Stanford University
Showing 21-30 of 38 Results
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Vivek Maradia
Postdoctoral Scholar, Radiation Therapy
Current Research and Scholarly InterestsI research ultra-high dose rate delivery using proton, x-ray, and electron beams for FLASH preclinical studies, aiming to understand efficacy and safety mechanisms. My work aims to transform cancer therapy and enhance patient outcomes. Leveraging insights from PSI's PROScan facility, I design a compact cyclotron-based proton therapy infrastructure for various radiation therapy setups.
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Sakib Mostafa
Postdoctoral Scholar, Radiation Physics
BioI am a Postdoctoral Research Fellow at Stanford University with a background in computational genomics and deep learning. My research focuses on developing AI-powered tools for genomic analysis, with a particular interest in cancer classification, pangenomes, and genotype imputation. Previously, I worked as a Research Officer at the National Research Council of Canada, contributing to large-scale sequencing projects and machine learning interfaces for biologists. I am passionate about bridging domain biology with cutting-edge computational methods to solve complex biological questions and drive innovation in precision agriculture and healthcare.
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Rohollah Nasiri
Postdoctoral Scholar, Radiation Physics
Current Research and Scholarly InterestsMy current research focuses on developing tumor-on-a-chip models for preclinical radiation therapy research.
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Junming Seraphina Shi
Postdoctoral Scholar, Radiation Biology
BioI am a postdoctoral fellow at Stanford University, jointly mentored by Dr. Mohammad Shahrokh Esfahani and Dr. Md Tauhidul Islam. My research focuses on developing robust statistical machine learning methods for noninvasive, cost-effective cancer diagnostics, with applications in early detection, treatment monitoring, and precision oncology.
I received my Ph.D. from UC Berkeley, where my dissertation centered on advancing biostatistical machine learning approaches for complex biomedical challenges. My work addressed causal inference for continuous treatments, bias and measurement patterns in ICU electronic health records, and deep learning–based biclustering and prediction of cancer-drug responses. Across these projects, I developed interpretable and scalable tools for analyzing high-dimensional, multimodal clinical data.
At Stanford, I continue to build novel statistical learning frameworks tailored to real-world clinical needs—particularly through the analysis of liquid biopsy (cell-free DNA) and cancer imaging data. My current work aims to improve cancer detection and monitoring, with a focus on noninvasive, accessible, and clinically meaningful solutions to pressing challenges in oncology. I enjoy interdisciplinary collaborations and working across fields to drive innovation in biomedical research. Deeply committed to cancer research, I aim to bridge rigorous computational methodology with patient-centered impact by designing tools that are scalable, equitable, and translational.
