Dr. Zhipeng Cai is an Associate Professor in the Department of Computer Science at Georgia State University. He earned his PhD degree from the Department of Computing Science at University of Alberta, Canada. Dr. Cai’s research focuses on algorithm design, big data analytics, cyber-security, privacy and networking. Dr. Cai is the recipient of an NSF CAREER Award. He has published more than 120 papers including over 40 IEEE/ACM Transactions papers. His work has been cited more than 4600 times in Google Scholar and his current h-index is 40. Dr. Cai’s research contributions include both theoretical results and system development. His research has been continuously supported by the National Science Foundation and Industrial grants. He has received more than 10 research grants with the total amount of more than 3 million dollars.
Dr. Cai has served as an associate editor for more than 10 journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Vehicular Technology (TVT), IEEE Network Letter, etc. He also has served as a guest editor for many prestigious journals, such as IEEE/ACM Transactions on Computational Biology and Bioinformatics, Theoretical Computer Science, Algorithmica. Dr. Cai is currently a Steering Committee Co-Chair for the International Conference on Wireless Algorithms, Systems, and Applications, and a Steering Committee member for the International Computing and Combinatorics Conference and the International Performance Computing and Communications Conference. He also served as a General Co-Chair, Technical Program Co-Chair or Local Arrangement Co-Chair for several prestigious international conferences including IEEE INFOCOM, IEEE ICDCS, IEEE IPCCC, COCOON, ISBRA, etc. Besides the leadership, Dr. Cai’s achievements and contributions at GSU are highly recognized by his college. He won the College of Arts and Sciences Outstanding Junior Faculty Award in 2017 and also won the Dean’s Early Career Award in 2016.
Dr. Cai has graduated 8 PhD students, among whom five are now Assistant Professors in US and two are Associate/Assistant Professors in China. Furthermore, he has supervised more than 20 MS students.
Data is getting dirty when data continues to grow in volume. As the fundamental components of data quality management, quality evaluation, dirty data repairing and querying dirty data are very important and challenging. One of my current research projects focuses on such three aspects in dirty data management. It aims to propose a series of solid works that enable the authentic data quality evaluation, derive explanations for abnormal query results and explanations for surprising observations, and provide a complete picture of the computational complexity hierarchy for semi-automatic data repairing. Moreover, we try to propose a new paradigm to query inconsistent data and build the corresponding theoretical foundation.
Our derived computational complexity results and proposed algorithms have solved some key problems for data inconsistency evaluation, dirty data repairing and querying. Especially, our results on computational complexity build a solid theoretical foundation for the semi-automatic data repairing paradigm, and our algorithmic work enables the reliable semi-automatic data repairing paradigm. The series of results also provide an answer for the theoretical questions on view propagation which have been widely studied for nearly 20 years, and make a significant contribution to data lineage and data quality management. Our work also quantitatively proves an interesting result that the computation load of “How many lies you have to tell carefully in order to cover a lie you told” is strictly higher than the truth ground verification in terms of complexity considering both query and data.
The tremendous growth of machine learning applications and services in the recent years has necessitated strong needs for qualified professionals who have capabilities to understand statistical model, data analysis, etc. In order to cope with the national needs for qualified machine learning workforce. I designed the first Machine Learning and Advanced Machine Learning courses for the undergraduate and graduate students in our department, respectively. I develop many meaningful and practical projects with the intention of closing the gap between the machine learning theories and real-world practices. It helps my students to better understand the principles of machine learning and they are able to develop appropriate machine learning strategies to solve the emerging real-life problems.
My favorite memory at Georgia State University is the graduation ceremony of my students. I feel lucky to have the opportunities to work with those creative young people. It is my great pleasure to watch them grow into independent researchers with bright futures. I am really proud of their achievements.