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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
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Blog Post number 4
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Blog Post number 1
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portfolio
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prof
Algebraic and Combinatorial Phylogenetics
July 2017, Barcelona, Spain
Attended the conference and course on Semialgebraic statistics, latent tree models, and phylogenetics.
Deep Learning specialization
February 2019, Online course by deeplearning.ai
Took online specialization on Coursera.
ASA New Mexico Chapter Annual Meetings
April 2019, Santa Fe, New Mexico
Attended ASA chapter annually meetings since 2016. Gave a talk in 2018.
Evolution meetings
June 2019, Providence, Rhode Island
Gave a talk at Evolution 2019.
Joint Statistical Meetings
July 2019, Denver, Colorado
Presented a poster at JSM 2019.
Women in Statistics and Data Science
October 2019, Seattle, Washington
Attended the conference and the Introduction to Spark with R course.
New Mexico Big data and Analytics Summit
October 2019, Albuquerque, New Mexico
Attended the Image Analysis and Biomedical applications tracks.
publications
Heuristics for unrooted, unranked, and ranked anomaly zones under birth-death models
Anastasiia Kim and James DegnanMolecular Phylogenetics and Evolution (under review), 2020
In this article, we introduce several heuristic approaches to infer whether species trees are in anomaly zones when it is difficult or impossible to compute the entire distribution of gene tree topologies.
PRANC: ML species tree estimation from the ranked gene trees under coalescence
Anastasiia Kim and James DegnanBioinformatics, 2020
View
PRANC is a computational framework to work with the ranked gene trees. PRANC performs a heuristic search from the initial trees to find a ML species tree.
Probabilities of unranked and ranked anomaly zones under birth-death models
Anastasiia Kim, Noah Rosenberg, and James DegnanMolecular Biology and Evolution, 2020
View ArXiv
In this paper, we study how the parameters of a species tree simulated under a constant rate birth-death process can affect the probability that the species tree lies in the anomaly zone. We derive the lower bound of the probability of the species tree being in an unranked anomaly zone with n leaves for large speciation rate $\lambda$, and we show that this lower bound approaches 1 as n $\rightarrow \infty$ and $\lambda \rightarrow \infty$.
talks
Maximum Likelihood Inference of Species Trees from Ranked Gene Trees Under the Coalescent Model
Published:
teaching
Intro to Statistics (Fall 2014, Spring 2015, Summer 2015, Fall 2015, Spring 2016)
Stat 145/Math 1350, UNM, Department of Mathematics and Statistics, 2016
Class Times (Spring 2016): TR 2 - 3.15
Book: The Basic Practice of Statistics (7th Edition), by Moore, Notz and Fligner
Syllabus and Schedule
Elements of Mathematical Statistics and Probability Theory (Spring 2020)
Stat 345, UNM, Department of Mathematics and Statistics, 2020
Class Time (DSH 225): MWF 9 - 9.50 am (Zoom meeting id on learn)
Office Hours (SMLC 319): F 3.30 pm Anastasiia (Zoom id on learn)
Discussion/Tutoring (DSH 326): W 2.30 pm Hasan, F 2 pm Jared (Zoom ids on learn)
Syllabus
Piazza link: piazza.com/unm/spring2020/stat345
Recorded Kaltura class videos, Zoom meetings ids, HWs submission : UNM Learn