Expanded Resume/CV

Education

• Northeastern University - Industry Ph.D. (2025 - Present)
  • Pfizer Advisors: Brett South and Ajit Jadhav
  • NEU Advisor: Professor Melanie Tory

• Johns Hopkins University - ScM in Biostatistics (2021 - 2023)

• University of Nevada, Reno - Honors BS in Mathematics (Statistics Emphasis), Minor in Big Data (2017 - 2021)

Skills and Interests

• Programming Languages: R, Python, SQL, C++, C, MATLAB
• Software: Amazon AWS, Dataiku, Docker, Singularity, SLURM, Markdown, Shiny, LaTeX, Linux Shell, Office Suite, Git, and GitHub
• Modeling and Computing: Classification, Regression, Predictive Analytics, Feature Engineering, Variable Selection, Exploratory Data Analysis, High-Performance/Cluster Computing, Containerization, Database Design and Implementation, Web-Application Development

Work

• Pfizer Digital Client Partners – Ajit Jadhav - Data Scientist - DRP Associate (Jun. 2024 - Present)
  • Programming static and time-dependent forced ranking systems for target product profile support using various Multi-Objective Optimization Algorithms, including Evolutionary Algorithms, Reinforcement Learning, and Robust Portfolio Optimization in Python and R, also supporting expanding existing indications of Pfizer’s current portfolio drugs under competing drug efficacy, safety, viability, and stakeholder preference functions.
  • Designing interactive D3, Python, and R-based visualizations to highlight the unmet needs areas for various demographic patient groups for diseases in Pfizer’s drug portfolio, using AWS for visualization deployments/usage data collection and custom HTML/CSS for user interface styling.
  • Enhancing internal/vendor data APIs with LLM functionalities for accelerated unstructured and structured data ingestion, creating retrieval augmented generation systems with source citation, and (tabular data) disease evidence map generation using specialized decision science and topic expert LLM-based agents.
• Pfizer AI/ML/Analytics Enterprise Architecture and L&D Analytics – Randy Duncan and Leonidas Petridis - Software/Solution Engineer - DRP Associate (Jun. 2023 - Jun. 2024)
  • Prototyped architectural features including Vector Databases and Retrieval Augmented Generation for the internal Large Language/GPT Model with enterprise scalability as the primary priority, utilizing AWS cloud-based tools and presenting results as an accessible Python/Streamlit-based interactive web-application.
  • Developed an Enterprise Architecture CityMap tool in Python to allow Architecture team members to easily provide project and skillset overviews in accessible and intuitive manner, thus preventing the horizontal siloing of AI/ML/Analytics-based projects throughout the enterprise.
  • Contributed to the evaluation framework documentation for the Architecture Data/Analytics Platform, addressing statistical and scalability concerns, also reviewing capabilities of services for the Data Visualization Decision Guide to ensure accurate and justified tool/service/programming language recommendations.
  • Created interactive dashboards and automating data visualization pipelines using a combination of Power BI, Tableau, Python, and Excel in the context of commercial and learning/development related outcomes data.
• Pfizer Machine Learning and Computational Science Group – Dr. Liang Xue - Data Scientist Intern (Jun. 2022 - Aug. 2022)
  • Cooperated with Chemical Biology Group scientists to incrementally develop a MySQL database to store and query experiment, protein, and meta-data, thus also providing a deliverable that functioned as a proof of concept for other Pfizer groups with lacking proteomics data infrastructure.
  • Programmed an accessible R Shiny-based UI to set up experiments/rawfiles/samplesheets, load in raw/processed data, modify data with excel-like interactivity, create a drag-and-drop tool to map omics files and data, store mapped data in a SQL database, and query stored data based on user needs.
  • Wrote markdown-based usage instructions and recorded tutorial videos to guide future database and UI users.
  • Created and tested scripts/methods to extend the Pfizer R Statistical Tool for Quantitative Mass Spectrometry-Based Proteomics (MSstats) package to process and standardize data from multiple proteomics data sources.
• Pfizer Simulation and Modeling Science Group – Dr. Liang Xue - Software Development Intern (Jun. 2021 - Aug. 2021)
  • Implemented an automated data pipeline in R, providing standardized computational proteomics cloud (SevenBridges) task execution and management capabilities.
  • Optimized the functionality and speed of core pipeline elements, using API’s for virtual private cloud interfacing and MySQL database task logging.
  • Developed an R Shiny-based General User Interface, using reactivity, datatable, and dashboard layout elements to provide user-friendly access to pipeline tools.
  • Wrote Markdown-based documentation to provide software feature and usage instructions.
• UNR Visual Perception Lab – Dr. Daniel Joyce - Student Researcher (HURA Award Funded) (Jan. 2021 - May. 2021)
  • Ported sleep classification algorithms from R to MATLAB and validated models with real-world sensor data.
  • Pre-processed light-intensity/movement sensor data and programmed a graphical user interface for data visualization with MATLAB.
  • Built, programmed, and calibrated a low-cost Arduino-based wrist-wearable for circadian rhythm measurement using C/C++.
• Nevada IDeA Network of Biomedical Research Excellence – Dr. Mihye Ahn - Student Researcher (INBRE UROP Award Funded) (Jan. 2020 - Jan. 2021)
  • Developed a user-friendly R Shiny web-application to deploy the best-performing prediction model.
  • Containerized parallelized R-based analyses in Docker and Singularity to minimize model training times on local high-performance computing clusters.
  • Minimized class imbalance effects of models using case-weighted models, subsampling techniques, and robust performance evaluation metrics.
  • Utilized both supervised machine learning and traditional statistical methods for pediatric patient traumatic brain injury predictive modeling.
• UNR Department of Mathematics - Dr. Grant Schissler - Statistics Research Assistant (Aug. 2019 - Jan. 2020)
  • Recommended future medical student outcome data collection and data evaluation strategies.
  • Conducted R-based frequentist and bayesian regression analyses to assess the difference of the new UNR medical school curriculum.
  • Performed data cleaning, numerical/visual exploratory data analysis, and assumption checking on medical school datasets in R.
• Swiss Armed Forces Nuclear, Biological, Chemical Defense School - Biology Laboratory Specialist (Jul. 2016 - Dec. 2016)
  • Identified microbiological hazards using multiple bacterial identification approaches.
  • Performed bacterial contamination analyses in a Biosafety Level 3 laboratory.
  • Worked as team coordinator to summarize, deliver, and present daily analysis results in an clear, concise, and timely manner.

Posters, Presentations, and Projects

• “Association of serum adiponectin and leptin levels with inner retinal thickness among individuals with or without elevated HbA1c”
  • Co-Authored Publication in Scientific Reports (2025)
• “Enhancing DataTrail Data Science Education: The BaltimoreTrails R Package and Dashboard”
  • ScM Thesis Web-App used and presented in the context of the DataTrail project (2023)
• “Proteomics Input and Output Tool (ProtIOT)”
  • Pfizer Machine Learning and Computational Science (MLCS) group talk regarding functionality and usage for the developed database access/querying and pipeline tools (2022)
• “Standardized Proteomics Pipeline Software Development”
  • Pfizer Simulation and Modeling Science (SMS) and Molecular Informatics (MI) group demonstrations regarding functionality and usage for the developed pipeline tools (2021)
• “A Low-Cost Arduino-Based Wearable for Circadian Rhythm Measurement and Light Exposure Classification”
  • UNR Undergraduate Research Symposium Presentation (2021)
• “Pediatric Traumatic Brain Injury Survival Prediction”
  • Research poster presented at the IEEE International Conference on Bioinformatics and Biomedicine (doi: 10.1109/BIBM49941.2020.9313568) and Fall UNLV Undergraduate Research Symposium (2020)
• “An Intuitive Introduction to Metropolis-Hastings Algorithm Sampling and Diagnostics”
  • Personal (R-based) Project (2020)
• “Personal Website and Project Portfolio”
  • Personal (R-based) Project (2020)
• “Bayesian Regression Model analyzing the UNR medical School Curriculum Change”
  • Research poster presented at the UNR Fall 2019 Statistical Computing Capstone Competition (2019)
• “Comparing Regularization Techniques on Simulated Data”
  • Personal (R-based) Project (2019)

Teaching and Tutoring

• JHU Bloomberg School of Public Health - Teaching Assistant (Aug. 2022 - May. 2023)
  • Teaching multiple discussion sections to help guide pre-medical and public health undergraduate students through a weekly problem set on topics related to the fundamentals of applied statistics and epidemiology.
  • Grading quizzes and discussion problem sets.
• UNR Honors College - Honors Peer Coach and Teaching Assistant (Aug. 2019 - May. 2021)
  • Taught, held office hours, and graded for an introductory honors course on academic integrity, undergraduate research, and professional networking.
  • Provided individual academic and professional mentorship and guidance to cohorts of 5-8 honors students in computer science and mathematics.
• UNR Department of Mathematics - Statistics Grader (Jan. 2020 - May 2020)
  • Graded homework, quizzes, and exams related to the statistical theory and R-implementation of linear models.
• UNR Department of Computer Science - Computer Science Teaching Fellow (Jan. 2018 - Apr. 2019)
  • Taught, tutored, and graded for the C language programming course.
  • Held weekly reviews and workshops on best practices to optimize and format C code.

Awards

• Honors Undergraduate Research Award (HURA)
  • 1,500 dollar undergraduate research funding and 500 dollar mentor stipend (2021)
• Nevada 2020 IDeA Network of Biomedical Research Excellence (INBRE) Undergraduate Research Opportunity (UROP)
  • 6,000 dollar undergraduate research funding, 1000 dollar mentor stipend, and weekly seminars on bioethics, medical grant writing, current biomedical practices, and preparation for a career in biomedical research (2020)
• 1st Place, 2019 Capstone Statistical Computing Project Competition
  • For undergraduate research results and poster presentation regarding the bayesian modeling of medical student outcomes (2019)