Ph.D., Carnegie Mellon University (2017)
M.S., Carnegie Mellon University (2015)
B.A., Amherst College (2012)
My research focuses on performance modeling: developing mathematical solutions that predict system performance and inform system design. While my work is theoretical, I draw my research topics from the problems that arise in practice in computer systems. One common performance metric is latency--the time from when a job arrives to the system until it completes service--and providing low latency is a key goal of most systems. To do this, we need to answer questions such as "how many servers does our system need?", "how should we dispatch jobs to servers?", and "in what order should we schedule jobs to run?" My work involves designing dispatching and scheduling policies and mathematically analyzing these policies to predict their performance. One of my particular interests is in developing theoretical models of computer systems that accurately capture important features of real systems without sacrificing analytical tractability.
I teach both courses in the core theory sequence for the major, as well as the introductory sequence. In future semesters, I plan to offer electives on topics in theoretical computer science focusing on probability in computing, queueing theory, and the intersection between theory and systems.
I am also committed to broadening participation in computer science, and I enjoy teaching outreach courses targeted to underrepresented groups.
Awards and Honors
Siebel Scholar, Class of 2017
Google Anita Borg Memorial Scholarship, 2016
Carnegie Mellon School of Computer Science Alan J. Perlis Graduate Student Teaching Award, 2016
Carnegie Mellon Computer Science Department Teaching Award, 2015
NSF Graduate Research Fellowship, 2012-2015