Biostatistics & Bioinformatics Researcher Resume

Johnson(Siyuan) Luan

Tel: +1-517-***-**** Email: ******@******.***

Education

Tulane University New Orleans, Louisiana

M.S in Biostatistics September 2022 - May 2025

• GPA: 3.54.

• Focused on statistical methods and computational approaches in biomedical research and bioinformatics, integrating machine learning and deep learning techniques for biomedical data analysis. Michigan State University East Lansing, Michigan

B.S in Zoology September 2018 - December 2022

• Specialized in genetic and molecular biology with an emphasis on computational genomics and bioinformatics, exploring evolutionary biology and human genetics.

Technical Strengths

Programming Languages: R, Python(Pandas, NumPy), SQL, JavaScript, HTML, Linux shell scripting Software & Techniques: BLAST, FASTA, Bioconductor, Cytoscape, PCA, LDA, Correlation Analysis, Logistic Regression, GWAS, MySQL, R-Studio, Emacs, VSCode, PyTorch, TensorFlow, NLP, Git, GitHub, AWS, GCP

Research

Dimensionality Reduction Techniques in Bioinformatics Tulane University, LA Research March 2024 - Present

• Investigated dimensionality reduction methods including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and the Laplacian Matrix for high-dimensional biomedical datasets, focusing on gene expression data.

• Employed deep learning autoencoders to enhance feature selection and improve the interpretability of gene expression patterns related to disease biomarkers.

• Research aims to develop bioinformatics tools that can support the optimization of diagnostic models in biomedical engineering, particularly for personalized medicine and medical device development. Arch Epidemiology Tulane University, LA

Research March 2024 - Present

• Investigated the influence of environmental factors on childhood health outcomes, utilizing advanced biostatistical methods and machine learning models to predict disease risk.

• Designed AI-based predictive models in collaboration with biomedical engineering teams, contributing to public health policy and the development of population health strategies.

• This work supports biomedical engineering innovations focused on public health interventions and disease prevention at the population level.

Correlation Analysis for VOC, CMPCBC, and Nano-sensor Array Tulane University, LA Research September 2023 - Present

• Conducted an in-depth statistical correlation analysis (Pearson and point-biserial) to assess the relationship between volatile organic compounds (VOC) and biomedical sensor arrays, with applications in early disease detection.

• Applied deep neural networks to optimize sensor-based data interpretation, improving accuracy and sensitivity in biomarker detection for respiratory and metabolic disorders.

• Contributions directly impact the development of AI-driven medical devices, with an emphasis on clinical diagnostics and sensor-based healthcare technology.

Protein Database Construction for Spectral Matching Tulane University, LA Research May 2023 - September 2023

• Developed an automated protein sequence database to improve the identification of disease biomarkers using machine learning algorithms for mass spectrometry spectral matching.

• Employed deep learning models to enhance spectral similarity calculations, improving diagnostic precision in proteomic studies and aiding in biomarker discovery for biomedical applications.

• The project provided computational infrastructure critical for advancing biomedical engineering tools in therapeutic development and personalized diagnostics.

Genetic Analysis of Human Diseases Michigan State University, MI Research September 2021 - January 2022

• Conducted genetic analysis of disease-associated variants using computational tools to analyze GWAS data, focusing on identifying risk alleles in human populations.

• Applied machine learning algorithms to predict hereditary disease transmission, contributing to research on the genetic underpinnings of human disorders and potential therapeutic interventions. Medical Infrastructure Utilization in Shanghai Michigan State University, MI Research September 2022 - December 2022

• Performed statistical analysis of healthcare utilization patterns in urban areas, leveraging machine learning to forecast demand for medical services based on demographic factors.

• Results contributed to healthcare policy reform by providing data-driven insights for biomedical infrastructure planning and resource optimization in densely populated regions. Pharmaceutical Service in Rural Medical Institutions Michigan State University, MI Research May 2021 - August 2021

• Analyzed the distribution of pharmaceutical services in rural areas using AI-based models to optimize drug delivery systems and improve access to healthcare.

• Developed strategies aimed at improving healthcare equity, focusing on biomedical engineering innovations that address disparities in rural health outcomes.

Work Experience

Zhuyuwan Zoo Yangzhou, China

Intern May 2019 - September 2019

• Behavioral Data Collection and Analysis: Systematically collected, recorded, and analyzed behavioral patterns and dietary preferences of Golden Snub-Nosed Monkeys and East African Black-and-White Colobus, generating datasets for potential bioinformatics research in primate genetics and evolutionary behavior.

• Data-Driven Care Optimization: Leveraged behavioral data to optimize feeding strategies, recognizing specific dietary aversions based on habitat adaptations, directly contributing to environmental and biological adaptation research.

• Habitat Simulation & Environmental Control: Assisted in designing controlled environments simulating high-altitude habitats, applying environmental data for the species’ wellbeing, which could be extrapolated to studies on genetic adaptability in changing climates.

• Behavioral Analytics: Analyzed primate social hierarchies and behavioral traits, providing foundational data that can inform bioinformatics research into genetic behaviors and evolution in primates. Yangzhou University Animal Hospital Yangzhou, China Intern November 2018 - January 2019

• Clinical Diagnostics and Data Analysis: Conducted X-ray diagnostics and analyzed images for approximately 20 animals, contributing to the construction of a comprehensive medical diagnostic database that can be applied in veterinary bioinfo.

• Anatomy and Physiology Insight: Gained extensive hands-on experience in animal anatomy, physiology, and behavior, laying the groundwork for applying bioinformatics techniques to comparative veterinary genomics.

• Surgical Assistance & Data Integration: Assisted in various surgical procedures and integrated clinical data into treatment records, enhancing the practical application of bioinformatics in real-world animal health care.

• Database Construction: Established and maintained an extensive database of veterinary treatment records, providing a scalable platform for future bioinformatics studies on treatment outcomes and disease patterns in veterinary medicine.

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