Tenure-track positions in machine learning and statistical learning at Umeå University

We are advertising two similar positions, one broad position in machine learning and one more focused position in nonconvex inference and learning. Both positions allow for independent research and come with generous start-up packages for setting up a research group. See:

https://www.mathjobs.org/jobs/list/16024

and

https://www.mathjobs.org/jobs/list/16023

We very much welcome applicants with mathematical/statistical backgrounds and interests.

With our very best wishes,

Åke Brännström & Jun Yu

Tenure-track Professorship, TU Graz, Austria

Graz University of Technology invites applications for an open-topic Tenure-Track Professorship with particular interest in basic and/or applied research on Information, Communication and Computing, especially also including Discrete & Computational Geometry and related areas.

Please find detailed information in the attached pdf-file and at the following link:
https://www.tugraz.at/go/professorships-vacancies/

Interested applicants are asked to send a detailed application in electronic form at the latest by September 30th, 2020 (date of email) to the Vice Rector of Research Horst Bischof, E-Mail: [email protected]

Position identification number: 31/R/PA/93070/20

TDA Postdoc at Michigan State University

One or two postdoc/research associate positions are available in the following fields:

-Machine learning/deep learning

-Mathematical molecular biology/biophysics/bioinformatics

-Topological data analysis

-Computational geometry

-Computational graphs/combinatorics

-Numerical PDEs

-Arbitrary combination of the above fields.

Ideal candidates will hold a Ph.D. degree in computational mathematics, or computer science, or computational biophysics, or bioinformatics, have extensive experience in code development and have demonstrated the potential for excellence in research. Applications consisting of a letter of application, current vita, and descriptions of research plans and teaching experience should submit via www.mathjobs.org/jobs/MSU/RAS2021. In addition, candidates should arrange for at least three letters of recommendation, which may be submitted on the same website. To receive full consideration the complete application must be received by August 14, 2020, but applications will be accepted until the positions are filled.

Please send an email to [email protected] to acknowledge your application.

CfP: WinCompTop Proceedings 2

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Call for Submissions
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We are calling for submissions for the Women in Computational Topology (WinCompTop) Second Workshop Proceedings, entitled “Research in Computational Topology 2,” which will be published as a volume in the Association for Women in Mathematics (AWM)-Springer series. The AWM-Springer series is a relatively new initiative by AWM (http://www.springer.com/series/13764) and the volumes are refereed proceedings at the level of the AMS Contemporary Mathematics standards.

This particular volume in the series is a follow-up to the second WinCompTop workshop, which was held at the Australian National University in Canberra, Australia in July 2019. We solicit submissions in the general area of applied and computational topology, broadly interpreted. Submissions should be reports on original work or possibly longer survey papers. While priority will be given to submissions co-authored by participants in the workshop, we expect to have significant room for additional papers, and welcome contributions from any WinCompTop network member or ally, provided that at least one of the authors on the submission identifies as a woman or gender diverse.
Please let us know if you plan to submit an article; you can do so by contacting Ellen Gasparovic ([email protected]), Vanessa Robins ([email protected]), and/or Kate Turner ([email protected]). 
The submission deadline is January 15, 2021. Please let us know if you have any questions, and we look forward to seeing your contributions!

Best,
Ellen Gasparovic, Vanessa Robins, and Kate Turner, editors

TDA Professorships in Rome, Italy

Paolo Salvatore writes: Dear topologists, I would like to draw your attention to a call of interest at the University of Rome Tor Vergata for some tenure-track / tenure positions in various subjects including data analysis, and in particular TDA. If you are interested send an email by July 15 as specified in the link below.

https://www.mat.uniroma2.it/Docs_avvisi/call-of-interest.pdf

TDA Postdoc at Michigan State University

Elizabeth Munch writes:

We have recently gotten word of funding for a postdoc position here at MSU to study plant morphology and gene networks through the lens of topological data analysis. The successful candidate will be housed in the Dept of Biochemistry and Molecular Biology, but the project includes collaborators from multiple departments including

The official job posting is here: https://careers.msu.edu/cw/en-us/job/503667/research-associatefixed-term

The relevant information is copied below. 
I would be happy to answer any questions you might have, so please feel free to get in touch! 
Best,Liz

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Job no: 654988
Work type: Faculty/Academic Staff
Major Administrative Unit / College: College Of Natural Science
Department: Biochemistry & Molecular Biology Cns 10032098
Salary: 48671.00
Location: East Lansing
Categories: Full Time (90-100%), Fixed Term Academic Staff, Research/Scientific, Non-Union

Position Summary

All data has shape, and embedded within all shapes is data that can be extracted. Shapes can be obvious and apparent to the eye, like the morphology of plants. Or, shapes can be more abstract, like that of a gene expression network. We are seeking an individual passionate about applying Topological Data Analysis (TDA), a mathematical method that measures shape, to the plant sciences. The successful candidate will be a pioneer, applying state-of-the-art data science techniques and mathematical approaches to the analysis of intensive datasets and predictive modeling.

The project is based at Michigan State University between the laboratories of Professors Beronda Montgomery, Arjun Krishnan, Elizabeth Munch, and Dan Chitwood together with collaborator Aman Husbands (Ohio State University). The postdoc will work with all five PIs, who offer complementary scientific and mathematical expertise. Between the PIs, the candidate will have access to expertise including Topological Data Analysis, network biology, machine learning approaches, bioinformatics, molecular biology, and plant growth and development. The PIs are committed to the career development of the candidate who will be mentored in line with their professional goals.

We live in extraordinary times during the COVID-19 pandemic. The circumstances of employment, including start date, possibility of remote work, and access to on-campus resources, will be discussed with the candidate to accommodate their health, well-being, and success on the project.

The post-doc will lead data analysis efforts in: 1) creating and processing plant growth time lapse images from 3D X-ray Computed Tomography (CT) data, 2) processing corresponding gene expression time series data using RNA-Seq and constructing gene networks, and 3) applying Topological Data Analysis (TDA) to plant morphology and gene networks, with the long-term goal of predictively modeling each from the other using machine learning approaches. This project will focus on the model plant Arabidopsis, utilizing accessions with contrasting developmental stability and leaf morphology, as well as differing light regimens that elicit plastic changes in plant growth and gene expression.

Required Degree

Doctorate -Math, Network Biology, Computation Biology

Minimum Requirements

A PhD with a strong background in mathematics, network biology, computational biology, computer science, machine learning, or other closely related fields. Good communication skills, willingness to collaborate openly, give/take constructive feedback, and sustain a friendly and collegial workplace environment.

Desired Qualifications

Programming experience in Python, R, and/or C++ with version control (Git), and experience with high-performance cluster computing.

Required Application Materials

A CV, 2) A statement of your research interests (≤ 500 words), and 3) Names & contact information for three references.

Website

https://bmb.natsci.msu.edu/

MSU Statement

Michigan State University has been advancing the common good with uncommon will for more than 160 years. One of the top research universities in the world, MSU pushes the boundaries of discovery and forges enduring partnerships to solve the most pressing global challenges while providing life-changing opportunities to a diverse and inclusive academic community through more than 200 programs of study in 17 degree-granting colleges.

Advertised: Jun 16, 2020 Eastern Daylight Time
Applications close: Nov 2, 2020 Eastern Standard Time

Online workshop: Optimal transport, topological data analysis and applications to shape and machine learning

Dear all,

We wanted to bring to your attention an upcoming workshop — July 27 to
July 31 — on applied topology, optimal transport and machine learning.
These are rapidly-developing verticals with budding interactions that
some of you might find interesting. The workshop will be entirely
online, and hosted through MBI-Ohio State. More information on events,
schedule and how to register can be found here:

https://mbi.osu.edu/events/optimal-transport-topological-data-analysis-and-applications-shape-and-machine-learning

Hope to see you there.

Jose Perea
Facundo Memoli
Tom Needham
Nicolas Garcia-Trillos

TDA at JSM

 ( https://ww2.amstat.org/meetings/jsm/2020/onlineprogram/ActivityDetails.cfm?SessionID=219282 )

Time: Thursday, August, 6th, 2020, 10:00 AM – 11:50 AM Eastern Daylight Time (EDT)

Place: Virtual Joint Statistical Meeting 2020 (https://ww2.amstat.org/meetings/jsm/2020/index.cfm).

Organizer(s): Chul Moon, [email protected], Southern Methodist University

Chair(s): Hengrui Luo, [email protected], The Ohio State University

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10:05 AM         Solution manifold and Its Statistical Applications

Speaker. Yen-Chi Chen, University of Washington

Abstract. A solution manifold is the collection of points in a d-dimensional space satisfying a system of s equations with s<d. Solution manifolds occur in several statistical problems including hypothesis testing, curved-exponential families, constrained mixture models, partial identifications, and nonparametric set estimation. We analyze solution manifolds both theoretically and algorithmically. In terms of theory, we derive five useful results: the smoothness theorem, the stability theorem (which implies the consistency of a plug-in estimator), the convergence of a gradient flow, the local center manifold theorem and the convergence of the gradient descent algorithm. To numerically approximate a solution manifold, we propose a Monte Carlo gradient descent algorithm. In the case of likelihood inference, we design a manifold constraint maximization procedure to find the maximum likelihood estimator on the manifold. We also develop a method to approximate a posterior distribution defined on a solution manifold.

10:25 AM         Persistent Topological Descriptors for Functional Brain Network

Speaker. Hyunnam Ryu, University of Georgia; Nicole Lazar, University of Georgia

Abstract. We compare the topological features of functional brain networks. In general, functional brain networks are dealt with in an elementwise manner based on the connectivity matrix as part of network data analysis. This tends to ignore the higher-order topology of the network, which can have significant implications. In recent studies, researchers have been interested in topological data analysis. Persistent homology is known to be useful for studying dynamic topological invariants hidden in complex data obtained from topological space. Analysis using persistent homology not only captures topological features that could be overlooked in the network data analysis but also addresses threshold selection problems commonly found in network data analysis.

We use persistent homology to compare the topological features of brain networks. We construct a brain network from the fMRI time series BOLD signal and calculate the persistent homology through the weighted brain network. Also, we compare the summarized topological features of different subject groups by calculating the persistence landscape.

10:45 AM         Uncovering the Holes in the Universe with Topological Data Analysis

Speaker. Jessi Cisewski-Kehe, Yale University

Abstract. The large-scale structure (LSS) of the Universe is a spatially complex web of matter that is difficult to analyze without losing potentially important information, but can help to constrain the underlying cosmological model that describes the Universe. Topological Data Analysis (TDA) is especially suitable for such weblike data and we have used this framework to visualize, define, and do inference on known (i.e., voids) and new (i.e., filament loops) cosmological structures. 

During this talk, I will discuss how TDA can be used to uncover cosmological structures.  The features on a persistence diagram represent homology group generators (connected components, loops, voids, etc.), which are not uniquely defined back in the dataset. However, having a way to visualize the generators in the dataset can be useful to better understand the data and to possibly determine the physical meaning of the structure. This led to a new procedure called “Significant Cosmic Holes in Universe” (SCHU) for defining representations of homology group generators in a cosmological survey, such as the Sloan Digital Sky Survey galaxy survey.  Cosmological voids correspond to the second homology group generators, and we also define a new class of voids based on the first homology group generators, which we call filament loops.

Persistence diagrams can also be used in hypothesis tests in order to make statistical comparisons between complicated spatial structures such as LSS.  I will present some developments using a two-sample hypothesis testing framework to distinguish LSS under different cosmological assumptions (e.g., cold dark matter vs. warm dark matter).

11:05 AM         Confidence Band for Persistent Homology

Speaker. Jisu Kim, INRIA

Abstract. Topological Data Analysis generally refers to utilizing topological features from data. For this talk, I will focus on persistent homology, which quantifies the salient topological features of data. I will present how the confidence band can be computed for determining the significance of the topological features in the persistent homology, based on the bootstrap procedure. First, I will present how the confidence band can be computed for the persistent homology of KDEs (kernel density estimators) computed on a grid. In practice, however, calculating the persistent homology of KDEs on d-dimensional Euclidean spaces requires to approximate the ambient space to a grid, which could be computationally inefficient when the dimension of the ambient space is high or topological features are in different scales. Hence, I will consider the persistent homology of KDE filtrations on Rips complexes as an alternative. I will describe how to construct an asymptotic confidence set for the persistent homology based on the bootstrap procedure. Unlike existing procedures, this method does not heavily rely on grid-approximations, scales to higher dimensions, and is adaptive to heterogeneous topological features.

11:25 AM         Discussant: Chul Moon, Southern Methodist University

11:45 AM         Floor Discussion and Follow-ups

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Everyone is welcomed to register for Joint Statistical Meeting (JSM) to join our virtual session!