Daniel Rud Email & Phone Number

Calabasas, Ca, Us

∘ Created a targeted learning based framework for testing gene-environment interactions (tlGxE) that outperforms popular methods in both estimation accuracy and statistical power. tlGxE was applied to mutlicohort data to analyze relationship between various modifiable risk factors and colorectal cancer risk.∘ Implemented an improved version of the popular Weighted Quantile Sum Regression model that does not require data splitting and can be applied to large datasets (FGWQSR). In this… Show more ∘ Created a targeted learning based framework for testing gene-environment interactions (tlGxE) that outperforms popular methods in both estimation accuracy and statistical power. tlGxE was applied to mutlicohort data to analyze relationship between various modifiable risk factors and colorectal cancer risk.∘ Implemented an improved version of the popular Weighted Quantile Sum Regression model that does not require data splitting and can be applied to large datasets (FGWQSR). In this project, I derived nonstandard asymptotic distributions for hypothesis testing of constrained model parameters along with a hybridized optimization procedure for efficient model fitting. I demonstrated that FGWQSR had superior statistical power over competing approaches, and applied the method to study the relationship between prenatal particulate matter exposure and Autism risk∘ Curated Bulk RNA sequencing, DNA methylation (CpG), and CRISPR/Cas9 single gene disruption data for statistical analysis, where the relations between epigenetic conservation, gene expression, and "essential" genes were studied Show less

∘ 2023, 2024: I worked as a teaching assistant in USC’s summer biostatistics program LA’s BeST. In the program, I mentored undergraduate students from various disciplines in applying appropriate statistical models for their unique research questions. In addition, I taught students how to code efficiently, visualize data, and perform various statistical analyses in R.∘ 2023, 2024: During Columbia University’s SHARP Multi-omics bootcamp, I led the lab portion of the gene environment… Show more ∘ 2023, 2024: I worked as a teaching assistant in USC’s summer biostatistics program LA’s BeST. In the program, I mentored undergraduate students from various disciplines in applying appropriate statistical models for their unique research questions. In addition, I taught students how to code efficiently, visualize data, and perform various statistical analyses in R.∘ 2023, 2024: During Columbia University’s SHARP Multi-omics bootcamp, I led the lab portion of the gene environment interaction section, which amounted to demonstrating and discussing implementations of methods for gene environment interactions in R and answering questions pertaining to these methods from an audience of international researchers.∘2023: I assisted Dr. Juan Pablo Lewinger in his PM591 Machine Learning course. My duties included leading the lab section of the course, answering course material and R coding related questions, and grading biweekly homework assignments. Show less

∘ Developed software to web-scrape dictionary webpages in order to extract phonetic strings∘ Created novel text embedding algorithm to transform sentences into numeric sequences according to prosodic principles∘ Utilized Variable Length Markov Chains (VLMC) to represent embedded texts into lower dimensional form∘ Delineated novel clustering and classification algorithms for VLMC data structures.

Regularly graded homework and quizzes for upper division Probability course and would produce corresponding solution sets.

Provide assistance to students ranging from elementary school to college with various levels of mathe-matics, including algebra, calculus, geometry, trigonometry, statistics, probability, etc.

Doctor Of Philosophy - Phd, Biostatistics, 4.0

Coursework: Statistical Inference, Regression Analysis, Categorical Data Analysis, Survival Analysis, Longitudinal Analysis, Statistical.

Bachelor Of Science - Bs, Mathematics And Statistics | Minor In Computer Science, Gpa: 3.89

High School Diploma, Gpa: 4.45

Valedictorian