Short-term support partnerships



Direct Support for Researchers

Get help with improvements like expanding your code functionality, transitioning from lab computers to HPC, or introducing new technologies into your workflow. 

MATCHPlus provides support to researchers through short-term engagements that pair a student-facilitator with an experienced mentor to address an immediate research need. Mentors are ACCESS Computational Science and Support Network (CSSN) experts with subject matter expertise and professional facilitation skills relevant to the engagement.

3-6 Month Engagements
Mentor/Student Team
No Cost
Leverage CSSN Expertise

The MATCHPlus Process

Researchers define the research need via the engagement request form, and then work with the MATCH team to identify required skill sets, deliverables and a set of milestones.

Researcher (PI) recognizes support need and submits an engagement request.
MATCHPlus matches a mentor and student facilitator with the project.
Student facilitator carries out the project with mentor and PI through regular meetings.
Research moves forward.

Mentors are typically professional Research Computing Facilitators (RCFs) from the CSSN or senior members of the researcher's existing group. Student facilitators are recruited unless researchers request a particular student. They commit 10-20 hours per week and receive a stipend or credit. Student facilitators present updates at a monthly meeting with other project teams and learn about other engagements in the program.

MATCHPlus is modeled after the workflow developed in 2017 by the NSF-sponsored Northeast Cyberteam and subsequently adopted and enhanced by the NSF-sponsored CAREERS Cyberteam to help researchers address computationally intensive research challenges and needs.

Interested in Joining the Pilot?

MATCHPlus will be selecting up to ten projects to launch between September, 2022 and May, 2023. Requests will be evaluated in the order received until January 31, 2023. Subsequent requests will continue to be accepted, but there may be a short delay in processing as we incorporate feedback from the pilot. 

Apply for MATCHPlus

Receive expert CSSN research support

Collaborate with a mentor/student pairing

Work with subject matter experts

Benefit from support dedicated to advancing your scientific research

Request a Pilot Engagement

Fill out our online form

Join our Mentors

Leverage your expertise

Mentor a student research facilitator

Help advance scientific breakthroughs

Help drive evolving and emerging research

Become a Mentor

Fill out our online form

Be a Student-Facilitator

Gain cutting-edge research experience

Work with a CSSN mentor and expert

Help drive scientific research

Showcase your expertise, learn new skills and gain experience

Join MATCHPlus

Fill out our online form

Featured MATCHPlus Engagements

Investigation of robustness of state of the art methods for anxiety detection in real-world conditions
Abdulrahman Alkurdi
University of Illinois at Urbana-Champaign

I am new to ACCESS. I have a little bit of past experience running code on NCSA's Blue Waters. As a self-taught programmer, it would be interesting to learn from an experienced mentor. 

Here's an overview of my project:

Anxiety detection is topic that is actively studied but struggles to generalize and perform outside of controlled lab environments. I propose to critically analyze state of the art detection methods to quantitatively quantify failure modes of existing applied machine learning models and introduce methods to robustify real-world challenges. The aim is to start the study by performing sensitivity analysis of existing best-performing models, then testing existing hypothesis of real-world failure of these models. We predict that this will lead us to understand more deeply why models fail and use explainability to design better in-lab experimental protocols and machine learning models that can perform better in real-world scenarios. Findings will dictate future directions that may include improving personalized health detection, careful design of experimental protocols that empower transfer learning to expand on existing reach of anxiety detection models, use explainability techniques to inform better sensing methods and hardware, and other interesting future directions.

GPU-accelerated ice sheet flow modeling
Anjali Sandip
University of North Dakota

Sea levels are rising (3.7 mm/yr and increasing!)! The primary contributor to rising sea levels is enhanced polar ice discharge due to climate change. However, their dynamic response to climate change remains a fundamental uncertainty in future projections. Computational cost limits the simulation time on which models can run to narrow the uncertainty in future sea level rise predictions.  The project's overarching goal is to leverage GPU hardware capabilities to significantly alleviate the computational cost and narrow the uncertainty in future sea level rise predictions. 

[Plan A] The PI is investigating numerical techniques to predict ice-sheet flow for large-to-continental glaciers on GPUs. Suppose a successful technique has been identified, implemented in MATLAB, and verified prior to the start of this project. In that case, the objective will be to port the code to CUDA C and investigate techniques to justify the GPU implementation in the price and power consumption to performance metric compared to a "standard" CPU implementation.

[Plan B] The PI developed a preliminary GPU implementation to predict ice-sheet flow for regional-scale glaciers on GPUs. The GPU ice velocity predictions agreed (~ 1% discrepancy) with a "standard" CPU implementation for the chosen glacier model configurations and input data. The GPU implementation was justified in the price and power consumption to performance metrics. However, the profiling results from the preliminary GPU implementation indicated non-optimal global memory access patterns reported in the LITEX and L2 cache. Furthermore, we observed significant drops in effective memory throughput with increased spatial resolution or degrees of freedom (DoFs). This project will aim to investigate techniques to reduce mesh non-localities and drop in effective memory throughput with an increase in spatial resolution. 


Run Markov Chain Monte Carlo (MCMC) in Parallel for Evolutionary Study
Yanni Chen
Texas Tech University

My ongoing project is focused on using species trait value (as data matrices) and its corresponding phylogenetic relationship (as a distance matrix) to reconstruct the evolutionary history of the smoke-induced seed germination trait. The results of this project are expected to increase the predictability of which untested species could benefit from smoke treatment, which could promote germination success of native species in ecological restoration. This computational resources allocated for this project pull from the high-memory partition of our Ivy cluster of HPCC (Centos 8, Slurm 20.11, 1.5 TB memory/node, 20 core /node, 4 node). However, given that I have over 1300 species to analyze, using the maximum amount of resources to speed up the data analysis is a challenge for two reasons: (1) the ancestral state reconstruction (the evolutionary history of plant traits) needs to use the Markov Chain Monte Carlo (MCMC) in Bayesian statistics, which runs more than 10 million steps and, according to experienced evolutionary biologists, could take a traditional single core simulation up 6 months to run; and (2) my data contain over 1300 native species, with about 500 polymorphic points (phylogenetic uncertainty), which would need a large scale of random simulation to give statistical strength. For instance, if I use 100 simulations for each 500 uncertainty points, I would have 50,000 simulated trees. Based on my previous experience with simulations, I could design codes to parallel analyze 50,000 simulated trees but even with this parallelization the long run MCMC will still require 50000 cores to run for up to 6 months. Given this computational and evolutionary research challenge, my current work is focused on discovering a suitable parallelization methods for the MCMC steps. I hope to have some computational experts to discuss my project.