Maral (Zeynab) Maghsoudi
Computational Biologist | PhD Candidate in Computer Science
I’m Maral (Zeynab) Maghsoudi, a Computational Biologist and PhD Candidate Specializing in Multi-Omics Data Integration and Pathway Analysis.
With a foundation in software engineering and years of experience in bioinformatics, I’ve developed robust, reproducible pipelines to analyze bulk mRNA and multi-omics datasets—contributing to tools like the RCPA R package and collaborating with NASA GeneLab. My work bridges computation and biology to uncover meaningful patterns in complex data. While my current research focuses on pathway-based interpretation of bulk omics, I’m now expanding into single-cell multi-omics and excited to explore new directions in systems biology and data science. My approach is rooted in curiosity, flexibility, and a drive to push the boundaries of biomedical discovery.
Education
Research Interests
Ph.D. in Computer Science
University of Nevada, Reno
GPA: 3.8/4.0
Thesis: Exploration of Single/Multi-Omics Data Integration for Systems-Level Understanding
M.Sc. in Software Engineering
Iran University of Science and Technology
GPA: 4.0/4.0
Thesis: A New Approach to Malware Detection and Classification based on the Combination of Static Structure and Dynamic Behavior
B.Sc. in Software Engineering
University of Arak, Iran
GPA: 3.4/4.0
Project: Investigating of Attacks in Computer Networks
My research focuses on the integration of multi-omics data and its applications to both bulk and single-cell datasets. My background is rooted in bulk multi-omics integration, where I have studied different integration techniques, as well as in practice computationally combined mRNA, methylation, and CNV profiles to extract pathway activity scores and reveal biological insights. I am highly skilled in pathway/gene set analysis, both in developing novel methodologies and applying existing techniques across diverse biological datasets to interpret molecular mechanisms at the systems level. While my primary expertise lies in the analysis of large bulk datasets, I am increasingly interested in advancing single-cell multi-omics integration, with an emphasis on developing scalable, noise-robust solutions that address the unique challenges of single-cell data. More broadly, I am passionate about applying machine learning, statistical modeling, and systems biology approaches to drive discoveries that contribute to precision medicine and translational research.
Technical Skills
Work Experience
Data Scientist & Bioinformatics Research Assistant
University of Nevada, Reno, USA
– Co-led development of the R package RCPA, enabling reproducible and scalable consensus pathway analysis workflows.
– Automated differential expression analysis for microarray/RNA-Seq with GEO support for 1,000+ species.
– Led pathway meta-analysis with NASA GeneLab, revealing mitochondrial dysfunction signatures across spaceflight datasets.
– Developed a personalized multi-omics pathway method using sequential NMF, improving tumor detection in TCGA breast cancer data up to 5%.
– Built an NGS variant calling pipeline for SARS-CoV-2 at Renown Hospital, ensuring accurate, reproducible mutation detection.
– Applied ResNet50/VGG-16 with BEMD for MRI-based breast mass classification, enhancing diagnostic model performance.
Teaching Assistant
University of Nevada, Reno, USA
– Mentored students in mitochondria-centered pathway analysis in collaboration with Purdue University Biomedical Engineering Department.
– Teaching assistant for Embedded System Design Lab for 3 years, managing and mentoring around 60 students each semester, designing lab assignments, and assisting in project-based learning.
C++ Developer | R&D Team Member
AmnPardaz, Tehran, Iran
– Researched and evaluated security solutions for antivirus tools.
– Designed, built, and maintained reliable and efficient C++ code.
– Collaborated with the software development team and provided technical feedback.
C# Developer
GoldIran (Representative of LG Products), Tehran, Iran
– Collaborated on programming the PDA to determine warehouse keeper’s tasks.
– Implemented the Warehouse Handling project to automate inventory checks.
– Collaborated on the Sales project to automate the process of taking customer purchase orders and handling further steps.
– Implemented the Soroush project to link all subsystems automatically through an automated workflow.
Malware Analysis Researcher | C# | Machine Learning
Iran University of Science and Technology, Tehran, Iran
– Developed a hybrid malware detection pipeline using static/dynamic analysis and machine learning.
– Built static analysis module to extract control flow features from system calls.
– Applied dynamic analysis using Pin and Cuckoo Sandbox for behavioral profiling.
– Enhanced malware detection performance by 7% via anti-analysis detection techniques.