Anna KirkpatrickGraduate Student
George Institute of Technology
About MeI am a sixth year student in the interdisciplinary Algorithms, Combinatorics, and Optimization program at Georgia Tech. My advisors are Cassie Mitchell and Prasad Tetali. I am fortunate to have been supported financially as an NSF Graduate Research Fellow.
You can find some of my work on my Github profile.
ResearchMy primary research interests are in discrete mathematical biology. My current work includes randomized algorithms for computation on knowledge graphs and Markov models for progression of Alzheimers Disease. I have also studied RNA secondary structures through a combinatorial lens, with techiniques including Markov chain-based sampling and analytic combinatorics. My other research interests include computational complexity questions on discrete structures and the combinatorics of substructure occurrence.
Here is my resume.
Papers and preprints
On the Asymptotic Distributions of Classes of Subtree Additive Properties of Plane Trees under the Nearest Neighbor Thermodynamic Model with Chidozie Onyeze.
RNAStructViz: Graphical base pairing analysis with Maxie Schmidt and Christine Heitsch
Markov Chain-based Sampling for Exploring RNA Secondary Structure under the Nearest Neighbor Thermodynamic Model with Kalen Patton,
The challenge of RNA branching prediction: a parametric analysis of multiloop initiation under thermodynamic optimization with Svetlana Poznanović, Fidel Barrera-Cruz, Matthew Ielusic, and Christine Heitsch.
The complexity of counting poset and permutation patterns with Joshua Cooper.
Critical sets for sudoku and general graph colorings with Joshua Cooper.
PersonalI have a passion for assistive technology and have worked on (and used) multiple open source projects, including OptiKey and Mathfly. You can find my open source work on my Github profile.
Contact InfoEmail: email@example.com
Office: Skiles 127A
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1650044. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.