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.
The US Army Research Office is looking to hire a new program manager, specifically with Topological Modeling and Analysis as one of the requested focus areas.Continue reading “US Army Research Office: Program Manager”
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
- Me, in MSU CMSE and Math
- Arjun Krishnan in MSU CMSE and BMB
- Beronda Montgomery in MSU BMB and MMG
- Aman Husbands in OSU MG
- Dan Chitwood in MSU CMSE and Horticulture
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!
Job no: 654988
Work type: Faculty/Academic Staff
Major Administrative Unit / College: College Of Natural Science
Department: Biochemistry & Molecular Biology Cns 10032098
Location: East Lansing
Categories: Full Time (90-100%), Fixed Term Academic Staff, Research/Scientific, Non-Union
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.
Doctorate -Math, Network Biology, Computation Biology
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.
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.
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
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@example.com, Southern Methodist University
Chair(s): Hengrui Luo, firstname.lastname@example.org, The Ohio State University
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
Everyone is welcomed to register for Joint Statistical Meeting (JSM) to join our virtual session!
We are pleased to announce that next year’s Young Topologists Meeting will take place between 12-16 July 2021 in Stockholm, jointly organized by the KTH Royal Institute of Technology and Stockholm University.
The intention of the conference is to create a setting in which young researchers in topology can meet each other and share their work. The program will consist of short talks given by the participants and three lecture series by invited speakers. This meeting serves as a replacement for the YTM 2020, which had to be cancelled because of the ongoing COVID-19 pandemic. In particular the invited speakers are the same ones that were invited for this year’s edition: Kathryn Hess (EPFL), Thomas Nikolaus (WWU Münster) and Karen Vogtmann (Cornell University and University of Warwick).
More information will be available soon on the conference website https://sites.google.com/view/ytm2021. We plan to open the registration mid-December.
If you have any questions, please do not hesitate to contact the organizers at email@example.com.
We look forward to seeing you in Stockholm!
As part of the Fields thematic program on Toric Topology and Polyhedral Products there will be two sets of Clay Lectures by
Gunnar Carlsson on 15 and 17 June
Shmuel Weinberger on 16 and 18 June
and an associated Workshop on Topological Data Analysis from 15-18 June.
All events are online. For more information and to register, please go to
Registration is free and all are welcome.
Best regards from the organizers:
Kathryn Hess, Frédéric Chazal and Umberto Lupo are curating an Article Collection published in Frontiers in Artificial Intelligence and entitled “Topology in Real-World Machine Learning and Data Analysis” (webpage). Its core mission is to promote the use of topological ideas and techniques as mainstream tools in data science.
We welcome contribution(s) to our Article Collection. Papers can be original research, reviews, or perspectives, among other article types. Deadlines are as follows:
- 17 August 2020 – Abstracts (soft deadline);
- 14 December 2020 – Manuscripts.
Upon publications, papers will be free to read for everyone.
There are processing charges associated to publishing with Frontiers in AI, but waivers can be applied for if your institution or grant does not cover Open Access fees.
Please get in touch if you would like to learn more about scope, deadlines, and publishing fees. Alternatively, you can sign up for participation directly from the Collection’s webpage.
Best wishes,Umberto, Kathryn, Frédéric
Dr. Veronica Ciocanel and Dr. Wasiur KhudaBuksh are organising a virtual mini-symposium on “Probabilistic and Topological Methods for Biological Data” as part of the SIAM conference on Mathematics of Data Science 2020. More information below.
Time: June 11, 2020 1-3pm EST (topology session) and 3-5pm EST (probability session)
Registration: Registration is free (and would take less than 30 seconds), but limited to 300 people. Link here:
Speakers in the topology session:
1. Francis Motta, Florida Atlantic University
2. Marilyn Vazquez, the Ohio State University
3. Veronica Ciocanel, the Ohio State University
4. Manuchehr Aminian, Colorado State University
Speakers in the probability session:
1. Wasiur KhudaBukhsh, the Ohio State University
2. Arindam Fadikar, Argonne National Lab
3. Pragya Sur, Harvard University
4. Yuekai Sun, University of Michigan
The talks will cover many different application areas ranging from microscopic cell biology to macroscopic epidemiology using tools from topology and probability theory.
Please register as soon as possible.
Aras Asaad writes:
I’m organizing a virtual mini-symposium titled ‘Topological Image Analysis’, which is part of SIAM2020 conference of Mathematics of Data Science.
To sign up for our online mini-symposium: Click at this site to get ZOOM details and a short description about this event: https://docs.google.com/forms/d/e/1FAIpQLSef7aJKpIKktF-mto1m1RAvd-ULJxIK2-uXsfqNCT3mu48M-Q/viewform
See attached, the flyer of the symposium for Date, Time, program schedule and speaker info’s.
Titles and Abstract of Talks can be found here:Session One: https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=68044 Session Two: https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=68045
The Dioscuri Center is a new research center for Topological Data Analysis, to start in July 2020 and led by Dr Pawel Dlotko.
The Dioscuri Center is seeking a postdoctoral researcher for a fixed-term 24-month position to work on developing mathematically rigorous geometry and topology based shape descriptors to solve important applied problems.
Application deadline: June 30, 2020