Nature-metaheuristics have been widely used in engineering, computer science and artificial intelligence to tackle various types of challenging optimization problems for decades and are increasingly used across disciplines. Interestingly, metaheuristics seem to be still relatively underused in the statistical research community.

I present an overview of nature-inspired metaheuristics and describe their main appealing features, which are their speed, flexibility, availability of codes in different platforms, and ease of implementation and usage. Above all, they are virtually assumptions free, enabling us to apply these general-purpose algorithms to tackle all kinds of high-dimensional optimization tasks. I will highlight some recent applications of these algorithms to construct theory-based early phase clinical trials, that are more realistic and flexible for dose response studies. If time permits, I will provide demonstrations to show how the codes work to find user-tailored optimal experimental designs.

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Bio: Professor Wong is a Professor at UCLA since 1990 and over the years, he has done collaborative work in dentistry, environment health science, rheumatology, and various domains in oncology, including in the design and analysis of cancer control and prevention trials for controlling Hepatitis B among Asians, colorectal cancer for Hispanics, and fighting obesity and promoting health of minorities at workplace. His main methodology research is in the construction of model-based optimal experimental designs for various biostatistical applications. His recent interests are in the applications of nature-inspired metaheuristics to tackle challenging estimation and design problems in toxicology, clinical trials and other areas of statistics. He has delivered more than 250 presentations globally, including recent short courses in design at Seoul National University and at the Toxicology Center in TU Dortmund University in Germany. Professor Wong has received grant awards from NSF and private foundations, along with several R01 grant awards from NIH as a principal investigator. He is fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Association for the Advancement of Science, an elected member of the International Statistical Institute and a full member of the Sigma Xi - The Scientific Research Honor Society. He has also just completed a 3-year Yushan Scholarship Award from the Ministry of Education in Taiwan.

Biostatistics is a field that requires multi-disciplinary collaboration between statisticians, medical professionals and other subject-domain experts. Over the last several years, BC Children’s Hospital Research Institute (BCCHRI) has built a biostatistics core to provide expertise to their research community on development of appropriate study design and analysis methods for clinical and public health research. This talk will outline the experience of leading the biostatistics unit, key skills for success in applied settings when working with non-statistical collaborators, and the tension between theoretical best practice and the constraints of real-world data. Examples of projects from BCCHRI will be used to illustrate statistical techniques and challenges. 

To join this seminar virtually, please request Zoom connection details from ea@stat.ubc.ca. 

Jeff Bone is the Biostatistical Lead at BC Children’s Hospital Research Institute. In this role, he provides methodological input to clinical and epidemiological research studies across a range of disciplines, supervises analysts and trainees, and provides community education. He has a PhD in Women’s and Children’s Health (UBC) focused on statistical methods and modelling in perinatal epidemiology, an MSc in Statistics (UBC) and BSc (Hons) in Mathematics and Statistics (UVic). His current areas of statistical research include analysis of population level data, causal inference for observational data and design and analysis of randomized controlled trials. His main areas of applied work are in perinatal epidemiology, obstetrics, and pediatric diabetes.