• Generated somatic mutation profiles, represented by mutation distributions and mutation types, from whole genome sequencing data of liquid biopsy • Trained deep neural network models to predict cancer types using the somatic mutation profiles• Evaluated model performance on somatic mutation profiles of blood samples with R and Python

• Guided students in computational biology exercises, fostering proficiency in R and Python • Facilitated learning of approximately 70 students by providing step-by-step instructions and real-time troubleshooting during lab sessions• Conducted weekly office hours to provide individualized assistance and address student questions• Graded weekly assignments, offering constructive feedback to enhance student comprehension

• Deployed enrichment analyses for genetic variants associated with age-related hearing loss and tinnitus using the eMERGE Network and U.K. Biobank datasets • Executed genome-wide association studies in a high-performance computing environment• Designed a study pipeline and processed raw patient demographic, clinical, and genotyping data with R and Python• Presented and discussed analysis reports in weekly lab meetings and regular joint meetings with geneticists and clinicians to gather feedback and improve experimental designs

• Led a project from beginning to end to study genetic and non-genetic risk factors associated with cisplatin-induced toxicities by performing statistical analyses such as regression analyses, two proportion z-tests, and in silico gene expression analyses in PLINK and R• Educated and guided two Ph.D. students on project procedures to provide students with practical experience and skill training in bioinformatics • Wrote research papers for publication based on experimental findings to highlight clinical awareness and potential therapeutic targets for cisplatin-induced toxicities

Critical Deterioration Prediction in Hospitalized Children Department of Pediatrics (Dr. Anoop Mayampurath)• Exploiting machine learning methods (e.g. random forest, XGBoost) and vital signs to predict critical deterioration in pediatric ICU patients utilizing R• Analyzing the most predictive contributors• Comparing our models with Pediatric Early Warning Systems• Supervisor: Dr. Anoop Mayampurath

Molecular Neuroanatomy of Cannabis-associated Genes in the Developing Human Brain(Dr. Leon French)•Utilized polygenic analysis of transcriptomic resources to identify cannabis impact on brain regions and periods of brain development

Finding host-specificity determinants of Pseudomonas syringaeDepartment of Cell and System Biology (Dr. David Guttman & Dr. Mehdi Layeghifard)• Processed genome sequences, protein family data, and several plant phenotypes• Applied machine learning approaches (e.g., support vector machine, random forest) to uncover the genomic determinants of host specificity in Pseudomonas syringae