I’m a PhD candidate in Statistics & Data Science at Carnegie Mellon University, working with Brian Junker. I received my MS in Statistics from CMU in 2015, and my BA in Mathematics with a minor in Computer Science in 2014 at the College of Saint Benedict.
I am primarily interested in better understanding individual differences among decision-makers, particularly in high-stakes situations. I work with the center for statistics and applications in forensic evidence (CSAFE), and am adapting psychometric models for forensic science settings including lineup design and proficiency exams. I have also worked on probabilistic graphical models for combining evidence and the application of new statistical methodology and experimental design for eyewitness identification procedures.
I am also an active member of the statistics education (StatEd) research group at Carnegie Mellon. We are currently working on redesigning our introductory statistics course to include modern data science concepts, active learning, and adaptive material. Part of this redesign includes developing a more effective assessment to identify misconceptions among introductory-level students.
Broadly speaking, I am interested in data visualization, latent variable models, bayesian methods, social science applications, and statistics education.
My CV was last updated November 2018. Selected papers and presentations appear below.
Amanda S Luby and Joseph B Kadane, “Proficiency testing of fingerprint examiners with Bayesian Item Response Theory,” Law, Probability & Risk, 2018. 17(2), 111-121.
Amanda S Luby, “Strengthening Analyses of Lineup Procedures: A log-linear model framework,” Law, Probability & Risk, 2017. 16(4), 241–257
“Opening up the court (surface) in tennis grand slams,” Shannon Gallagher, Kayla Frisoli, and Amanda Luby. Carnegie Mellon Sports Analytics Conference. Pittsburgh, PA. Honorable Mention - Reproducible Research Competition.
“Accounting for individual differences among latent print examiners using Item Response Theory,” Joint Statistical Meetings, 2018. Vancouver, BC.
“Identifying misconceptions of introductory data science using a think-aloud protocol,” P Burckhardt, P Elliott, C Evans, S Hyun, K Lin, A Luby, CP Makris, J Orellana, A Reinhart, J Wieczorek, R Yurko, G Weinberg, R Nugent. Electronic Conference on Teaching Statistics, 2018. https://www.causeweb.org/cause/ecots/ecots18/posters/3-10
“Proficiency Testing for Fingerprint Examiners: A Bayesian Approach,” Co-author: Joseph B. Kadane. International Conference on Forensic Inference and Statistics, 2017. Minneapolis, MN.
“Frameworks for complex evidential reasoning: statistical implications and comparitive assessment,” Advisors: Brian Junker and Anjali Mazumder. International Conference on Forensic Inference and Statistics, 2017. Minneapolis, MN.
“A Graphical Model Approach to Eyewitness Identification,” Advisor: Stephen E. Fienberg. Bayesian networks and argumentation in evidence analysis at the Isaac Newton Institute for Mathematical Science, 2016. Cambridge, UK.
I taught the summer session of Introduction to Probability Theory (36-225) in both 2017 and 2018, as well as the 2016 summer session of Experimental Design for Social and Behavioral Sciences (36-309) through the Carnegie Mellon University statistics department. I have worked as a teaching assistant for undergraduate statistics courses including Experimental Design (36-309), Statistical Graphics & Visualization (36-315), and Modern Regression (36-401). I was an undergraduate teaching assistant for calculus (MATH 119 & 120) at the College of Saint Benedict.
I designed a series of introductory probability lectures as a supplemental program for forensic science students participating in the CMU Summer Undergraduate Research Program (SURE) in both 2016 and 2017. I have also taught introductory sessions on probability for forensic scientists in Allegheny County.
I’ve participated in the Future Faculty Program through the Eberly Center, consisting of pedagogical seminars, independent projects, and teaching feedback consultations and observations. My seminar transcript, early course feedback report, and teaching observation memo are available upon request.
I am passionate about making STEM more inclusive, particularly Statistics & Data Science. I am a founding member and current co-president of CMU’s Women in Statistics group (WinS). I serve as a Women in Data Science (WiDS) ambassador, and helped plan the inaugural WiDS Pittsburgh @ CMU conference. I’m also a co-PI for an internal grant to establish a formal mentorship program housed in the Statistics & Data Science department at CMU. Kayla Frisoli, Shannon Gallagher, and I were awarded honorable mentions for the ASA’s Gertrude Cox Scholarship for our work with CMU’s Women in Statistics group.
I’ve represented the Statistics & Data Science department in the Graduate Student Assembly (GSA) since 2017. The GSA is the branch of student government that represents all graduate students at Carnegie Mellon. Its mission is to advocate for and support the diverse needs of all Carnegie Mellon University graduate students in their personal, professional, and public lives.
I also sit on the Dietrich College Council as the graduate student representative. All academic policy decisions for CMU’s Dietrich College are made by the College Council, which is made up of the dean, associate deans, department heads and three student representatives: one graduate student and two undergraduate.