Continuing project under Dr. David Rudoler exploring the dose-response effect of Cognitive Behavioural Therapy, exploring factors impacting treatment response and using statistical methods to determine ideal dosage.

Assisted in project analyzing health administrative data to determine ideal dose response for patients receiving cognitive behavioural therapy in specialized mental health facility.Used survival analyses modelling in R to determine percentile responses of patients to CBT and to determine predictors of response or failure to respond.Worked with data analysts, clinicians and supervisors at Ontario Shores, developing study methodology and analysis plans.

Ran project to analyze gene isoform expression and determine isoform-level biomarkers of drug response in acute myeloid leukemia (AML) patients using both pharmacogenomic and clinical datasets.Parallelized computational processes in Python and R to generate large-scale isoform co-expression networks on high-performance computing cluster.Used network analysis methods in Python and R with igraph, to cluster and classify isoforms within co-expression network.Used various regression… Show more Ran project to analyze gene isoform expression and determine isoform-level biomarkers of drug response in acute myeloid leukemia (AML) patients using both pharmacogenomic and clinical datasets.Parallelized computational processes in Python and R to generate large-scale isoform co-expression networks on high-performance computing cluster.Used network analysis methods in Python and R with igraph, to cluster and classify isoforms within co-expression network.Used various regression analyses and survival analyses in both Python and R to explore clusters as potential biomarkers for drug response.Currently working as part of XevaDB project to analyze and compare genomic data of various cancer models using R. Show less

Helped build and curate large-scale pharmacogenomic drug-combination datasets for use in finding genomic biomarkers for drug combination sensitivity and resistance in cancer cell-lines. Worked alongside the lab's software engineer, using R and basic SQL to help clean and manage data and data objects effectively. Engaged in project reviewing and re-implementing machine learning methods from literature for drug-response prediction in cancer cell-lines.Explored applications of… Show more Helped build and curate large-scale pharmacogenomic drug-combination datasets for use in finding genomic biomarkers for drug combination sensitivity and resistance in cancer cell-lines. Worked alongside the lab's software engineer, using R and basic SQL to help clean and manage data and data objects effectively. Engaged in project reviewing and re-implementing machine learning methods from literature for drug-response prediction in cancer cell-lines.Explored applications of pharmacogenomic datasets for training machine learning methods to predict cancer drug response on patient-derived xenografts datasets Explored impacts of training methodology on model performance using Python and R. Worked on a project for creating a unified platform in R and methodology for training precision medicine machine learning methods.Performed literature review of machine learning methods for predicting drug Show less

Accepted under BHKLAB as a U of T Medical BioPhysics (MBP) Summer Student. Designed and tested machine learning method aimed at predicting drug synergy and efficacy of drug combinations on patients.Used measures of drug synergy from large-scale pharmacogenomic datasets to train a novel tensor decomposition recommender-system under Dr. Soheil Jahangiri Tazehkand.Self-taught how to create machine learning methods in Python using tensorflow and scikit-learn. Built recommender system… Show more Accepted under BHKLAB as a U of T Medical BioPhysics (MBP) Summer Student. Designed and tested machine learning method aimed at predicting drug synergy and efficacy of drug combinations on patients.Used measures of drug synergy from large-scale pharmacogenomic datasets to train a novel tensor decomposition recommender-system under Dr. Soheil Jahangiri Tazehkand.Self-taught how to create machine learning methods in Python using tensorflow and scikit-learn. Built recommender system from ground-up, wrote code to parallelize performance on high-performance cluster.Wrote bash scripts in linux environment to interface with slurm back-end on high-performance computing clusters.Awarded UROP for academic excellence Show less