessary to manage their own finances and includes a foreword by Mel Lindauer, co-author of The Bogleheads' Guide to Investing.Medical professionals, especially doctors, spend many years in training as they accumulate debt and delay their earnings. This book presents a time-tested formula that students and established professionals can follow at any stage during their careers to achieve fiscal peace of mind.Students will learn how to budget and adopt disciplined financial practices. Residents and other trainees will learn how to defend against calamity with various insurances and how to manage debt. Junior professionals will acquire the skills needed to invest and grow their portfolios, while senior professionals will better understand the essentials of estate planning and retirement.Drs. Chirag P. Shah and Jayanth Sridhar wrote this inspiring text to guide physicians where to put their next dollar. This is particularly important during the financial uncertainties brought on by COVID-19 and insurance cuts. Financial Freedom Rx sets forth principles that will pilot medical professionals toward financial independence.Chapters include useful advice on topics such as:Financial planningInvesting and asset allocationJobs and contractsTaxes and insuranceStudent loans and debtRetirement savings and distributionsFinancial Freedom Rx: The Physician’s Guide to Achieving Financial Independence serves as a timeless blueprint for financial planning that medical professionals will follow throughout their careers, and as a reference that readers will revisit again and again as they progress through the various stages of life.

Since user study design has been widely applied in search interactions and information retrieval (IR) systems evaluation studies, a deep reflection and meta-evaluation of interactive IR (IIR) user studies is critical for sharpening the instruments of IIR research and improving the reliability and validity of the conclusions drawn from IIR user studies. To this end, we developed a faceted framework for supporting user study design, reporting, and evaluation based on a systematic review of the state-of-the-art IIR research papers recently published in several top IR venues (n=462). Within the framework, we identify three major types of research focuses, extract and summarize facet values from specific cases, and highlight the under-reported user study components which may significantly affect the results of research. Then, we employ the faceted framework in evaluating a series of IIR user studies against their respective research questions and explain the roles and impacts of the underlying connections and "collaborations" among different facet values. Through bridging diverse combinations of facet values with the study design decisions made for addressing research problems, the faceted framework can shed light on IIR user study design, reporting, and evaluation practices and help students and young researchers design and assess their own studies.

Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better.

Generative Artificial Intelligence (GenAI) has emerged as a groundbreaking technology that promises to revolutionize many industries as well as people’s personal and professional lives. This book discusses GenAI and its role in information access - often referred to as Generative Information Retrieval (GenIR) - or more broadly, information interaction.The role of GenAI in information access is complex and dynamic, with many dimensions. To address this, following a brief introduction to GenAI and GenIR, the remainder of the book provides eight chapters, each targeting a different dimension or sub-topic. These cover foundations of GenIR, interactions with GenIR systems, adapting them to users, tasks, and scenarios, improving them based on user feedback, GenIR evaluation, the sociotechnical implications of GenAI for information access, recommendations within GenIR, and the future of information access with GenIR.The book is targeted at graduate students and researchers interested in issues of information retrieval, access, and interactions, as well as applications of GenAI in various informational contexts. While some of the parts assume prior background in IR or AI, most others do not, making this book suitable for adoption in various classes as a primary source or as a supplementary material.

Generative Artificial Intelligence (GenAI) has emerged as a groundbreaking technology that promises to revolutionize many industries as well as people’s personal and professional lives. This book discusses GenAI and its role in information access - often referred to as Generative Information Retrieval (GenIR) - or more broadly, information interaction.The role of GenAI in information access is complex and dynamic, with many dimensions. To address this, following a brief introduction to GenAI and GenIR, the remainder of the book provides eight chapters, each targeting a different dimension or sub-topic. These cover foundations of GenIR, interactions with GenIR systems, adapting them to users, tasks, and scenarios, improving them based on user feedback, GenIR evaluation, the sociotechnical implications of GenAI for information access, recommendations within GenIR, and the future of information access with GenIR.The book is targeted at graduate students and researchers interested in issues of information retrieval, access, and interactions, as well as applications of GenAI in various informational contexts. While some of the parts assume prior background in IR or AI, most others do not, making this book suitable for adoption in various classes as a primary source or as a supplementary material.

Compiled by world- class leaders in the field of collaborative information retrieval and search (CIS), this book centres on the notion that information seeking is not always a solitary activity and working in collaboration to perform information-seeking tasks should be studied and supported. Covering aspects of theories, models, and applications the book is divided in three parts:·         Best Practices and Studies: providing an overview of current knowledge and state-of-the-art in the field.·         New Domains: covers some of the new and exciting opportunities of applying CIS·         New Thoughts: focuses on new research directions by scholars from academia and industry from around the world.Collaborative Information Seeking provides a valuable reference for student, teachers, and researchers interested in the area of collaborative work, information seeking/retrieval, and human-computer interaction.

Compiled by world- class leaders in the field of collaborative information retrieval and search (CIS), this book centres on the notion that information seeking is not always a solitary activity and working in collaboration to perform information-seeking tasks should be studied and supported. Covering aspects of theories, models, and applications the book is divided in three parts:·         Best Practices and Studies: providing an overview of current knowledge and state-of-the-art in the field.·         New Domains: covers some of the new and exciting opportunities of applying CIS·         New Thoughts: focuses on new research directions by scholars from academia and industry from around the world.Collaborative Information Seeking provides a valuable reference for student, teachers, and researchers interested in the area of collaborative work, information seeking/retrieval, and human-computer interaction.

This volume summarizes the author’s work on social information seeking (SIS), and at the same time serves as an introduction to the topic. Sometimes also referred to as social search or social information retrieval, this is a relatively new area of study concerned with the seeking and acquiring of information from social spaces on the Internet. It involves studying situations, motivations, and methods involved in seeking and sharing of information in participatory online social sites, such as Yahoo! Answers, WikiAnswers, and Twitter, as well as building systems for supporting such activities.The first part of the book introduces various foundational concepts, including information seeking, social media, and social networking. As such it provides the necessary basis to then discuss how those aspects could intertwine in different ways to create methods, tools, and opportunities for supporting and leveraging SIS. Next, Part II discusses the social dimension and primarily examines the online question-answering activity. Part III then emphasizes the collaborative aspect of information seeking, and examines what happens when social and collaborative dimensions are considered together. Lastly, Part IV provides a synthesis by consolidating methods, systems, and evaluation techniques related to social and collaborative information seeking. The book is completed by a list of challenges and opportunities for both theoretical and practical SIS work.The book is intended mainly for researchers and graduate students looking for an introduction to this new field, as well as developers and system designers interested in building interactive information retrieval systems or social/community-driven interfaces.

This volume summarizes the author’s work on social information seeking (SIS), and at the same time serves as an introduction to the topic. Sometimes also referred to as social search or social information retrieval, this is a relatively new area of study concerned with the seeking and acquiring of information from social spaces on the Internet.

Today’s complex, information-intensive problems often require people to work together. Mostly these tasks go far beyond simply searching together; they include information lookup, sharing, synthesis, and decision-making. In addition, they all have an end-goal that is mutually beneficial to all parties involved. Such “collaborative information seeking” (CIS) projects typically last several sessions and the participants all share an intention to contribute and benefit. Not surprisingly, these processes are highly interactive.Shah focuses on two individually well-understood notions: collaboration and information seeking, with the goal of bringing them together to show how it is a natural tendency for humans to work together on complex tasks. The first part of his book introduces the general notions of collaboration and information seeking, as well as related concepts, terminology, and frameworks; and thus provides the reader with a comprehensive treatment of the concepts underlying CIS. The second part of the book details CIS as a standalone domain. A series of frameworks, theories, and models are introduced to provide a conceptual basis for CIS. The final part describes several systems and applications of CIS, along with their broader implications on other fields such as computer-supported cooperative work (CSCW) and human-computer interaction (HCI).With this first comprehensive overview of an exciting new research field, Shah delivers to graduate students and researchers in academia and industry an encompassing description of the technologies involved, state-of-the-art results, and open challenges as well as research opportunities.

Today’s complex, information-intensive problems often require people to work together. Mostly these tasks go far beyond simply searching together; they include information lookup, sharing, synthesis, and decision-making. In addition, they all have an end-goal that is mutually beneficial to all parties involved. Such “collaborative information seeking” (CIS) projects typically last several sessions and the participants all share an intention to contribute and benefit. Not surprisingly, these processes are highly interactive.Shah focuses on two individually well-understood notions: collaboration and information seeking, with the goal of bringing them together to show how it is a natural tendency for humans to work together on complex tasks. The first part of his book introduces the general notions of collaboration and information seeking, as well as related concepts, terminology, and frameworks; and thus provides the reader with a comprehensive treatment of the concepts underlying CIS. The second part of the book details CIS as a standalone domain. A series of frameworks, theories, and models are introduced to provide a conceptual basis for CIS. The final part describes several systems and applications of CIS, along with their broader implications on other fields such as computer-supported cooperative work (CSCW) and human-computer interaction (HCI).With this first comprehensive overview of an exciting new research field, Shah delivers to graduate students and researchers in academia and industry an encompassing description of the technologies involved, state-of-the-art results, and open challenges as well as research opportunities.

Most AI books either promise utopia or warn of apocalypse. This one does neither.We are living through the most consequential technology deployment in human history, yet most of the people making decisions about AI—from judges using it in courtrooms to HR executives screening job candidates—don't actually understand how it works or what could go wrong. AI systems embed the biases, assumptions, and blind spots of their creators, then amplify them at scale across society.Chirag Shah has spent his career building intelligent systems—and the last decade explaining them to the people who actually use them. In this book, he challenges us—and himself—to think deeper about how AI learns and why that matters for how we build and use these systems.Through vivid encounters with HAL, the Terminator, and TARS, Shah shows how science fiction has shaped our expectations of AI—for better and worse. Using parenting as a metaphor—from strict boundaries to guided autonomy to eventual partnership—he offers three principles for developing AI responsibly as it evolves from narrow tools toward general intelligence.Blending science fiction, personal narrative, and frontline experience with decision-makers, Shah delivers something rare: an AI book that educates without lecturing, acknowledges uncertainty without despair, and offers a practical framework for developing AI as it matures. Whether you're building these systems, deploying them, interacting with them, or simply living with their consequences, Raising AI is essential reading.