I’m Omar, a machine learning researcher and engineer. I study how learning systems can generalize from sparse experience and use structured internal models to reason about physical processes.
Robot learning, physically grounded world models, and uncertainty-aware autonomy.
Explore my workIn ChronoState and Chronometric Injection, we study timing signals in frozen language models. I contributed to research framing, code and data processing, and manuscript development.
The evidence matters as much as the mechanism: a stronger prompted-timestamp baseline, weak held-out quota transfer, and a negative zero-shot result help define what these representations can and cannot do.
Read ChronoStateMy work on multisensor aircraft tracking connects reproducible analytics with decisions. Related collaborations examine detection, fusion continuity, and sensor handoffs through Bayesian models, alongside constrained multi-satellite coordination.
Read DataOps-to-decisionI first-authored work on reconstructing digital-forensics evidence graphs from legal documents using language models, treating provenance and explainable relationships as central to the task.
Read the COMPSAC paperO. Ramadan, A. K. Saeed, B. A. Johnson & B. M. Rodriguez
A. K. Saeed, A. S. Yasin, B. A. Johnson, O. Ramadan & B. M. Rodriguez
O. Ramadan, R. Xiao, D. Xu & W. Xu
S. Siavoshian, O. Ramadan, A. K. Saeed, B. A. Johnson, A. M. E.-A. Diab & B. M. Rodriguez
S. Siavoshian, O. Ramadan, A. K. Saeed, B. A. Johnson, A. M. E.-A. Diab & B. M. Rodriguez
I move between research and implementation: from Bayesian models and reproducible experiments to the systems that make them usable.
At Johns Hopkins University, I develop physics-informed Bayesian neural-network workflows, uncertainty-quantification modules, and scientific machine-learning infrastructure. Earlier work spans secure AI, graph learning, retrieval, and high-performance inference.
Johns Hopkins UniversityMachine Learning Engineer
Johns Hopkins APL / NSAAI Researcher
Opinion.aiMachine Learning Engineer
University of BaltimoreMachine Learning Engineer
M.S. Data Science
Johns Hopkins University, Whiting School of Engineering
2026 · Conferral pending
B.S. Cyber Forensics
University of Baltimore
May 2025 · Summa cum laude
Since April 2026, I’ve mentored Sam Siavoshian in research design, statistical reasoning, literature review, and manuscript development. Sam originated the time-perception research direction and is first author of our two temporal-representation papers.
For conversations about learning, physical reasoning,
and building useful research systems.