Calendar

Mar
9
Mon
ISITA talk
Mar 9 @ 1:30 pm – Mar 9 @ 2:30 pm

The ISITA 2020 conference on coding and information theory
will be held at Ko Olina on October 24-27, 2020.
     http://www.isita.ieice.org/
The organizers are meeting in Hawaii this week, and have
agreed to give two talks at UH:

   Friday, March 6, 1:30pm–2:15pm in Keller Hall 413
   Speaker: Prof. Akiko Manada
   Shonan Institute of Technology

   Monday, March 9, 1:30pm–2:15pm in Keller Hall 413
   Speaker: Prof. Takayuki Nozaki
   Department of Informatics,
   Yamaguchi University

Each talk will be followed by refreshments and a problem
session. You are cordially invited to attend.

Mar
13
Fri
Logic seminar: Jack Yoon
Mar 13 @ 2:30 pm – 3:30 pm
Jun
26
Fri
Jack Yoon (PhD candidate): Grätzer-Schmidt theorem in Arithmetical Transfinite Recursion
Jun 26 @ 2:00 pm – 3:00 pm
Jun
29
Mon
Sana Habib (MA candidate): Visualizations of Schottky Groups
Jun 29 @ 2:00 pm – 3:00 pm
Aug
11
Tue
Yulia Kravchenko specialty exam
Aug 11 @ 2:00 pm – 3:00 pm
Aug
24
Mon
Fall semester instruction begins
Aug 24 all-day
Sep
7
Mon
Labor day
Sep 7 all-day
Oct
16
Fri
Data Science seminar
Oct 16 @ 2:00 pm – 3:00 pm


Join the Hawai‘i Data Science Institute for another Data Science Friday seminar titled “Bayesian Topological Learning for Complex Data Analysis” presented by Assistant Professor of Mathematics Dr. Farzana Nasir on October 16, 2020 at 2 pm on Zoom. 

Please find more information below and on the attached flyer. 

Zoom registration: http://go.hawaii.edu/39f


Abstract: Persistent homology is a tool in topological data analysis for learning about the geometrical/topological structures in data by detecting different dimensional holes and summarizing their appearance disappearance scales in persistence diagrams. However, quantifying the uncertainty present in these summaries is challenging. In this talk, I will present a Bayesian framework for persistent homology by relying on the theory of point
processes. This Bayesian model provides an effective, flexible, and noise-resilient scheme to analyze and classify complex datasets. A closed form of the posterior distribution of persistence diagrams based on a family of conjugate priors will be provided. The goal is to introduce a
supervised machine learning algorithm using Bayes factors on the space of persistence diagrams. This framework is applicable to a wide variety of datasets. I will present an application to filament networks data classification of plant cells.

Bio: Farzana Nasrin graduated from Texas Tech University with a Ph.D. in Applied Mathematics in August 2018. Her research interests span algebraic topology, differential geometry, statistics, and machine learning. Currently, she is holding an assistant professor position at UH Manoa in the Department of Mathematics. Before coming to UHM, she was working as a postdoctoral research associate funded by the ARO in mathematical data science at UTK. She has been working on building novel learning tools that rely on the shape peculiarities of data with application to biology, materials science, neuroscience, and ophthalmology. Her dissertation involves the development of analytical tools for smooth shape reconstruction from noisy data and visualization tools for utilizing information from advanced imaging devices.