Author: Henry Adams

TRIPODS Summer Bootcamp on Topology and Machine Learning, August 2018

We invite you to apply to attend the TRIPODS Summer Bootcamp on Topology and Machine Learning. The bootcamp will be hosted at ICERM during the week of August 6-10, 2018. Applications are currently being accepted, and will be accepted until July 5. For more information, please see the webpage at
https://icerm.brown.edu/tripods/tri18-2-tml/

Best,
Henry Adams, Colorado State University
Jeffrey Brock, Brown University
Melissa McGuirl, Brown University
Bjorn Sandstede, Brown University
Isaac Solomon, Brown University

Postdoctoral Research Associate: Large Scale Brain Network Computation

Postdoctoral positions are available for large-scale brain image and network analysis at the University of Wisconsin-Madison. The postdoctoral fellow will work with professor Moo K. Chung (www.stat.wisc.edu/~mchung) on developing new innovative statistical and machine learning methods for large scale brain images and networks.

Candidates should have received or expected to receive PhD degree or equivalent in mathematics, CS, EE, statistics, physics, biomedical engineering, psychology, neuroscience or related areas. Previous neuroimaging research experience is a plus but not necessary. Expertise in the following areas may be useful: matrix computation, time series analysis, manifold learning, topological data analysis, functional analysis, graph & network analysis.

Interested candidates should email CV (with the name of references) and two representative papers to Moo K. Chung (mkchung@wisc.edu).

GETCO 2018 Conference

Here is a conference announcement:

GETCO 2018
September 10-14, 2018
Oaxaca, Mexico
https://sites.google.com/view/geometricandtopologicalmethods/home
The GETCO conference series focus on applications of algebraic topology in computer science with special emphasis in concurrency, distributed computing, networking and other situations related to systems of sequential computers that communicate with each other. It is aimed at mathematicians and computer scientists working in or interested in these subjects, including researchers and graduate students.

A special issue of the Journal of Applied and Computational Topology will be dedicated to selected papers from the conference.

Committee
Armando Castañeda, Universidad Nacional Autónoma de México
Dmitry Feichtner-Kozlov, University of Bremen.
Eric Goubault, École Polytechnique, Paris.
Maurice Herlihy, Brown University, USA.
Ran Levi, University of Aberdeen.
Martin Raussen, Aalborg University.

Job opening in Applied and Computational Topology advertised by the Queen Mary, University of London

From Michael Farber:

Dear colleagues,

I want to attract your attention to the job opening in Applied and Computational Topology advertised by the Queen Mary, University of London (Lecturer/Senior Lecturer in Applied and Computational Topology – QMUL13442), see

https://webapps2.is.qmul.ac.uk/jobs/job.action?jobID=2940

The salary range is £40,865 – £60,109.

Closing date: 11 January, 2018.

Data science (especially topology position at The Henry M. Jackson Foundation

THE HENRY M. JACKSON FOUNDATION FOR THE ADVANCEMENT OF MILITARY MEDICINE

Position Description
Data Scientist

Position No: FLSA Status: Exempt
Grade: EEO Category/Job Group:

JOB SUMMARY We are seeking a Data Scientist to join the Austere environments Consortium for Enhanced Sepsis Outcomes (ACESO). ACESO aims to identify host-based markers capable of accurately diagnosing and prognosing patients with severe infections in austere settings and transitioning those markers to point-of-care assay platforms. The Data Scientist is responsible for analyzing complex data and developing insights through the use of statistical models, data mining, and data visualization techniques.

This position is based at The Henry M. Jackson Foundation (HJF) in Bethesda, Maryland, although alternate arrangements will be considered. HJF provides scientific, technical and programmatic support services for the worldwide ACESO program.

ESSENTIAL JOB DUTIES: 95% of time

1. Analyzes complex datasets including RNA sequence data, proteomic, phosphoproteomic, and metabolomics data. Applies advanced statistical and predictive modeling techniques and data visualization approaches. Develops innovative approaches to answer research questions.

2. Integrates and prepares large datasets, develops specialized database and computing environments as needed.

3. Provides subject matter expertise as needed, including recommendations on data collection and integration.

4. Communicates results on a regular basis with the science team and key stakeholders, and prepares presentations and reports as needed.

5. Performs other duties as required.

JOB SPECIFICATIONS:
Required Knowledge, Skills, and Abilities:
 Experience with complex datasets
 Proficiency in statistical analysis, forcasting/predictive analytics, and algorithm optimization.
 Experience with data mining/pattern recognition approaches; experience with topological data analysis preferred
 Strong programming skills
 Able to develop solutions to loosely defined problems
 Able to communicate effectively

Minimum Education/Training Requirements: PhD in mathematics, statistics, computer science or related field. At least 2 years relevant experience.

Physical Capabilities: Extended periods of sitting

Required Licenses, Certification or Registration: n/a

Supervisory Responsibilities/Controls: May provide guidance to junior analysts

Work Environment: Office or laboratory environment

Any qualifications to be considered as equivalents, in lieu of stated minimums, require the prior approval of the Director of Human Resources

Job Opening in Topological Data Analysis at Florida Atlantic University

Math Jobs add:
https://www.mathjobs.org/jobs/jobs/11058

FAU Jobs website:
https://jobs.fau.edu/applicants/Central?quickFind=62778

The Department of Mathematical Sciences at Florida Atlantic University invites applications for a tenure-track position at the assistant professor level in the area of topological data analysis, starting in August 2018.

The successful candidate will be expected to pursue research that complements current expertise in the department, which includes applications of computational homology to dynamical systems and topology of random manifolds. Research areas of particular interest for this position include, but are not limited to, mathematical foundations of TDA, geometric and topological methods in data science, computational topology, and interdisciplinary applications. Ideally the successful candidate will broaden the scope of mathematical research in these areas, forming collaborations within the department as well as conducting interdisciplinary research.

Responsibilities for this position will be teaching, scholarly research, and professional service. For additional information about the position, please contact the Chair of the Search Committee by email to mathsearch@fau.edu.

The Department of Mathematical Sciences at FAU offers a full range of undergraduate and graduate degree programs in mathematics. The department has more than forty full-time faculty members, approximately two-thirds in tenure-earning positions, and nearly fifty doctoral students. FAU is home to The Center for Cryptology and Information Security and The Center for Complex Systems and Brain Sciences. Both the Scripps Research Institute and the Max Planck institute have branches located near the Jupiter campus.

Applicants must possess a Ph.D. in Mathematics or a closely related field. Candidates in all areas of topological, geometric, and high-dimensional data analysis will be considered.

The successful candidate will be expected to teach effectively and direct research at both the undergraduate and graduate level, participate in interdisciplinary programs, and apply for external research funding.

The salary range is $65,000 – $75,000. For additional information about the position, please contact us by email to mathsearch@fau.edu.

This position is open until filled and may close without prior notice. Priority consideration will be given to applications received by January 1, 2018.

All applicants must apply electronically to the currently posted position on the Office of Human Resources’ job website (https://jobs.fau.edu/applicants/Central?quickFind=62778) by completing the Faculty, Administrative, Managerial & Professional Position Application and submitting the related documents. The position number that should be referenced while applying is #981754.

In addition, please arrange to have three letters of recommendation sent by first class mail to: Chair of the Search Committee, Department of Mathematical Sciences, Florida Atlantic University, 777 Glades Rd., Boca Raton, FL 33431 or by email to mathsearch@fau.edu.

A background check will be required for the candidate selected for this position.

Florida Atlantic University is an equal opportunity/affirmative action Institution, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. Individuals with disabilities, requiring accommodation, please call (561) 297-3057 – TTY 711.

Machine Learning Fellowship position for PhD students at SAS

The following link is from Ilknur Kabul, who is a manager for the Machine Learning group at SAS Institute in Cary, NC. Recently they became interested in topological data analysis for exploring large data sets. They’re opening a Machine Learning fellowship for next summer for graduate students to work in this area:

https://careers-sas.icims.com/jobs/3845/sas-summer-fellowships-in-machine-learning/job?mode=view

The position is funded. The program provides a salary and housing support for a twelve-week internship at SAS headquarters in Cary, North Carolina, during the summer of 2017.

Professorship at the University of North Carolina at Greensboro

The Department of Mathematics and Statistics at the University of North Carolina at Greensboro (UNCG) seeks applications for a tenure-track position in Computational Topology at the Assistant Professor rank beginning August 1, 2017. For more information, please see:

https://www.mathjobs.org/jobs/jobs/9007

UNCG_ComputationalTopology

Open Question: Lions and Contamination

I’d like to point to you an open problem that I find interesting. A good reference is the paper “How many lions are needed to clear a grid?” by Florian Berger, Alexander Gilbers, Ansgar Grüne, and Rolf Klein [1].

Disclaimer:  I would classify this problem as more combinatorial than topological.

Suppose we have a graph which is an \(n \times n\) grid. This graph contains \(n^2\) vertices, and the case \(n=5\) is drawn below.

5x5grid

We have \(k\) lions moving on this grid. At each time step a lion occupies a vertex, and between adjacent time steps a lion either stays put or travels across one edge to an adjacent vertex.

We also need to define the subset of vertices \(W(t)\) which are “contaminated” at time \(t\). A lion can clean a contaminated vertex, but in the absence of lions, the contamination spreads. At starting time \(t=0\) every vertex not occupied by a lion is contaminated; this gives \(W(0)\). How does the contaminated set update as the lions move? A vertex \(v\) is in \(W(t+1)\) if \(v\) is not covered by a lion at time \(t+1\) and either

  • \(v\) belongs to \(W(t)\), or
  • \(v\) has a neighbor \(u\) in \(W(t)\) such that no lion travels from vertex \(v\) to \(u\) between times \(t\) and \(t+1\).

Suppose you are given \(k\) lions. You get to choose the lions’ starting vertices at time zero in the grid – all other vertices begin contaminated. You get to pick how each lion moves at each time step. Can you design a way to clear all contaminated vertices from the grid?

If \(k \geq n\) then this problem is easy. At time zero simply line up the lions along the left-hand side of the grid, from top to bottom. At each time step, sweep each lion one step to the right. At time \(t=n-1\) the lions will be on the right-hand side of the grid, and there will be no contaminated vertices.

It is unknown whether \(k=n-1\) lions are sufficient to clear the grid or not. I would guess that most people think \(k=n-1\) lions are insufficient, but nobody has a proof!

An equivalent way to phrase this problem is to use a mobile evader instead of the set of contaminated vertices. Suppose our evader moves at the same speed as the lions: at each time step the evader occupies a vertex, and between adjacent time steps the evader either stays put or crosses one edge. The evader is caught if it occupies the same vertex or crosses the same edge as any lion. It is known that \(k\geq n\) lions can catch any such evader (say by sweeping from left to right), and it is unknown whether \(k=n-1\) lions are sufficient or not. To see the equivalence between the formulations using a mobile evader or contaminated vertices, note that \(W(t)\) is the set of all possible locations of a mobile evader at time \(t\).

This is one of those problems that is harder than it sounds. Upon first hearing it your reaction is that you will have a proof after one evening of hard work. A week later you still haven’t made much progress, and you’re a week behind on your normal research agenda. Consider yourself warned!

One reason why I classify this problem as more combinatorial than topological is that the details of the discretization matter. For example, see Figure 1 of [1] (Note – in this figure, a vertex of the \(n \times n\) grid is drawn as a square. This is their representation of a \(4 \times 4\) grid with 16 vertices, not a \(5 \times 5\) grid with 25 vertices). For a second example, see Figure 5 of [1]. In the 3d version of this problem, you might expect that \(n^2\) lions are necessary to clear an \(n \times n \times n\) grid. Figure 5 of [1] shows that this is false – 8 lions (which is less than 9) are sufficient to clear the \(3 \times 3 \times 3\) grid.

References

[1] Florian Berger, Alexander Gilbers, Ansgar Grüne, and Rolf Klein. How many lions are needed to clear a grid? Algorithms 2009, 2, 1069-1086.