Research

Research interets

My current research interest is Geometric and Topological Data Analysis, and my PhD supervisor's are Dr Kate Turner and Professor Stephan Tillmann. I completed a Bachelor of Arts (Honours) at the University of Sydney, and my Honours Thesis was in Algebraic Geometry, supervised by Dr Emma Carberry, after which I completed a Master of Mathematical Sciences at ANU.
In particular, I am intersted in questions focusing on object recovery and object classification. These questions arise naturally in many areas of science, in particular biology.

Writings

Yossi Bokor, Katharine Turner, Christopher Williams, Reconstructing linearly embedded graphs: A first step to stratified space learning. Foundations of Data Science, doi: 10.3934/fods.2021026.
Yossi Bokor, Daniel Grixti-Cheng, Markus Hegland, Stephen Roberts, and Katharine Turner, Stratified Space Learning: Reconstrucing Embedded Graphs (2019).
Yossi Bokor, Honours Honours Thesis, The University of Sydney (2017).

Code

Skyler is Julia package which identifies the coarsest abstract graph structure underlying a point cloud, and modells it. Currently, we are restricted to graphs with linear edges which satisfy conditions detailed below. See the paper 'Reconstructing linearly embedded graphs: A first step to stratified space learning'.
DiscretePersistentHomologyTransform is a Julia package for calculating the Persistent Homology Transform.
I have contributed to Eirene, a Julia package for persistent homology.

Talks

October 2022: topology seminar at the School of Mathematics and Statistics, University of Melbourne.
Title: Geometric and Topological Data Analysis: Investigating and summarising the shape of data
Abstract: The shape of data can provide many insights, and is useful in both reconstructing and classifying objects. In this talk, we look at some examples of reconstruction and classification problems.
May, 2021: Asia Pacific Seminar on Applied Topology and Geometry
Title: Learning linearly embedded graphs
Abstract: In this talk, we consider the simplest class of stratified spaces - linearly embedded graphs. We describe an algorithm that learns the abstract structure of an embedded graph. We use tools and inspiration from computational geometry, algebraic topology, and topological data analysis and show the correctness of the identified abstract structure under assumptions on the embedding. The algorithm is implemented in the Julia package Skyler.
September, 2020: seminar in the Applied Topology Seminar, Mathematical Institute, University of Oxford
Title: Differentiating Lychees and Grapes
Abstract: Distinguishing classes of surfaces in ℝn is a task which arises in many situations. There are many characteristics we can use to solve this classification problem. The Persistent Homology Transform allows us to look at shapes in ℝn from Sn−1 directions simultaneously, and is a useful tool for surface classification. Using the Julia package DiscretePersistentHomologyTransform, we will look at some example curves in ℝ2 and examine distinguishing features.
June, 2020: seminar in the Graduates Reminisce Online On Topology (GROOT) Summer Seminar. A recording can be found on YouTube.
Title: What Doughnuts Tell Us About Data: An Introduction to Topological Data Analysis
December, 2019: seminar at the Institute for Mathematical Stochastics, University of Göttingen.
December 2019: seminar at EPFL Applied Topology Seminar.
December 2019: talk at MODSIM 2019, about the paper Stratified Space Learning: Reconstrucing Embedded Graphs.
July 2019: talk at the Young Topologists Meeting 2019, in Lausanne, about Stratified Space Learning.
March 2019: seminar to the School of Mathematical and Physical Sciences, University of Newcastle in March, 2019.
TItle: What Doughnuts Tell Us About Data: An Introduction to Topological Data Analysis
August 2017: seminar to the Mathematics Postgraduate Society at The University of Sydney, about resolving singularities of plane algebraic curves.