Nic Weber and Kyle Niemeyer
September 28, 2026
We are happy to announce a third cohort of the US Research Software Sustainability Institute (URSSI) Early-Career Fellowship. This cohort includes six fellows working on the following projects:
Reliable Agentic Workflows for Sustainable Multi-Language Scientific Software Interfaces - Su Sun is a postdoctoral scientific software engineer in Chemical Engineering at Northeastern University. His project will investigate whether agentic AI workflows can reliably maintain and sustain cross-language interfaces for scientific software, using the Cantera MATLAB interface as a case study. You can read more at the project website.
From Pixels to Peaks: Sustainable Software for Global Seamount Discovery - Sarah Beethe is a PhD student in the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University. Her project will rebuild the Seamount Catalog as open, Python-based infrastructure, then use that foundation to investigate the performance of machine-learning tools for identifying seamounts. You can follow her project here.
HDL-Verify: A Validation and Reproducibility Toolkit for AI Generated Hardware Description Code - Shima Mohaghegh is a graduate student at the University of Kansas where she works on computer architecture and hardware design in HPC. Her work is unique in that she is developing both a tool (HDL-Verify) and a benchmark for reference circuits based on the tool. You can read more about her work, and see her impressive progress so far, here.
Reliability Checks for Machine-Learning Surrogates in Scientific Software - Isaac Malsky works in computational astrophysics as Pipeline Scientist at the SETI Institute, supporting the TOLIMAN mission. His project will develop vibe-check, which in his own words addresses the following problem: “A surrogate (an emulator, neural operator, or learned solver component) is usually judged by its average test error. That number can hide problems: the model may be extrapolating outside its training data, the preprocessing may have leaked test data, or the outputs may break a physical constraint. vibe-check takes a predict function (or saved predictions) and your data splits, runs twelve checks, and writes a report that someone who did not train the model can read.” You can follow his work here.
Teaching Coding Agents the Brain Imaging Data Structure - Scott Huberty is a cognitive neuroscientist who currently works as a research fellow at the University of Southern California. His project will develop a prototype to help coding agents use BIDS (a community-developed specification, plus an ecosystem of tools, for organizing and describing neuroscience datasets).
Silent Failures in ML-Driven Simulations - Ali Mohaghegh is a PhD student in the Department of Aerospace Engineering at the University of Kansas. His project aims to produce an open-source diagnostic framework for detecting subtle reliability failures in scientific simulations that use machine learning (ML), reduced-order models (ROMs), neural operators, and other learned components. You can follow along here.
We’re excited to bring these projects into the URSSI early-career program, and continue working with highly-motivated scientists who take their software seriously.