TDA Postdoc, Cornell

The Greater Data Science Cooperative Institute (GDSC), jointly established by Cornell University (CU) and the University of Rochester (UR) as part of the NSF funded HDR TRIPODS program, seeks exceptional candidates for postdoctoral research positions at Cornell (note: UR has a simultaneous and coordinated, but separate search). Appointments are renewable up to a total period of three years. The target start date is August 1, 2020, but positions may be filled immediately.

GDSC is based on two founding tenets: that enduring advances in data science require combining techniques and viewpoints across electrical engineering, mathematics, statistics, and theoretical computer science; and, that data-science research must be grounded in an application domain. Cross-disciplinary research directions include: (i) Topological Data Analysis; (ii) Data Representation; (iii) Network & Graph Learning; (iv) Decisions, Control & Dynamic Learning; and, (v) Diverse & Complex Modalities. GDSC specifically aims to consider applications in medicine and healthcare, a major strength of CU and UR, and one for which advances in data science can have a direct, positive impact on society. Applicants should have completed their PhD in a discipline listed above (or related) by the time of their appointment. Some background or interest in biomedicine and/or healthcare would be an advantage.

Cornell appointed postdocs will be mentored by at least two GDSC members, including one from the Cornell GDSC leadership team: David S. Matteson, Aaron Wagner, Qing Zhao, David Bindel and Gennady Samorodnitsky. The Rochester GDSC leadership team includes Mujdat Cetin, Daniel Gildea, Tong Tong Wu, Alex Iosevich and Daniel Stefankovic; and GDSC includes 17 additional founding senior personnel across CU and UR (see: gdsc.cornell.edu for more information).

Any questions about the Cornell positions can be sent to David S. Matteson (matteson@cornell.edu); and please contact Mujdat Cetin (mujdat.cetin@rochester.edu) for questions about the UR positions. All applicants must submit a cover letter, a curriculum vitae including a list of publications, research and teaching statements, and arrange to have two reference letters submitted electronically. Up to two relevant research publications may also be submitted. Application materials must be submitted at: https://academicjobsonline.org/ajo/jobs/15856

Evaluation of applicants will begin February 15, 2020 and continue until the position is filled.

Differential Geometry in Computer Vision and Machine Learning

We are organizing the 5’th International workshop on Differential Geometry in Computer Vision and Machine Learning (DiffCVML) in conjunction with CVPR 2020 to be held in Washington, Seattle in June 2020. We invite contributions for full length papers from you and your colleagues on the broad topic of topological methods and differential geometric approaches for computer vision, machine learning, and medical imaging. 

We are planning a full day workshop comprising both oral and poster presentations. You can find more information at https://diffcvml.org/2020 along with the call for papers at http://diffcvml.org/2020/cfp_DiffCVML2020.pdf.

The workshop will be held on one of the days from June 16’th to 18’th June. The paper should be 8 pages in length excluding references and the submission date (tentatively) is March 8’th 2020. We will send out further emails when CVPR informs us the final date of the workshop.

The workshop will feature keynote lectures by the following distinguished speakers:

Dr. Tamal Dey, Ohio State University, USA 
Dr. Guido Montufar, University of California Los Angeles, USA
Dr. Elizabeth Munch, Michigan State University, USA
Dr. Lek-Heng Lim, University of Chicago, USA
Dr. Pavan Turaga, Arizona State University, USA

We aim to foster interactions between engineers, mathematicians, statisticians, and computer scientists but also domain experts in the field of computer vision, biology, and medicine. 

Please email me if you have any questions. 
Thank you,
Shantanu Joshi

On behalf of the organizing committee, DiffCVML 2020

Shantanu H. Joshi, University of California Los Angeles, USA
Pavan Turaga, Arizona State University, USA
Vittorio Murino, IIT Genoa, and the University of Verona, Verona, Italy
Baba Vemuri, University of Florida, USA
Anuj Srivastava, Florida State University, USA
https://diffcvml.org/2020

Air Force Research Lab: Summer of TDA Internship

This summer in Dayton OH, our group at AFRL is hosting a summer of TDA 10 week internship program. During these 10 weeks interns will work on DOD relevant data sets exploring the use of TDA to develop explainable artificial intelligence systems as well as user interface visualization dashbroads.  This internship is with the Air Force Research Lab 711th Human Performance Wing, led by Dr. Ryan Kramer and is administered through KBR Inc., a DOD contracting and support company.  The KBR link to the job description and link to apply is below.

https://kbr.wd5.myworkdayjobs.com/KBR_Careers/job/Dayton-Ohio/Topological-Data-Analyst-Intern_R2008281

WinCompTop@ATMCS

The WinCompTop@ATMCS event, organized by Erin Chambers, Ellen Gasparovic, and Yusu Wang, will be held June 6-7 at The Ohio State University.  Any member of the community (man or women) is welcome and encouraged to join us for this meeting.  In addition to talks by participants from the prior WinCompTop workshops and members of the community, there will also be a panel discussion, a dinner or social event, and open time for working groups to meet (or start new projects).  Funding is available to help with travel, particularly for junior participants.  

If you were a participant of one of the first two workshops and would be interested in talking about your group’s project or follow-up work, please get in touch with an organizer! 

After scheduling these talks, we plan to add additional talk slots for members of the community, but we hoped to first showcase some of the fantastic research that came out of WinCompTop 1 and 2.

Stay tuned for funding details and a program soon, and we hope to see many of you there!

Postdocs and PhD positions in TDA at Swansea

1. Postdoc in Topological Data Analysis (part of the EPSRC-funded Oxford-Livepool-Swansea Centre for Topological Data Analysis)
Closing date: 29 February 2020

https://www.swansea.ac.uk/personnel/jobs/details.php?nPostingId=64919&nPostingTargetId=81292&id=QHUFK026203F3VBQB7VLO8NXD&LG=UK&mask=suext

2. Postdoc in computational tropical geometry
(Funded by Yue Ren’s UKRI fellowship)
Closing date: 1 March 2020

https://www.swansea.ac.uk/personnel/jobs/details.php?nPostingID=65099&nPostingTargetID=81412&option=52&sort=DESC&respnr=1&ID=QHUFK026203F3VBQB7VLO8NXD&JOBADLG=UK&Resultsperpage=20&lg=UK&mask=suext

These are both 3-year positions, 100% research focussed with no obligatory teaching load.

3. PhD scholarship in topological data analysis
Closing date: 28 February 2020
(open only to EU/UK residents)
https://www.swansea.ac.uk/postgraduate/scholarships/research/mathematics-phd-topological-data-biomedical.php

4. Additional PhD funding opportunities at Swansea though CDTs that could cover projects in topological data analysis (both of these are unfortunately open only to EU/UK residents:

• EPSRC multi-disciplinary Centre for Doctoral Training in Human-Centred AI and Data Science.   https://www.swansea.ac.uk/science/epsrc-centre-for-doctoral-training/

• UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing (AIMLAC)  http://cdt-aimlac.org/

Computational & Algorithmic Topology, Sydney (CATS 2020)

Computational & Algorithmic Topology, Sydney (CATS 2020)

The University of Sydney, 27-31 July 2020

https://sites.google.com/view/cats-2020


Abstract:

This workshop at the University of Sydney will bring together experts and emerging researchers from Australia, the USA and Europe to report on recent results and explore future directions in computational and algorithmic topology and related areas. There will be a focus on problems in computational geometric topology and computational algebraic topology. This workshop aims to stimulate interaction between researchers in order to bring about new collaborations on difficult problems that cannot be tackled from one viewpoint alone.

We plan to have talks by our invited speakers in the mornings, and open problem sessions, a poster session, and plenty of time for collaboration in the afternoons.

Invited Speakers:

Herbert Edelsbrunner (Institute of Science and Technology Austria)
Brittany Fasy (Montana State University)
Gregory Henselman (Princeton University)
Feng Luo (Rutgers University)
Zuzana Patáková (Institute of Science and Technology Austria)
Jessica Purcell (Monash University)
Vanessa Robins (Australian National University)

Organizers:

Jonathan Spreer (The University of Sydney)Stephan Tillmann (The University of Sydney)
Katharine Turner (Australian National University)

Applied Machine Learning Days 2020

“AI & Topology” at the Applied Machine Learning Days 2020, EPFL

This year’s Applied Machine Learning Days will take place on January 27-29, 2020 at EPF Lausanne (Switzerland).  The goal of this conference is to bring together the best people in the field to talk about a variety of topics in the practice of machine learning — from technical developments to applications in social, scientific or other domains.  Previous editions featured keynote speakers such as Jeff Dean and Christopher Bishop, researchers from academic institutions and industry (Google AI and DeepMind, Facebook AI, IBM Research, Microsoft Research, and others), as well as public figures such as Zeynep Tufekci and Garry Kasparov.

This is the fourth iteration of the event, with more than 1500 people expected to attend each day this time.  The event will feature more than 20 domain-specific tracks and, over the weekend preceding the main event, a set of workshops, challenges, and other “hands-on” events.

L2F and the Laboratory for Topology and Neuroscience have the pleasure to announce this year’s AI & Topology track which will take place on Tuesday 28 January.  The purpose of the track is to showcase a variety of successful ideas and concrete applications of topology, geometry or other abstract mathematics to machine learning and data analysis.  It will feature the following speakers:

  • Kathryn Hess Bellwald (EPFL)
  • Frédéric Chazal (Inria)
  • Vitaliy Kurlin (University of Liverpool)
  • Bastian Rieck (ETH)
  • Leland McInnes (Tutte Institute)
  • Nicole Sanderson (LBNL)

Additionally, there will be a panel discussion with a Q&A session. The talks will be uploaded to YouTube soon after the event.

A range of tickets for the AMLD event (including discounts for students, academics, non-profits and startups) are still available here.

TDA at the Joint Mathematics Meetings 2020

Here are some of the talks that I (Mikael Vejdemo-Johansson) discovered when looking through the programme.

Wednesday

Thursday

Friday

Mikael Vejdemo-Johansson and Henry Adams are organizing a full-day special session on applied topology.

Room 113.

Also this day are

Saturday

Hope to see you at the #JMM2020

Block Course: Stochastic Topology – Berlin March 16-20 2020

———————-
CALL FOR PARTICIPATION
———————-

We wish to announce the upcoming block course


  Stochastic Topology

  TU Berlin (Germany), March 16-20, 2020

https://www3.math.tu-berlin.de/mathplus/TES-Summer2020/TES_Block_Course_Stochastic_Topology.html


Block Course Lecturers:
———————–

  Omer Bobrowski (Technion)
  Matthew Kahle (Ohio State, TU Berlin)
  J. Andrew Newman (TU Berlin)
  Yuval Peled (NYU Courant Institute)

Scope:
——

Stochastic topology began in the mid-twentieth century with the study
of random graphs, that is, random 1-dimensional spaces. More recently,
several models of random high-dimensional simplicial complexes have been
introduced. Some of the most important questions in stochastic topology
have to do with establishing thresholds for various topological properties
within these models. This course will begin with an overview of the
standard combinatorial and geometric models, and then we will explore
some of the techniques that have been used to study them.

The block course is organized within the third Berlin Thematic Einstein
Semester on the “Geometric and Topological Structure of Materials”,
devoted to recent developments in the field of computational materials science.

Participation:
————-

  free.

Registration:
————-

  please register via

https://forms.gle/QtG94ewFLJ9gdFiv6

Financial Support:
——————

There is limited financial support for attendance.

The deadline for support applications (via the registration form)
is

  ***  January 23, 2020  ***

  (Notification is by January 30, 2020.)

We are hopeful of encouraging people from a wide variety of scientific
and mathematical backgrounds to attend. Any queries can be addressed
to the organizing committee at

tes-summer2020@math.tu-berlin.de

Spring School on Data Science and Quantum Computing – London 29 March – 2 April 2020

The Spring School on Data Science and Quantum Computing is being organised by the Institute of Applied Data Science at Queen Mary University of London. It takes place from Sunday 29th March to Thursday 2nd April 2020. The event includes:   

    Spring School on Data Science: 29-31 March 2020,

     Workshop on Quantum Computing: 1 – 2 April,  2020. 

The Spring School and Workshop welcomes applications from PhD students and early career researchers with interests  in data science. The School will cover a broad range of topics such as democracy and data, mathematical mechanisms of social choice, natural language processing, topological data analysis, health AI, mathematics of cancer amongst others. There will be special emphasise on quantum computing and quantum information which shall be covered by several introductory mini-courses preparing the participants for the Workshop on Quantum Computing.

The event will take place at Cumberland Lodge, Windsor.

For more information and registration please visit 

https://sites.google.com/view/spring-school-on-data-science/home