"I see yellow; therefore, there is colocalization.â Is it really so simple when it comes to colocalization studies? Unfortunately, and fortunately, no. Colocalization is in fact a supremely powerful technique for scientists who want to take full advantage of what optical microscopy has to offer: quantitative, correlative information together with spatial resolution. Yet, methods for colocalization have been put into doubt now that images are no longer considered simple visual representations. Colocalization studies have notoriously been subject to misinterpretation due to difficulties in robust quantification and, more importantly, reproducibility, which results in a constant source of confusion, frustration, and error. In this talk, I will share some of our effort and progress to ease such challenges using novel statistical and computational tools. Bio: Ming Yuan is Senior Investigator at Morgridge Institute for Research and Professor of Statistics at Columbia University and University of Wisconsin-Madison. He was previously Coca-Cola Junior Professor in the H. Milton School of Industrial and Systems Engineering at Georgia Institute of Technology. He received his Ph.D. in Statistics and M.S. in Computer Science from University of Wisconsin-Madison. His main research interests lie in theory, methods and applications of data mining and statistical learning. Dr. Yuan has been serving on editorial boards of various top journals including The Annals of Statistics, Bernoulli, Biometrics, Electronic Journal of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B, and Statistical Science. Dr. Yuan was awarded the John van Ryzin Award in 2004 by ENAR, CAREER Award in 2009 by NSF, and Guy Medal in Bronze from the Royal Statistical Society in 2014. He was also named a Fellow of IMS in 2015, and a Medallion Lecturer of IMS.