Biomedical Data Science
Showing 1-28 of 28 Results
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Shahira Abousamra
Postdoctoral Scholar, Biomedical Data Sciences
BioShahira Abousamra is a Postdoctoral scholar in the department of Biomedical Data Science at Stanford University, working with Dr. Sylvia Plevritis at the Plevritis Lab. She earned her PhD in Computer Science from Stony Brook University in 2024 under the supervision of Dr. Chao Chen and Dr. Dimitris Samaras.
In her research, she integrates mathematical modeling with computer vision to create more robust solutions, particularly in the context of advancing cancer research and enhancing our understanding of the tumor microenvironment. She leverages computational topology and spatial statistics to provide spatial semantic grounding to complement machine learning models. She publishes in top computer vision, artificial intelligence, and medical image analysis conferences including CVPR, ECCV, ICCV, AAAI, and MICCAI. -
Vasiliki (Vicky) Bikia
Postdoctoral Scholar, Biomedical Data Sciences
BioDr. Vasiliki Bikia is a Postdoctoral Researcher at Stanford University, jointly affiliated with the Institute for Human-Centered Artificial Intelligence (HAI) and the Department of Biomedical Data Science, where she works under the mentorship of Prof. Roxana Daneshjou. She holds an Advanced Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTH), Greece (2017), and a Ph.D. in Biomedical Engineering from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland (2021). Her doctoral work focused on addressing the clinical need for non-invasive cardiovascular monitoring by combining machine learning with physics-based numerical modeling.
Dr. Bikia's research centers on the development of large multimodal models to improve patient outcome prediction. She is also passionate about building patient-facing chatbots that help individuals better understand complex medical information, ultimately aiming to enhance communication and empower patients in their care journey. Moreover, she has contributed to the Stanford Spezi framework, designing and prototyping the Spezi Data Pipeline tool for enhanced digital health data accessibility and analysis workflows. -
Pauline Brochet
Postdoctoral Scholar, Biomedical Informatics
BioPauline Brochet is a French scientist from Souraide, France. She completed her undergraduate studies in Molecular, Cellular and Physiological Biology (BSc, Université Clermont-Auvergne) and earned a Master's degree in Software Development and Data Analysis (MSc, Aix-Marseille Université). Pauline pursued a PhD at TAGC (Theories and Approaches for Genomic Complexity) in Marseille, France.
Under the supervision of Dr. Christophe Chevillard and Dr. Lionel Spinelli, Pauline integrated multi-omics data from human heart tissue to investigate the pathogenic processes associated with Chagas Disease Cardiomyopathy (CCC). Notably, she contributed to the development of ChagasDB, the first database associating key features with the different stages of Chagas disease. Her research identified the involvement of mitochondrial DNA mutations, non-coding RNA, transcription factors, and DNA methylation in various pathogenic processes, all leading to the progression of CCC.
Currently, at Stanford University, under the guidance of Dr. Matthew Wheeler and Dr. Daniel Katz, Pauline is conducting postdoctoral research on multi-omics data analysis as part of the Molecular Transducers of Physical Activity Consortium (MoTrPAC). Her work focuses on identifying key covariable features associated with physical exercise, with the ultimate goal of discovering exercise-mimetic drugs that could help prevent heart diseases. -
Ying Cui
Postdoctoral Scholar, Biomedical Data Sciences
BioI am currently a postdoctoral scholar in the Department of Biomedical Data Science at Stanford Universiry. I received my Ph.D. in Biostatistics at Emory University. Prior to Emory, I received my B.S. in Statistics from Nankai University.
My research, located at the intersection of biomedical data science and statistics, is dedicated to enhancing the integration of statistical insights and data science innovations in biomedical research. I have a broad interest in developing innovative statistical methods and easy-to-use computational tools to understand complex associations using nonparametric and semiparametric methods, with recent work exploring their intersections with machine learning and causal inference to advance precision health. I have also been involved in various collaborative researches in multiple domains, including clinical trials and large language models (LLMs). -
François Grolleau
Postdoctoral Scholar, Biomedical Informatics
BioFrançois Grolleau MD, MPH, PhD is a Postdoctoral Scholar at the Stanford Center for Biomedical Informatics Research. His research work centers on developing and evaluating computational systems that use large language models and other advanced methods from statistics and machine learning to assist medical decision-making.
François is a certified Anesthesiologist and Critical Care Medicine specialist from France. He holds an MPH degree and a PhD in Biostatistics from Paris Cité University. In 2016/2017, he worked as a research fellow in the Department of Health Research Methods, Evidence, and Impact at McMaster University, Canada (Profs Yannick Le Manach and Gordon Guyatt). During his doctorate with Prof. Raphaël Porcher, he utilized causal inference, personalized medicine methods, and statistical reinforcement learning for medical applications in the ICU. -
Dina Hany
Postdoctoral Scholar, Biomedical Data Sciences
BioI am currently a postdoctoral researcher in the laboratory of Prof. Sylvia Plevritis, Department of Biomedical Data Sciences. My current work involves establishing drug testing platforms to evaluate tumor drug responses with respect to the tumor microenvironment and the its spatial organization. I hold a Ph.D. in Life Sciences (Pharmaceutical Sciences) from the University of Geneva, Switzerland, where I conducted research in Prof. Didier Picard's laboratory from 2017 to 2022. Prior to that, I earned a Master’s degree in Pharmacology and Experimental Therapeutics from Alexandria University, Egypt, and a Bachelor’s degree in Pharmacy with honors from Pharos University. My professional experience includes postdoctoral research in molecular pharmacology at UNIGE and a lecturer position in Pharmacotherapeutics and Cancer Biology at Pharos University. I have extensive teaching experience, supervising undergraduate and postgraduate courses, and have successfully guided master's thesis projects. My research has focused on endocrine resistance in breast cancer, utilizing CRISPR/Cas9 screens and exploring drug combinations, resulting in several relevant publications. I have presented my work at numerous conferences and received several awards, including the Ernst et Lucie Schmidheiny Fondation grant and the Ph.D. Booster prize from the faculty of medicine, Geneva, Switzerland. I am an active member of the Life Sciences Switzerland (LS2) and the European Association of Cancer Research (EACR).
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Siyu He
Postdoctoral Scholar, Biomedical Data Sciences
BioI am a postdoctoral fellow in the Department of Biomedical Data Science at Stanford University, where I am advised by Dr. James Zou and Dr. Stephen Quake.
My research interests lie at the intersection of statistical machine learning, computational biology, stem cell engineering, and disease modeling. My mission is to leverage AI methodologies in biomedicine to accelerate our understanding of diseases. I earned my PhD in Biomedical Engineering from Columbia University, where I am co-advised by Dr. Kam Leong and Dr. Elham Azizi. I hold a Bachelor's degree in Physics from Xi'an Jiaotong University. -
Sheng Liu
Postdoctoral Scholar, Biomedical Data Sciences
BioSheng Liu is a postdoctoral fellow at Stanford University. In May 2023, He received a Ph.D. degree from New York University, majoring in Data Science and Machine Learning. His background is in the area of robust and trustworthy machine learning, machine learning for healthcare.
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Pan Lu
Postdoctoral Scholar, Biomedical Data Sciences
Current Research and Scholarly InterestsMy research goal is to build machines that can reason and collaborate with humans for the common good. My primary research focuses on machine learning and NLP, particularly machine reasoning, mathematical reasoning, and scientific discovery:
1. Mathematical reasoning in multimodal and knowledge-intensive contexts
2. Tool-augmented large language models for planning, reasoning, and generation
3. Parameter-efficient fine-tuning for fondation models
4. AI for scientific reasoning and discovery -
Fateme (Fatima) Nateghi
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoc researcher at the Division of Computational Medicine, I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I explored the complexities of machine learning algorithms and their transformative potential in clinical settings. My research focused on adapting these algorithms for time-to-event data, a method used to predict when specific events may occur in a patient’s future.
At Stanford, my work centers on building trustworthy AI systems to enhance healthcare delivery. I develop and evaluate machine learning models that integrate structured electronic health records (EHRs) and unstructured clinical notes to support real-world clinical decision-making. My recent projects include predicting treatment retention in opioid use disorder, improving antibiotic stewardship for urinary tract infections, and enabling digital consultations through large language models (LLMs). I'm particularly interested in embedding-based retrieval and retrieval-augmented generation (RAG) methods that help bridge cutting-edge AI research with clinical practice.
My role involves not just advancing the integration of machine learning in healthcare but also collaborating with a diverse team of clinicians, data scientists, and engineers. Together, we're striving to unravel complex healthcare challenges and ultimately improve patient outcomes. -
Humaira Noor
Postdoctoral Scholar, Biomedical Informatics
BioDr. Humaira Noor is a postdoctoral researcher in the Gevaert Lab with a PhD in glioma genomics from University of New South Wales, Australia. Her expertise spans biomarker discovery, with particular emphasis on prognostic and molecular determinants of glioma treatment-response, radiogenomic model development for early high-risk patient stratification, and the integration of multi-omics and biomedical imaging to advance precision oncology
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Namu Park
Postdoctoral Scholar, Biomedical Informatics
BioDr. Park is a Postdoctoral Scholar at the Division of Computational Medicine at Stanford University, where he is co-advised by Dr. Tina Hernandez-Boussard and Dr. Yair Bannet. He received his PhD in Biomedical and Health Informatics from the University of Washington.
His research focuses on clinical natural language processing and large language models for healthcare. He develops clinically grounded information extraction methods and evaluation frameworks that reflect real-world clinical workflows. His work examines how large language models can be aligned with clinical reasoning and rigorously evaluated for safe and effective deployment in health systems.
Through interdisciplinary collaboration, Dr. Park aims to bridge advances in foundation models with measurable clinical impact, emphasizing reliability, transparency, and scalability in AI-driven healthcare applications. -
Soumyadeep Roy
Postdoctoral Scholar, Biomedical Informatics
BioI am a postdoctoral scholar at the Center for Biomedical Informatics Research of Stanford University, advised by Prof. Tina Hernandez-Boussard.
My primary area of research is natural language processing, with expertise in medical and healthcare applications. My research areas of interest are Foundation Models for Medicine, Generative AI, Text Summarization, and Efficient Pretraining.
I hold a PhD in Computer Science and Engineering from the Indian Institute of Technology Kharagpur, where I worked with Prof. Niloy Ganguly and Prof. Shamik Sural. Here, I was part of the Complex Networks Research Group (CNeRG). My PhD thesis is titled “Domain Adaptation for Medical Language Understanding”, where I developed novel domain adaptation techniques to effectively and efficiently adapt open-domain AI models to the medical domain.
In summary, I have six years of experience working with medical NLP data, which includes clinical trial registry data (2018-2021), medical forum questions (2020-2021), DNA sequence data (2021-2024), biomedical scientific literature (2023 - 2025), clinical data (2021-2023) and EHR clinical notes (2025). My medical AI research experience includes 2.5 years at L3S Research Germany collaborating with Hannover Medical School as well as a 7-month research internship at GE HealthCare Technology and Innovation Center (HTIC) in Bangalore, India. I also presented a tutorial on March 10, 2025 titled "Building Trustworthy AI Models for Medicine" at WSDM 2025 held in Germany.
In my free time, I like hiking, and playing chess or table tennis. -
Bo Xiong
Postdoctoral Scholar, Biomedical Informatics
Current Research and Scholarly InterestsAI, Foundation Models, Biomedical Data Science
