Limeng X. - STEM Specialist

Las Vegas, United States

Experience

Feb 2025 - Present

11 months

Designed 50+ expert-level biology prompts to challenge LLMs at top AI research labs to enhance model accuracy and scientific reasoning.
Developed corresponding grading rubrics to evaluate LLM model output and improve AI learning and feedback loops to help AI learning.
Reviewed 200+ prompt submissions, and provided constructive feedback to enhance prompt and iterative feedback.
Conducted rigorous quality control of 400+ textbook-style multimodal prompts for accuracy, completeness, and alignment with client requirements.

Sep 2022 - Dec 2022

4 months

Led two lab sections for Principles of Plant Biology (PBIO1210), instructing 50 freshman students on core plant biology concepts such as plant physiology, development, pathology, and ecology.
Facilitated hands-on lab activities and utilized active learning techniques to engage students.
Implemented a flipped classroom strategy to encourage interactive discussions, including debates on GMOs and the creation of Herbarium posters.

May 2022 - Aug 2022

4 months

Created a high-throughput plant image phenotyping platform, integrating automated image analysis pipelines to accelerate data-driven decision-making.
Developed visualization tools and statistical models (Python, R) to analyze plant trait data, influencing breeding strategies.
Designed and executed biology experiments (controlled environment & field), aligning research with breeding objectives.
Streamlined image analysis workflows, reducing manual processing time by 70% while boosting phenotypic data accuracy.
Presented data-driven insights to interdisciplinary teams, contributing to the development of improved cotton varieties.

Sep 2019 - Dec 2019

4 months

Led three lab sections for Concepts in Biology (BIOL1310), teaching 70 non-science majors foundational biology concepts relevant to everyday life.
Covered topics such as biological macromolecules, bacterial drug resistance, water quality assessment, molecular genetics, and inheritance patterns.
Employed cooperative learning and inquiry-based teaching approaches to promote critical thinking and active student engagement.

Aug 2018 - May 2024

5 years 10 months

Developed and implemented scientific tools using Python, R, and Bash to process large-scale datasets on high-performance computing clusters, improving workflow efficiency and data accuracy.
Optimized image processing workflows (calibration, cleaning, feature extraction), supporting biological research and trait analysis.
Built and deployed a machine learning pipeline in Docker for automated image analysis, increasing reproducibility and scalability.
Led analyses integrating genomic, phenotypic, and environmental datasets (e.g., greenhouse conditions, soil data), achieving a >20% increase in selection accuracy for drought tolerance.
Coordinated National Science Foundation (NSF) and led NSF-REU projects, mentoring three undergraduate researchers on plant phenotyping, GWAS, environmental data analysis projects.

Aug 2014 - Dec 2017

3 years 5 months

Coordinated research projects in the vegetable breeding program, managing greenhouse and field trials, seed inventory, data collection, quality control, and statistical analysis to support breeding objectives.
Led genotyping projects, generating high-quality genomic datasets, applying experimental design principles and computational analysis to derive genetic insights.
Developed high-density linkage maps and identified 29 QTLs associated with root traits in a tomato population, integrating genomic and phenotypic data for marker-assisted selection.
Designed and executed data-driven experiments to evaluate the performance of pepper, tomato, melon, and squash, leveraging statistical modeling and multi-environment trials for trait assessment.
Communicated complex data insights through reports, publications, and presentations, aiding decision-making for germplasm release.

Proven success in designing, evaluating, and refining biology prompts and model outputs to improve large language model (LLM) performance.
Strong skills in Python, R, Unix, SQL, and Docker for machine learning, computer vision, and complex data annotation.
Experienced in scientific writing, prompt design, and rubric creation to support AI model instruction and evaluation.
Advanced degree in biology and +10 years in biology research