Analyst

Evaluated AI model performance across 6 different dimensions; analyzed model capabilities on complex STEM problems and quantitative reasoning tasks, assessing how different model architectures handle high-complexity problem domains. Built publication-quality performance reports synthesizing AI model evaluation data; communicated findings on model strengths, failure modes, and capability patterns to technical stakeholders. Tracked evolution of successive model versions across 35+ evaluation tasks; analyzed how model improvements correlate with architectural changes and training advances, identifying performance trends in latest model releases. Coordinated AI model evaluation and reporting across 10+ concurrent projects; maintained data integrity and ensured consistent evaluation methodology across diverse STEM and general reasoning problem sets.

Conducted systematic evaluation of Large Language Model (LLM) performance on STEM research problems, reasoning tasks, and complex quantitative challenges; assessed model output quality and capability across diverse problem domains. Analyzed evaluation data across 21+ AI model versions, identifying performance patterns and trends; tracked how model capabilities evolved across successive releases and assessed performance improvements in high-complexity problem-solving. Evaluated model behavior on multimodal problems (text, image, audio, video); assessed model strengths and failure modes on complex STEM tasks and investigated how different model architectures handle problem complexity. Collaborated with research teams to communicate findings on model capabilities and performance trends, contributing to understanding of how models evolve and improve on quantitative research domains.

Reviewed 50+ peer-reviewed scientific papers via PubMed, Google Scholar, and academic databases; assessed research quality, evaluated technical claims, and synthesized findings from STEM literature for accuracy and relevance. Performed fact-checking and data validation of technical claims in published research; verified claims against primary sources and assessed consistency of reported findings. Authored 20+ publication-quality scientific articles and research-driven content pieces; translated complex STEM research findings into clear, accurate technical narratives for diverse audiences. Developed research-backed analysis reports for 3 organizations, synthesizing findings from scientific and technical literature to support data-driven decision-making.

Conducted theoretical and computational research on particle dynamics in complex spacetime geometries; developed mathematical models, performed numerical simulations, and derived physical insights from analytical and computational approaches. Synthesized research literature and formulated research hypotheses; contributed to 4 peer-reviewed publications in top-tier physics journals (Physical Review D, Physics of the Dark Universe, European Physical Journal C, Universe). Developed numerical and symbolic computation frameworks using Python, MATLAB, and Mathematica; validated theoretical predictions against computational results and assessed consistency of analytical approximations. Collaborated with faculty researchers and PhD scholars on quantitative research projects; participated in research discussions on problem formulation, methodology, and interpretation of results.