Stanford University
Showing 29,001-29,020 of 37,009 Results
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Yash Shah
Ph.D. Student in Computer Science, admitted Autumn 2025
Current Research and Scholarly InterestsMy research interests lie in developing neuroconnectionist mechanistic models of the brain that deepen our understanding of neural computation and representations. I aim to explore how physiological and anatomical constraints shape cortical topography and, in turn, scaffold development. I am particularly intrigued by observing certain behaviors emerge from mechanistic models, even when the model was not optimized to do so.
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Shagufta Shaheen
Clinical Assistant Professor, Medicine - Oncology
BioDr. Shaheen specializes in the gastrointestinal malignancies and she has expertise in treating neuroendocrine tumors (NETs). Following her fellowship in Hematology and Oncology, Dr Shaheen completed an advanced fellowship in Neuroendocrine tumors from Stanford University. The NET advanced fellowship is first of its kind in United State started under the leadership of Dr Pamela Kunz who is the founding Director of the Stanford Neuroendocrine Tumor Program established in 2015. After completing her advanced fellowship, Dr Shaheen joined Stanford Oncology division as Clinical Assistant Professor. Dr Shaheen is involved in further developing the neuroendocrine oncology program at Stanford which serves as a centre of excellence in the treatment of neuroendocrine tumors. Dr Shaheen is actively involved in clinical research and clinical trials. Dr Shaheen is also involved in taking care of patients admitted to the oncology service as well as resident and fellow teaching.
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Mohammad Shahrokh Esfahani
Assistant Professor of Radiation Oncology (Radiation and Cancer Biology)
BioI lead a computational oncology laboratory that develops machine learning and statistical methods for high-dimensional genomics, with particular expertise in Bayesian and uncertainty-aware modeling to integrate prior biological knowledge with large-scale datasets.
Our research centers on liquid biopsy analytics—especially cell-free DNA (cfDNA)—to noninvasively quantify genetic and epigenetic states relevant to cancer detection, monitoring, and tumor evolution. We developed EPIC-seq, a fragmentomics-based method that uses cfDNA fragmentation patterns to infer regulatory activity and gene expression programs, providing a scalable framework for epigenetic profiling from blood.
A core methodological focus of the lab is enabling reliable inference in extremely low signal-to-noise settings that are typical of cfDNA and early-stage disease. We build robust, interpretable models and benchmarking frameworks that support clinical translation, with the long-term aim of democratizing access to sensitive, minimally invasive cancer diagnostics. -
Hormoz Shahrzad
Graduate, Medicine, Psychiatry and Behavioral Sciences
BioHormoz has worked on many topics in evolutionary computation, both in theory and practice. His master's thesis focused on BLADE, a method for automatically focusing evolutionary search on the most promising part of the genome, with the search distributed over multiple hosts. He is currently a PhD candidate at UT Austin, continuing his research at the Cognizant AI Lab (https://www.cognizant.com/us/en/ai-lab) and as a visiting student researcher at Stanford University, where he is working on his dissertation.
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Mohammed Abdul Saqhlain Shaik
Temp - Non-Exempt, Ophthalmology Research/Clinical Trials
BioSaqhlain is a Research Assistant at the Byers Eye Institute, where he applies his expertise in data science and statistical analysis to ophthalmic research. He holds a master’s degree in Statistics - Data Science from Cal State University East Bay and a bachelor’s degree in Computer Science from Hyderabad, India.
His work focuses on data analysis, management, and standardization of ophthalmic imaging data to support clinical research. He collaborates with researchers to design and execute data-driven projects, ensuring data quality control and validation. Additionally, he assists with data acquisition, statistical analysis, and streamlining research workflows to enhance efficiency in ophthalmology studies.
Saqhlain is particularly interested in leveraging data science to improve research methodologies and facilitate insights in medical imaging.
