Nick Kashani Motlagh

I work on when a machine learning system should answer, weigh evidence, revise, or stay quiet. Every model ships with a confidence signal, and every one of them answers a slightly different question than the one the next decision actually depends on. My dissertation, Answering Under Uncertainty, studies three places where that gap bites: abstaining from an unreliable prediction, checking whether evidence really moved the model, and deciding whether a second pass will repair a draft answer or ruin it.

I write training code in Python and PyTorch, build the evaluation harnesses around it, and run distributed experiments on Slurm and Singularity clusters. My most recent study spans 25,870 held-out questions and roughly 400 H200-GPU-hours.

I earned my PhD at The Ohio State University in August 2026, advised by Jim Davis, with graduate minors in mathematics and high-performance computing. I did my M.S. (2025) and B.S. with Honors (2021) there too. I am first author on four published papers — reject-option classification (Springer Best Paper Award at ISVC 2022, extended in Machine Vision and Applications 2025), evidence-use metrics for multimodal translation (WMT 2024), and a temporal satellite-imagery collection framework (ICCV Workshop 2021) — with a fifth manuscript revised after ACL Rolling Review and headed to arXiv. I have reviewed for CVPR, ICCV, and ECCV.

I am looking for Research Scientist, Applied Scientist, and ML Engineer roles, available to start now, and I am most useful to teams working on LLM evaluation, calibration, retrieval-augmented systems, or reliability infrastructure. Based in Columbus, OH, open to relocation and remote. U.S. citizen with five summers of AFRL-sponsored research experience; federal roles welcome.