Amita Manatunga to deliver the 2020 E.L. Scott Lecture
Liza Levina (University of Michigan, Chair of the Award Committee) and Lance Waller (Emory University) write:
The COPSS Elizabeth L. Scott Award committee has awarded the 2020 award to Professor Amita Manatunga of Emory University. She was selected as this year’s recipient for her dedicated mentoring of the next generation of statisticians; committed leadership in expanding statistical opportunities for women and minorities at the individual, institutional, and professional society levels; and for excellence in biostatistical research.
The award will be presented at the 2020 JSM in Philadelphia, where Dr. Manatunga will also deliver the inaugural Scott Lecture: “Statistical Methods for Diagnosis of Complex Diseases with Complex Data.”
Dr. Manatunga is the Donna Jean Brogan Professor of Biostatistics and Bioinformatics in the Rollins School of Public Health at Emory University. She was born and raised in Sri Lanka and received her BS degree in physics and mathematics, with first class honors, from the University of Colombo, Sri Lanka. She earned her Master’s in statistics from Purdue University, and her PhD in statistics from the University of Rochester in 1990. Dr. Manatunga was an Assistant Professor of Biostatistics at Indiana University before joining Emory in 1994.
Dr. Manatunga’s research is inspired by the need for innovative statistical methods in important and complex public health problems. Three primary areas of application she has worked on include mental health, epidemiology, and nuclear medicine. She has made substantial methodological contributions in multiple areas including survival analysis, interpretation of diagnostic markers, agreement studies, and functional data. She has published over 125 peer-reviewed papers and been funded by numerous methodological and collaborative grants from the NIH. Dr. Manatunga is a recipient of many awards, including a FIRST award from NIH in 1996, and she was elected fellow of the ASA in 2004.
Throughout her career, Dr. Manatunga has served as a devoted mentor to many graduate students, junior faculty, and other researchers in biostatistics and the health sciences, with a special focus on helping early-career women. She has chaired the ASA Committee on Women in Statistics and the Gertrude Cox Scholarship for Women Award Committee, and is deeply involved in multiple diversity initiatives. She is a co-founder (in 2010) and consistent supporter of ENAR’s Diversity Caucus, and a frequent invited speaker at workshops aimed at increasing diversity. Her contributions to ENAR’s annual Fostering Diversity in Biostatistics Workshop, continuously since its inception, have had a lasting impact on the participants and many others who see her as a role model. Many of her former students are now in leadership positions in academia, government, and professional societies.
Dr. Manatunga’s Scott lecture, titled “Statistical Methods for Diagnosis of Complex Diseases with Complex Data,” will cover innovative statistical methods that address challenging problems in the diagnosis of complex diseases and characterization of their underlying mechanisms, in two specific contexts. One is mental disorders, complex and multifactorial conditions often lacking reliable tools for diagnosis. Multiple instruments are often used to quantify the same mental health trait, and combining them is a challenge. A statistical framework will be proposed for creating new scales and interpreting new instruments on different types of measurements. In a different setting, nuclear medicine practitioners collect and analyze diverse and complex clinical data to characterize kidney obstruction, including renal images, renogram curves and pharmacokinetic parameters. Due to lack of well-established and objective guidelines for analyzing these data, clinical judgment of kidney obstruction heavily depends on the experience of the radiologist and typically has poor inter-rater agreement. A statistical model will be presented to effectively integrate information from different modalities and produce accurate interpretations and stable predictions of kidney obstruction.