Long-term embedded specialists

MATCHPremier

LIMITED SCOPE PILOT

Does your Project Need More Support?

MATCHPremier provides embedded support by pairing one or more MATCHPremier Consultants with a research team for a period of engagement typically ranging from 12 - 18 months. Engagements requested al least six months in advance. 

Pre-Planned Engagements
Consultant Coordination
Researcher Funded
12 - 18 Months

MATCHPremier Consultants are selected from the Computational Science and Support Network (CSSN) depending on the researcher's needs - they may be facilitators, research software engineers, or other types of appropriate support personnel.

Funding for the MATCHPremier consultants will be arranged directly between the researcher's institution and the consultant. We anticipate that personnel participating in the MATCH-Premier program will be deployed at institutions across the country and partially funded for other work at their home institutions.

The MATCHPremier Process

Researchers define the research need, required skill sets, deliverables and a set of milestones via the engagement request form.

Researcher submits a project and secures funding (MATCH helps if needed)
MATCHPremier matches consultant candidates with the project.
Researcher selects consultant and makes payment arrangements.
Consultant carries out project with PI. Research moves forward.

Funding for the MATCHPremier consultants will be arranged directly between the researcher's institution and the consultant. We anticipate that personnel participating in the MATCHPremier program will be deployed at institutions across the country, and partially funded for other work at their home institutions.

Expert Consultants, Supported by MATCH

While they are funded by the researcher, the MATCH program works with MATCHPremier project teams to ensure that consultants have a strong support system. Consultants are invited to join monthly meetings with other MATCHPremier consultants and bi-annual reviews are conducted with the project team to review progress, set milestones, and address issues or concerns that arise.

MATCHPremier engagements match researchers to consultants with the expertise to help with massive allocations or with using ACCESS resources in novel ways. In certain cases, we will support proposal development efforts with detailed input that ultimately leads to the development of allocation proposals that demonstrate the need for a significant or unusual allocation, and a well designed plan that maximizes scientific output per resource used.

Funding your MATCHPremier Engagement

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Interested in Joining the Pilot?

MATCHPremier will be selecting ten projects between September, 2022 and March, 2023. Requests will be evaluated in the order received until October 31, 2022. 

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Benefit from expert advice

Expand the reach of your discoveries

Benefit from support dedicated to advancing your scientific research

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Match-making for significant or unusual allocation

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Sample Engagements from the Northeast and CAREERS Cyberteams

Optimization and Parallelization of A Numerical Gravitational-Wave Model

Next-generation gravitational-wave (GW) detectors, such as the Laser Interferometer Space Antenna (LISA), will detect GW signals from extreme mass-ratio inspirals. High fidelity and fast GW models are essential for achieving the full scientific potential of LISA. We have developed a high-accuracy, data-driven (surrogate) model for LISA-type sources. The code is currently in a Jupyter notebook, but to enable data analysis studies, we require the model to operate as an optimized, stand-alone library. This project aims to accomplish this goal by porting the model into two publicly available, community-driven packages GWSurrogate and the Black Hole Perturbation Toolkit. In this project, the student will port the model to these existing codebases before optimizing. The model data will be stored in HDF5 file format. One of the main computational bottlenecks is likely to be the large matrix-vector multiplication required to compute each harmonic mode. The student will explore offloading this cost to a GPU through the cupy package and parallelization over mode computations. Code profiling will also be carried out to identify other parts of the code that could benefit from further optimizations.

  • gravitational-waves
Statistical Analysis of criminal cases in the United States District Court of Puerto Rico
Salve Regina University

For the purposes of submitting an amicus brief to the US Supreme Court, the Puerto Rico Association of Criminal Defense Lawyers (PRACDL) compiled several indictments and docket sheets from the PACER system. Data from these documents were extracted and analyzed with sociodemographic data from the US Census. Nevertheless, there is still an opportunity to continue to analyze the remaining data to present a visual representation of not only the type of cases seen in this court but also the length of time that the case is "open", the percentage of persons represented by a court-appointed attorney, the average length of sentences, the number of persons granted bail, the number of persons with bail violations and the reasons for those violations, among others. An understanding of these data will facilitate related future social justice projects in this jurisdiction.

  • ai
  • data-analysis
  • machine-learning
  • python
UVM Art and AI Initiative
University of Vermont

The UVM Art and AI Initiative is exploring approaches to artistic image production, comparing the results of StyleGAN and Genetic Algorithms*. More broadly, the project explores emerging artistic practices with Machine Learning and AI while referencing an artistic lineage to the artists Wassily Kandinsky, Jonn Cage and Yoko Ono; these artists employ(ed) instructions and systems in their non-digital artworks. Kandinsky distinguished systems and developed a science of aesthetics with the basic elements of point, line and plane; Cage used the oracle 'I Ching' like a computer to inform his compositional decisions; Ono writes poetic scores that turn her audience into active participants when they follow a series of imaginative instructions. Through this ongoing research and practice, we intend to join the larger conversation about art and A.I and design new curriculum for UVM undergraduate students.

This work began in February 2020 and is led by Jennifer Karson of UVM’s Department of Art and Art History and the CEMS UVM FabLab. The team has included three UVM students: two graduate students in data science and one undergraduate mechanical engineering student. The team currently uses RunwayML for the StyleGAN experiments and Processing, an open-source language and development environment built on top of the Java programming language, for Genetic Algorithms.

Additional summer funding ($2,000) is sought for one of the UVM Art and A.I. Initiative student coders. The funding will assist the team in reaching a short-term goal to present initial findings this July at Alife 2020 Montreal; a longer-term goal is to create an art installation for the UVM Fleming Museum of Art in the spring of 2021. This is a unique opportunity to exhibit as part of the statewide project 2020 Vision: Seeing the World through Technology and alongside the work of internationally renowned computer artist and co-founder of the Processing programming language Casey Reas.

Milestone 1:

Genetic Algorithms: Develop successful genetic algorithm code that meets compositional standard (color, architecture, appropriate datasets) while creating new compositions from the elements of existing hand-drawn compositions. The program should output image files that can be stored and printed at high resolutions on paper.

StyleGAN: Transition from RunwayML to coding in Python and employing VACC computer cluster. The process should output image files that can be stored and printed at high resolutions and on paper to be exhibited.

Milestone 2:

Genetic Algorithms: Create an interactive version of the program that allows for audience participation; can be exhibited in a museum gallery and online.

 

StyleGAN: Develop video that can be exhibited in museum gallery and online.

*Our Genetic Algorithm base code was developed by Daniel Shiffman

  • image-processing