The Han Lab Team Members

Summer Han, PhD
Principal Investigator

Dr. Han is an Associate Professor of Neurosurgery, Medicine, and Epidemiology & Population Health in the Stanford School of Medicine. She holds a PhD in Statistics (Yale, 2009) with a concentration on statistical genetics. Dr. Han's research focuses on developing novel statistical methods for understanding the interplays between genes and the environment and for evaluating efficient screening strategies based on etiological understanding. She is the Principal Investigator of the NIH funded project for conducting GWAS, building risk prediction models, and developing decision analysis for cancer screening for second primary lung cancer (SPLC).

Renuka Devi Chintapalli, Medicine, MB and BChir  Postdoctoral Fellow

Renuka is a recent graduate of the School of Clinical Medicine at the University of Cambridge, where she earned her medical degree, earning the Gold Medal for best aggregate performance across the clinical course. Renuka’s research interests include utilizing machine learning and biostatistics to model and predict neurosurgical outcomes in brain tumor and spine surgery patients, with a view to optimizing care in a clinical setting. Currently, she is working on developing accurate, dynamic risk prediction models in benign brain tumor patients and using ‘Big Data’ to determine predictors of outcomes in spinal metastatic and mechanical spine pathology. 

Sayeri Lala, Ph.D.
Postdoctoral Fellow

Sayeri completed her undergraduate and master’s education in Electrical and Computer Science from Massachusetts Institute of Technology (MIT, S.B. 2017 and MEng. 2019) and obtained her Ph.D. in Electrical and Computer Engineering from Princeton University (2024). At MIT, she conducted research on developing deep-learning-based methods to improve fetal brain MRI. For her Ph.D., she developed methods to improve the efficiency of clinical randomized controlled trials using methods based on causal inference and deep learning. Currently, Sayeri is developing solutions based on machine learning to analyze wearable-based physical activity data from spine surgery patients for improved postoperative monitoring and to analyze time-to-event data for dynamic risk prediction. 

Zhenjiang Fan, Ph.D. 
Postdoctoral Fellow

Zhenjiang completed his Ph.D. in Computer Science at the University of Pittsburgh. His dissertation, "Elucidating complex biological interactions using computational techniques”, focused on discovering biological causal relationships and learning biological interactions and states across various levels by using computational tools such as algorithms, statistical methods, machine learning, deep learning, and other artificial intelligence models. Beyond his dissertation, Zhenjiang's research interests extend to predictive analysis of biomarkers in biological and clinical datasets, as well as the development of applications and tools to advance computational biology research. Currently, his primary focus lies in the development of a novel cell-cell communication method and the exploration of interactions within single-cell and spatially resolved transcriptomics for brain metastasis.

Justin Lee, MPH
Biostatistician

Justin completed his MPH degree at the University of Miami where he mostly focused on data management and statistical analyses of infectious diseases with UM’s Clinical and Translational Sciences Institute and the Miami-Dade County Health Department. He is currently working on numerous projects that include developing predictive models for second primary lung cancer and parallelizing GWAS through Stanford’s computer cluster (Sherlock) with The Han Lab.

Victoria Ding, M.S.
Senior Biostatistician

Victoria completed her M.S. in Biostatistics at the University of Washington, where she engaged in collaborative research pertaining to mental health and gerontology.  She is currently working on several risk modeling projects for second primary lung cancer and is developing an R Shiny app for the prediction models. 

Victor Ritter, Ph.D.
Senior Biostatistician

Victor received his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in August of 2021 and is interested in statistical methods for non and semiparametric inference for time-to-event data with a focus on competing risks, cure rate, and multistate models. He developed methods for time-to-event analyses under complex sampling designs and has collaborations in various biomedical research areas. He has a particular interest in analyses of national health surveys, newborn medicine, and hospital pediatrics. Currently, Victor is currently working on estimating the parameters of the natural history model for second primary lung cancer using the NCI SEER cancer registry data.

Ingrid Luo, M.S.
Biostatistician

Ingrid completed her M.S. in Biostatistics at the University of Washington. Her current research projects include characterizing the tumor microenvironment of lung cancer patients with single-cell RNA sequencing.  

Yuhan Liu, M.S.
Biostatistician

Yuhan received her master’s degree in Biostatistics from Duke University. After that and prior to Stanford, she worked as a biostatistician at the Vanderbilt University Medical Center, where she worked on ophthalmology and pediatrics projects. In the Han Lab, Yuhan is working on analyzing SEER-Medicare data to evaluate the impact of cancer treatment on the risk of second primary lung cancer.

Mengrui (Ray) Zhang
Senior Biostatistician

Mengrui completed his Ph.D. degree in Statistics from the University of Georgia in May 2022. Mengrui’s research focuses on leveraging advanced statistical and machine-learning techniques to extract meaningful insights from complex biological datasets. His research interests include bioinformatics, deep learning/machine learning, statistical testing, high-dimensional data, non-parametric modeling, time series analysis, and spatial statistics. Additionally, he is also interested in developing new methods, tools, and pipelines for various kinds of biological datasets, especially in single cell, spatial transcriptome, RNA-Seq, metagenomics, and proteomics to support drug discovery and development.

Chloe Su, B.S.
Graduate Student in Epidemiology and Clinical Research

Chloe received her Ph.D. in Epidemiology at Stanford. She completed her B.S. in Molecular, Cell, and Developmental Biology with a minor in Biomedical Research at UCLA, where she worked on hematopoietic stem cell development. Prior to Stanford, she was a Staff Research Associate at UCLA Health, managing clinical trials in early lung cancer detection and prediction. Currently, she is working on various projects to build prediction models for brain metastases and recurrence in lung cancer patients.

Anna Graber-Naidich, Msc, PhD
Senior Data Scientist

Anna obtained her PhD in healthcare operations research from the University of Toronto, focusing on Canadian primary care services. She has a MSc in biostatistics and developed estimation techniques for survival analysis for correlated and missing data for breast cancer patients. Her current research projects include developing and evaluating risk-stratified screening strategies for second primary lung cancer among breast cancer survivors.

Fatma Gunturkun, Ph.D.
Senior Data Scientist

Fatma obtained her PhD in statistics from Dokuz Eylul University, Turkey, in 2019. Her academic interests cover several areas, such as predictive modeling, analyzing ECG signals, medical image analysis, statistical modeling, machine learning, deep learning, and data visualization for clinical decision-making. Currently, Fatma is working on abstracting recurrence after lung cancer diagnosis using CT imaging and radiology reports. 

Andrew Cheng, B.S.
Graduate Student in Computer Science

Andrew is a master's student in Computer Science at Stanford with a concentration in artificial intelligence. He completed his bachelor's degree from Rutgers University-New Brunswick with a double major in Computer Science and Biomathematics, where he worked on spatial transcriptomics and protein structure prediction. Currently, he is interested in developing advanced statistical and machine learning methods for spatially resolved transcriptomics data.

Rika Terashima, M.D.
Graduate Student in Epidemiology and Clinical Research

Rika is a master’s student in epidemiology and clinical research at Stanford. She completed medical school at Gunma University in Japan. Prior to her studies, she worked as a physician at the Center Hospital for the National Center for Global Health and Medicine located in Tokyo. Currently, she isworking on projects for assessing tumor burden levels and building prediction models for lung cancer metastases.

Megan Chang, B.A.
Graduate Student in Epidemiology and Clinical Research

Megan is a master’s student in epidemiology and clinical research at Stanford. She completed her undergraduate education at Pomona College (Claremont, CA) in May 2023, where she majored in Molecular Biology and minored in Music. Currently, Megan is abstracting patient data to help develop NLP models that can assess tumor burden levels to aid in lung cancer treatment decision-making.

Rakshit Kaushik
Undergraduate Student

Rakshit is an undergraduate student at Stanford University and is engaged in the study of Mathematicsand Computer Science. With a profound interest in data science and artificial intelligence, he hasshown versatility by contributing to both academic research and industry projects. His expertise lies in applying computational methods to solve complex problems. Presently, Rakshit is involved in the Tumor Burden project, where he is applying his skills in data analysis to advance research in this critical area.

Julie Wu, M.D., Ph.D.
Medical Oncology Fellow

Julie is a Stanford medical oncology fellow specializing in thoracic malignancies. She completed her undergraduate education in Molecular Biology with a certificate in Biophysics at Princeton University and went on to UCSF for MD/PhD, where her PhD focused on cancer cell biology. She then completed internal medicine residency at Vanderbilt University, where she led a project on tumor drivers in metastasis. Currently, she is working on genomics of brain metastasis development in lung cancer patients and development of a predictive model of brain metastasis with the goal of optimizing brain MRI surveillance.

Eunji Choi, MPH, PhD (former postdoc/instructor)

Assistant Professor in Population Health Sciences at Cornell University

Aparajita Khan, PhD (former postdoc)

Assistant Professor in Computer Science at the Indian Institute of Technology (IIT) Roorkee 

Chloe Su, B.S. (former PhD student in Epidemiology)

Senior biostatistician in the Quantitative Sciences Unit at Stanford

Anya Fries, B.S. (former MS student in MS&E)
PhD Student in Statistics at ETH Zürich

Ines Dormoy, B.S. (former MS student in ICME)

Software Engineer at Waymo 

Jane Hua, B.S. (former MS student in Epidemiology)

Clinical Coordinator at Stanford University School of Medicine

Sophia Luo, B.S. and M.S.

Medical student at the Vanderbilt University School of Medicine

Jacqueline Aredo, MD

Resident at UCSF

Hugo Kitano, MS

Software Engineer in Seer

Eric Chow, MS

Medical student at Chicago Medical School in Rosalind Franklin University

Matthieu de Rochemonteix, MS

Quantitative Research Analyst at Citadel

Nilotpal Sanyal, PhD

Natasha (Purington) Shamas, MS

Senior Data Scientist at Genentech