Ishta Madan

Biostatistician with an MS in Biostatistics (Statistical and Computational Genomics) and hands-on experience applying statistical methods, including linear mixed-effects models, to longitudinal patient cohort data, with a genomics-focused research background relevant to cell and gene therapy and other precision-medicine areas. Skilled in developing and maintaining reproducible, well-documented analytical pipelines, ensuring data quality, and uncovering meaningful patterns in data, with experience communicating findings clearly to both technical and non-technical audiences using R, Python, and related computational tools. Experienced working in multidisciplinary research teams across academic and industry settings, with a commitment to reproducible, well-documented science and peer-reviewed publications, including a published manuscript in The Lancet Regional Health – Africa and additional work under review.

RESEARCH & PROFESSIONAL EXPERIENCE

Research & Development Intern | Sygnomics (Mentor: Dr. Serdar Turkarslan; PI: Dr. Nitin Baliga) | Seattle, WA

August 2026 – Present

• Incorporating a new scoring scheme into patient reports to rank-prioritize personalized cancer care recommendations for glioblastoma patients.
• Collating multi-omic datasets to support development of a pan-cancer model leveraging systems biology tools and large language models (LLMs).

Statistical Analyst Intern | University of Oxford (Mentors: Dr. Seyed Alireza Hasheminasab and Dr. Devi Nandana Suchrita; PI: Dr. Anjan Thakurta) | Oxford, UK

July 2026 – Present

• Investigating longitudinal histone modification dynamics (H3K27 acetylation, H3K36 methylation) as epigenetic modifiers of disease progression in multiple myeloma, using linear mixed-effects models to track changes across patient samples.
• Evaluating the effects of two investigational epigenetic-targeting therapies (an NSD2 inhibitor and a p300 inhibitor) on chromatin modification patterns to support assessment of treatment responsiveness.
• Communicating complex findings to both scientific collaborators and non-specialist audiences.

Statistical Analyst Intern | Bruker Spatial Biology (Mentor: Patrick Danaher) | Seattle, WA

July 2026 – Present

• Conducting whole-transcription spatial imaging analysis of breast cancer tissue samples using CosMx tools in R to characterize the spatial gene expression landscape of tumors.
• Setting up and managing cloud computing infrastructure (AWS EC2) to support large-scale data processing needs.

Statistical Analyst | University of Pittsburgh (Mentor: Dr. Tiffany Gary-Webb; PI: Dr. Bamba Gaye) | Pittsburgh, PA

April 2025 – August 2026

• Analyzing large-scale longitudinal datasets to investigate genetic and environmental determinants of noncommunicable disease burden and cardiometabolic health disparities across Senegal and the African Continent, using R for data cleaning, normalization, and integrity checks to prepare high-dimensional datasets for analysis.
• Developing reports and supported data integrity across the full research lifecycle in collaboration with a multidisciplinary international research team across Africa.
• Co-authoring manuscripts for submission to high-impact journals, including The Lancet Regional Health—Africa and The American Heart Association Journal, translating complex statistical findings for scientific and non-specialist audiences.

MS Thesis Candidate | University of Pittsburgh (Mentor: Dr. Daniel Weeks) | Pittsburgh, PA

March 2025 – April 2026

• Developed and maintained reproducible, well-documented analytical pipelines in R/Bioconductor, including QC pipelines for high-dimensional Illumina EPICv2 DNA methylation microarray data (CpG probe filtering, Noob background correction, BMIQ normalization, mean imputation) using MethylCallR, with thorough documentation of all steps.
• Applied three epigenetic clocks (PC Horvath 1 & 2, Wu) and linear mixed-effects models to measure longitudinal epigenetic age acceleration in a pediatric patient cohort comparing TBI and orthopedic injury groups across acute, 6-month, and 12-month timepoints, contributing to understanding of epigenetic mechanisms underlying neurological injury.
• Created compelling, publication-quality visualizations and written reports to communicate complex findings to both scientific collaborators and non-specialist audiences.
• Presented thesis research and findings to a committee of faculty advisors as part of the MS thesis defense process.

Graduate Course Project: Advanced Computational Genomics | University of Pittsburgh (Mentor: Wenzhuo Lin, PI: Dr. Jiebiao Wang) | Pittsburgh, PA

September 2025 – December 2025

• Benchmarked TRIPOD, a nonparametric gene regulatory network (GRN) inference algorithm, on single-cell multi-omic data (scRNA-seq & scATAC-seq), implementing QC measures and evaluating performance against published standards.
• Processed and integrated multi-omic datasets in R, implementing QC filters and data transformation steps to prepare complex datasets for downstream analysis.
• Applied GRN evaluation frameworks from recent literature to contextualize TRIPOD’s performance against field benchmarks.
• Authored a technical abstract summarizing results, assumptions, and limitations for a scientific audience.

Research & Development Intern | ThermoFisher Scientific (PI: Tim Stockwell) | Frederick, MD

May 2025 – August 2025

• Developed and deployed Python-based machine learning pipelines (XGBoost, Gradient Boosting, Extra Trees Regression, Random Forest) to build predictive models from high-dimensional biological datasets in a commercial R&D setting.
• Partnered with senior bioinformatics and software staff to develop scalable, well-documented data products and analytical workflows in a commercial R&D environment.
• Performed quality checks and validation testing on processed datasets to ensure accuracy and reliability of outputs delivered to the research team.
• Presented internship findings and machine learning pipeline results to the broader R&D team at the conclusion of the internship.

Research Technician | University of Arizona College of Medicine (Mentors: Dr. Ariel Negron, Dr. Sarmed Al-Samerria; PI: Dr. Sally Radovick) | Phoenix, AZ

June 2024 – August 2024

• Conducted hypothesis-driven research using murine models to study the role of growth hormones in obesity, applying a metabolomic approach for biomarker identification relevant to understanding disease mechanisms.
• Ensured compliance with strict IACUC protocols, managed detailed documentation and reagent inventories to support rigorous, reproducible research.

Research Technologist | Ivy Brain Tumor Center (Mentor: Dr. Gozde Uzunalli; PI: Dr. Shwetal Mehta) | Phoenix, AZ

August 2023 – March 2024

• Executed complex immunofluorescence assays to study protein interactions in glioma stem cells (GSCs) derived from patient samples, contributing to preclinical in-vitro research on therapeutic resistance mechanisms in brain tumors.
• Analyzed imaging data to assess the impact of various treatment strategies on tumor biology, contributing to data interpretation and evidence communication within a preclinical neuroscience research team.

EDUCATION

UNIVERSITY OF PITTSBURGH, SCHOOL OF PUBLIC HEALTH | Pittsburgh, PA

Master of Science in Biostatistics, concentration in Statistical and Computational Genomics | May 2026

• GPA: 3.8
• Relevant Coursework: Statistical Methods for Omics Data, Genomic Data Visualization and Integration, Computational and Statistical Analysis of Biomarkers, Applied Regression Analysis, Introductory Statistical Learning for Health Sciences

UNIVERSITY OF CALIFORNIA, SAN DIEGO | La Jolla, CA

Bachelor of Science in Biology | June 2023

• GPA: 3.8
• Focus: Molecular Biology, Genetics, Cancer Biology