Research and Technical Writing for Science and Engineering

Engineering and science research can be difficult for beginners because scientific research is fraught with constraints and disciplines. Research and Technical Writing for Science and Engineering breakdowns the entire process of conducting engineering and scientific research. This book covers those fascinating guidelines and topics on conducting research, as well as how to better interact with your advisor.

Key Features:

• advice on conducting a literature review, conducting experiments, and writing a good paper summarizing your findings.
• provides a tutorial on how to increase the impact of research and how to manage research resources.

By reflecting on the cases discussed in this book, readers will be able to identify specific situations or dilemmas in their own lives, as the authors provide comprehensive suggestions based on their own experiences.

Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received the PhD degree of Computer Science from University of Texas at Dallas. Currently, he is the Department Head and tenured full professor of Texas A&M University Commerce. He is an ACM Distinguished Member and IEEE Senior member. He is also the Highly Cited Researcher in 2021 from Web of Science and IEEE Distinguished Visitor in 2021-2022. He is the Chair of IEEE Smart Computing Technical Committee. His research interests include Cyber Security, Big Data Analysis, Cloud Computing, Smarting Computing, Intelligent Data, Embedded systems, etc.

Han Qiu received the BE degree from Beijing University of Posts and Telecommunications and received the ME degree and PhD degree in Computer Networks from Telecom-ParisTech. He is currently an assistant professor in the Institute for Network Sciences and Cyberspace at Tsinghua University. His research interests include AI Security, Big Data Security, Internet of Things, Cloud Computing, etc. He won the best paper awards of the ICA3PP 2020 and SmartCom 2019. He has published 40+ peer-reviewed journal and conference papers. He served as the program chair for the KSEM 2021 and the IEEE ISPA 2021.

Yi Zeng is a first-year PhD student in Computer Engineering at Virginia Tech. He earned his BE in Electronic and Information Engineering from Xidian University and his MS in Machine Learning and Data Science from the University of California, San Diego. His research interests include trustworthy machine learning, AI security, and the reliable data market for ML. He received the best paper award at the ICA3PP 2020.