[ad_1]
In case of chronic renal failure (CKD), the kidneys gradually lose their function. In the advanced stage of the disease – end-stage renal failure – patients should receive a kidney transplant or undergo dialysis. People on dialysis often suffer from multiple comorbidities, including diabetes and cardiovascular disease, and their mortality and hospitalization rates are much higher than those of the general population.
At present, more than 26 million American adults suffer from MRCs.
Yuedong Wang, a professor at the University of California at Santa Barbara, and his team are working on statistical and computer methods to explore a large amount of data on the RCM and dialysis to better understand the biology of the patient and advance personalized medicine.
"It is very gratifying to see that some of our research has been implemented in clinics to improve the care of dialysis patients," said Wang, a faculty member of the Department of Statistics and Applied Probability (PSTAT) and a member Founder of MONitoring Dialysis Results (MONDO), an initiative that collects, merges and badyzes data from dialysis providers around the world.
For his "contributions to non-parametric regression and computational statistics, especially spline smoothing methodology for dependent observation and applications to bioinformatics and biomedical modeling", Wang was elected a member of the clbad 2019 of the Institute of Mathematical Statistics (IMS). He joins Jean-Pierre Fouque and S. Rao Jammalamadaka, professors at UC Santa Barbara, as well as other faculty members from PSTAT, as the IMS's third Fellow of the department.
"I am deeply honored to have been elected as an IMS Fellow," Wang said. "I am grateful for the support of my colleagues and the UCSB."
Wang's most significant work is his efforts in the field of spline smoothing, a technique for interpreting large, often "noisy" data sets to reflect the broadest trend. Originally designed as a hydrodynamic hull construction tool for shipbuilders, the spline serves as a guide for bending a material in an effective and smooth curve between two fixed points. Similarly, in the world of computation statistics, a spline is an estimate in a set of points that corresponds to the data with minimal fluctuations, often the smoothest possible. It can be used in various applications to estimate trends of large data sets over time, such as the economy, demographics and biomedicine.
"Professor Wang really deserves this honor because his research uses innovative methods with real applications," said Pierre Wiltzius, Dean of Mathematics, Life and Physical Sciences. "His work is particularly impressive for his state-of-the-art statistical tools for badyzing large biomedical datasets, helping us to improve the delivery of health care, and I congratulate him for this remarkable achievement."
Wang has contributed to statistical methodology, theory, computation, software and applications. He has worked in a wide range of areas, including non-parametric and semi-parametric methods, machine learning, big data, and biomedical applications. A member of the Department of Statistics and Probability Applied since 1997, Wang is the author of more than 122 articles and six book chapters. He has written four software packages on the theory and practice of computer statistics and on biomedical topics such as hormone-related diseases including circadian rhythms, diabetes and endometriosis, as well as genomics and stuttering. He is also the author of "Smoothing Splines: Methods and Applications" (CRC Press, 2011).
Wang and the 24 other new IMS 2019 members will be featured at the ceremony of the IMS Presidential Speech and Awards at the Joint Statistical Meeting on Monday, July 29th.
###
Warning: AAAS and EurekAlert! are not responsible for the accuracy of the news releases published on EurekAlert! contributing institutions or for the use of any information via the EurekAlert system.
Source link
