PhD Students

Maryam Abdel Magid graduated in public health and biology from Santa Clara University (SCU). She conducted undergraduate research on the impact of social policy on the unhoused population in Santa Clara County and opioid addiction on SCU’s campus. This research led her to Stanford University’s Center for Dissemination and Implementation (CDI), where she analyzes state and NIDA-funded opioid addiction treatment and SAMHSA-funded projects spanning (K-12) school mental health. Her work at CDI, combined with an internship at California’s Department of Public Health focusing on disease reporting and surveillance, has enriched her expertise in implementation and data-driven science. Now a first-year PhD student in Biomedical Informatics and Medical Education at the University of Washington with an NLM pre-doctoral fellowship, her research spans public health informatics, consumer health informatics, and social theory. She aims to improve substance abuse treatment and health equity through mixed methods.

Ronald W. Buie received his MS in Biomedical and Health Informatics, and MPH in Health Services: Health Systems and Policy, both from the University of Washington. He is currently pursuing his PhD in Biomedical and Health Informatics while working as an epidemiologist in Public Health Seattle & King County’s Assessment, Policy Development, and Evaluation Unit. His dissertation is a study of analytics teams in population health and health care settings, with an aim of describing the work, structures, and interdependencies of these teams. His professional work centers around the evaluation of health care and population health programs where he develops analytics systems in support of continuous improvement processes, systems integration, and informed decision making by grantors, program leadership, researchers, and communities.

Arjun Chakraborty received a Bachelor of Arts in Biochemistry from Northwestern University and a Masters in Bioinformatics and a Masters in Data Science from UC Berkeley. He has pursued research on generating donor-specific transplant tolerance after renal transplantation by coculturing donor B cells and recipient T cells. He has also pursued research on using graph databases to build a biomedical knowledge network which can enable more precise disease diagnoses and delineate novel mechanisms of disease. Arjun moved to Seattle to pursue a PhD in Biomedical Informatics at the University of Washington. During his graduate studies, he hopes to apply novel NLP techniques to make tools which uses electronic health records to make advancements in translational bioinformatics.

Brian Chang, MD is a graduate of Rutgers New Jersey Medical School and also received a MSc in biomedical informatics from NYU School of Medicine. During his clinical rotations, Brian saw opportunities in informatics to optimize delivering care and decided to pursue a PhD at University of Washington. His research interests are in translational artificial intelligence, where his dissertation work focuses on developing mult-modal models to detect osteoporotic compression fractures for opportunistic screening of osteoporosis.

Chak Charoensilpchai earned his degree in pharmacy and gained experience as a pharmacist at the Food and Drug Administration (FDA) in Thailand. He received the Royal Thai Government Scholarship to study abroad and graduated with an MPH in Health Management and Policy from Oregon State University. Currently, Chak is pursuing a PhD in Biomedical Informatics and Medical Education (BIME) at the University of Washington with a focus on the utilization of electronic health records in primary care, public health informatics, and consumer health informatics.

Yile Chen received her BS in Applied Biology from Zhejiang University and worked as a bioinformatician for three years in China analyzing high-throughput genomics data. She then pursued an MS in Biostatistics at the University of Michigan. She is now a Ph.D. student in BIME at the University of Washington and a trainee in the IGVF consortium working on understanding genomic variation’s impact on genome function. Her general interest is in integrating molecular omics data with ML algorithms to gain a better understanding of clinical data.

Ya-Lin (Amber) Chen earned her PharmD and Master’s degree in biomedical informatics at Taipei Medical University. During her Master’s program, she published the first machine learning study on medication adherence in Taiwan, focusing on insulin usage for type 2 diabetes patients. In addition to academic achievements, she contributed to the practical aspects of healthcare by […]

Feng Chen received his MS in Biomedical Informatics from Harvard Medical School and his BS in Computer Science and Biochemistry from Brandeis University. His previous research includes developing machine learning and graph representation learning for protein-protein interaction prediction, as well as bias detection and mitigation in EHR data. He is currently pursuing a PhD in Biomedical and Health Informatics at the University of Washington, focusing on the application of deep learning and natural language processing in clinical settings to detect implicit bias in healthcare providing, especially in EHR data and patient-provider conversations.

Ray Chung received his MPH in Nutritional Science and Dietetics from the University of Michigan at Ann Arbor. Ray is a registered dietitian and has experience working in clinical settings, sports performance centers, food tech companies, and research institutions. He is currently pursuing his PhD in Biomedical and Health Informatics at the University of Washington, advised by Professor Andrea Hartzler. Ray’s research interest lies in the intersection of human-computer interaction, computer science, and consumer health informatics. He is working on leveraging human-centered design techniques and machine learning to build holistic mhealth applications.

Xiruo Ding received his BS in Environmental Science in Tongji University and M.S. in Biostatistics in Duke University. His experience includes predictive modeling and utilization of EHR. His current research is on deep learning and natural language processing, for both theoretical part and their application in biomedical domain. A recent focus is to address confounding and distribution shift, with the hope to make models robust and accurate.

Joyanne Huang holds a BEng in Biomedical Engineering and an MS in Health Data Science. She is currently pursuing a PhD in Biomedical Informatics and Medical Education at the University of Washington. With a multi-disciplinary background, her research focuses on modelling population health patterns and advancing personalized medicine in real-world settings. She is particularly interested in developing advanced statistical methodologies and computational approaches for health data analysis. Specifically, she is exploring robust methods for longitudinal data and data-driven approaches to multimodal EHR.

Yein Jeon’s research interest is the application of deep learning to multi-modal health data. Previously, she earned her B.S. in Statistics and M.S. in Data Science from George Washington University. Following her experience in the industry, she pursued an M.S. in Biostatistics at Georgetown University to transition into the field of public health. During her time at Georgetown University, she worked in the lab, focusing on analysis for metabolite biomarker selection and cancer classification utilizing deep learning. Outside the lab, she performed quantitative bias analysis of the confounding effects between NOAC and GI bleeding compared to Warfarin. After completing M.S. in Biostatistics, she joined Johns Hopkins University, participating in epidemiology research related to diabetes and cardiovascular disease using data from wearable devices, surveys, and EHR. Currently, she is pursuing a Ph.D. in Biomedical and Health Informatics at the University of Washington.

Kaylin received a BS in Computer Science and a BA in Linguistics from University of Maryland, and a MS in Health Informatics from Yale University. When pursuing her master’s, she worked on research related to biomedical literature curation, mental health trends on social media, vaccine hesitancy, and user-centered design for digital health. As a PhD student, Kaylin focuses on applying NLP/ML to address critical challenges in biomedical domains, including mental health, health equity, and digital health interventions, etc. She aims to develop interdisciplinary solutions that integrate technology and healthcare to benefit diverse communities.

Peter Ju received a BS in Molecular and Cellular Biology and a minor in Psychology at the University of Illinois at Urbana-Champaign. He then worked as a research technician for the Department of Radiology at Northwestern University. He assisted with lab operations and imaging analysis for cardiac magnetic resonance imaging projects. After working for two years, Peter moved to Seattle to pursue a PhD in Biomedical Informatics at the University of Washington. During his graduate studies, he hopes to strengthen his computational skillset to conduct exciting research projects in translational bioinformatics.

Oliver Li received his BS in Biology from National Chiao Tung University in 2018. During his years in undergraduate, he started up a company called, Vernace Tech, focusing on precision agriculture using machine learning, an IOT system, and an eco-friendly pesticide. Currently, Oliver is pursuing a PhD in Biomedical Informatics and Medical Education (BIME) at the University of Washington exploring how social media users react to misinformation with the aim to assist social media researchers to identify potentially harmful content in the early stage on the social media.

Luna Li earned her degree in Basic Medical Sciences from Sun Yat-sen University, with a minor in Statistics, followed by a Master’s in Biomedical Informatics at Harvard. She has previously engaged in research on medical coding within large-scale EHRs, focusing on uncovering patterns of chronic disease progression. Currently, she is pursuing a PhD in Biomedical and Health Informatics at the UW. Her current research focuses on developing and refining reproducible gene regulatory network models for cancer patients. In addition to this, she maintains a keen interest in various aspects of biomedical informatics, including medical data analysis and clinical decision support.

Christine Lin received her MS in Data Analytics Engineering from Northeastern University and BS in Applied Statistics from Purdue University. Currently, she is pursuing a Biomedical and Health Informatics Ph.D. degree at the University of Washington, with a specialization in data science. She is also working as a Data Engineer at the Allen Institute of Brain Science where she integrates research data from various sources and builds software pipelines that support scientific research. Her main research interests are to apply data science methods and develop relevant applications for bioinformatics research and computational genomics. She is also interested in EHR, ontologies, machine learning, and the use of artificial intelligence in the research field.

Dr. Zoljargal (Zoey) Lkhagvajav is a first-year PhD student in the Biomedical and Health Informatics Program at the University of Washington. She graduated from the Mongolian National University of Medical Sciences as a physician and mainly worked at a non-governmental organization in Mongolia for works related to public health and eHealth. Later she earned an MPH degree with a Certificate in Public Health Informatics at Johns Hopkins University. Currently she is working as a Graduate Research Assistant at the International Training and Education Center for Health (I-TECH). She has a broad range of research including health data interoperability standards, specifically FHIR standard, and policy and planning of digital health system implementation. She is interested in how digital health and health informatics solutions could be applied and implemented to low and middle-income country settings to support evidence-based decision making, provide clinical decision support to physicians, improve quality of care to the patients and advance the health system as a result.

Jenni Logue received her BS in Biology from the University of Washington. Prior to starting her PhD, she served as Program Manager in an infectious disease research lab, where she led multiple studies and clinical trials focused on emerging infectious diseases, immune responses to respiratory viral infections, and Long COVID. Jenni has extensive experience in managing large teams, overseeing research budgets, and advancing research initiatives from concept to completion. Her background in clinical and immunological research informs her current interest in leveraging multi-omic approaches to understand the underlying causes of chronic conditions like Long COVID.

Namu Park is a PhD student in Biomedical Informatics at the University of Washington. He earned his BS in Convergence Software from Sogang University and his MS in Data Science from Yonsei University in South Korea. With prior experience as a researcher at Asan Medical Center, he has applied various natural language processing (NLP) techniques to medical data, further cultivating his interest in Clinical NLP. His primary research goal is to develop automated methods to extract valuable information from clinical notes such as radiology reports to enhance patient care.

Danner Peter is a PhD student in Biomedical Health Informatics. He is an enrolled member of the Diné (Navajo) from New Mexico, and has worked in various capacities to improve the health and well-being of Indigenous communities across the US. A graduate of the University of New Mexico, Danner has a Bachelor of Science in Biology with a minor in Navajo Linguistics. He completed a Master of Public Health from the University of Hawai’i at Mānoa with a specialization in Native Hawaiian & Indigenous Health. As a Program Specialist at the Northwest Portland Area Indian Health Board’s Tribal Epidemiology Center, he oversees the VacciNative program to increase vaccination rates among American Indian/Alaskan Native adults. His research interests include applying data science and artificial intelligence to public health challenges, especially in the areas of infectious diseases, disease surveillance, and predictive epidemiology. In his spare time, he enjoys getting lost in the outdoors, woodworking, baking, knitting, and being involved in the local AI/AN communities.

Aishwarya Raj is a PhD student in Biomedical and Health Informatics, and received a Bachelor of Science in Biochemistry from the University of Illinois Urbana-Champaign and a Master of Science in Law from Northwestern University. Her professional work has spanned the pharmaceutical and biotechnology industry where she consulted on business insights, data privacy, and regulatory compliance. Aishwarya hopes to combine her interests in bioinformatics, law, and data visualization for applications in public and consumer health.

Ashmitha Rajendran completed an MS in Quantitative Biology from Northwestern University, and BS in biomolecular engineering and neuroscience from UC Santa Cruz. Since, she worked at the Knight Cancer Institute and a breast cancer risk prediction startup, Gabbi Inc. Ashmitha works with Seattle Children’s Research Institute on research projects which involve mapping pediatric brain cancer initiation during embryonic neurodevelopment with genomics.

Ojas Ramwala is a Biomedical and Health Informatics Ph.D. student at the University of Washington, Seattle. His research focuses on developing parameter-efficient, multimodal, and interpretable Deep Learning algorithms for Biomedical Image and Signal Processing, Bioinformatics, EHR Analysis, Genetics, and other diverse healthcare applications. He received a B.Tech. in ECE from the National Institute of Technology Surat, India, in 2021. During his undergraduate, he has been fortunate to work as an AI Research Intern at the Indian Institute of Science (IISc), the Indian Space Research Organization (ISRO-IIRS), and the Council of Scientific and Industrial Research (CSIR-CSIO). Before beginning his Ph.D., he spent a year at New York University developing methods to optimize AI algorithms for Bioinformatics and Medical Image Processing applications. Currently, he is pursuing Breast Cancer research under the wonderful guidance of Dr. Christoph Lee and Dr. Sean Mooney.

Nick Reid is a PhD student, who previously earned a Master of Health Informatics at the University of Michigan and a Bachelor of Arts in Art Practice from the University of California at Berkeley. Currently, Nick is evaluating a patient-centered decision support tool for cystic fibrosis advanced lung disease and the implementation of home spirometry in cystic fibrosis clinical research. Nick researches how information systems support people to manage chronic health conditions using design, engineering, mixed, and qualitative methods — they aim to improve health information accessibility.

Xinyang Ren received her BS in electrical and computer engineering from Shanghai Jiao Tong University in 2020. Currently, Xinyang is pursuing a PhD in Biomedical Informatics and Medical Education (BIME) at the University of Washington with a focus on mental health informatics. Her research projects involved utilizing large language models to analyze text data for diagnosis of mental health disorders such as depression and suicide prevention.

Aparajita Saha is pursuing her PhD in Biomedical Informatics and Medical Education at the University of Washington. She holds an MD from Bangladesh and an MS in Genetic Epidemiology from the University of Washington. Drawing on her clinical expertise in managing chronic infectious diseases, her past research has investigated innate and adaptive immune dysregulation caused by TB-HIV coinfection in pregnant populations within endemic settings. Currently, she is affiliated with Institute for System Biology, where she utilizes high-dimensional analytical techniques on electronic health records data to uncover sequelae associated with rare immune-mediated inflammatory diseases. Her research interests focus on leveraging large language models to integrate multimodal datasets and identify severity metrics for autoimmune diseases.

Ayana Sarrieddine is a National Library of Medicine (NLM) Pre-Doctoral Fellow pursuing a PhD in Biomedical and Health Informatics at the University of Washington. She has five years of experience in clinical data management, health informatics research, and technology integration. In her previous roles as Clinical Data Manager at Amgen, Inc. and Senior Clinical Data Manager at FibroGen Inc., Ayana led global Phase 0 through III clinical trials, overseeing data integration, validation, and analytics across therapeutic areas such as Oncology, Rare Disease, Metabolism, and Inflammation. Ayana holds a Bachelor of Science in Biological Sciences from the University of California, Santa Barbara, and a Master of Science in Informatics from San José State University, where she received the Director’s Award for Excellence in Intellectual Inquiry. Her doctoral research focuses on developing innovative informatics solutions to improve clinical trial efficiency and patient outcomes through advanced data management methods.

Zhaoyi Sun is PhD student in Biomedical and Health Informatics at the University of Washington with current research focused on applying large language models (LLMs) to medical question answering and error detection. He earned his BS in Chemistry from Nanjing University and an MS in Health Informatics from Cornell University. While at Weill Cornell Medicine, he evaluated LLMs on various clinical tasks, including radiology report generation, and evidence summarization. He is also interested in multi-modal deep learning with biomedical images and text. His research goal is to enhance the efficiency and effectiveness of natural language processing techniques in clinical domains.

Wesley completed his undergraduate studies in Biological Sciences at the University of Southern California, and continued on to do a Master’s in Neuroimaging and Informatics there. Now a graduate student in the BHI program, his current work mainly involves using MRI breast cancer screening images along with clinical factors for cancer risk modeling. He is interested in learning more about medical imaging informatics and statistical methods. On weekends, he enjoys practicing archery at the Husky Archery Club, and trying out new cooking recipes at home.

Amanda Tsai received a Bachelor of Science in Mathematics with a specialization in computing from UCLA and pursued a Master of Science in Biostatistics at Columbia University where she worked as a graduate researcher at the Center for Statistical Genetics. At the center, Amanda focused on developing methods to identify causal variants for Alzheimer’s Disease. Amanda is now a Ph.D student in the BIME program at the University of Washington and am interested in working on projects involving translational bioinformatics, mainly in the field of Alzheimer’s Disease, by coordinating clinical and genetic data.

Bhargav Vemuri received his BS in medical sciences and MPH in biostatistics from the University of Cincinnati. During that time, he completed several bioinformatics projects including his undergraduate senior capstone, “Integrative omics to discover candidate therapeutics for glioblastoma and systemic sclerosis”, and master’s thesis, “Identification of prognostic metabolic classifier in localized clear cell renal cell carcinoma”. Currently, Bhargav is a PhD student in biomedical and health informatics at UW and a member of the Hadlock Lab at the Institute for Systems Biology. He is interested in applying deep learning methods to longitudinal real-world data to identify multivariate trajectories of treatment response in chronic disease.

Serena Xie received a BS in Mathematics and Economics at the University of California, Davis, and her MS in Information System Management at Carnegie Mellon University. Her research projects converge at the intersection of health equity, informatics tools, and natural language processing. Her research centers around engaging patients and providers in the development of informatics tools. She is dedicated to devising scalable strategies for the cultural adaptation of digital health solutions. Through her work, Serena strives to enhance mental healthcare accessibility, leveraging the power of informatics and NLP in the process.

George received a BS degree in Bioengineering at University of California, San Diego. Now he pursues a Ph.D. degree in BIME at University of Washington, with a data science focus. His current research involves analyzing text coherence for the detection of neuropsychiatric conditions. He hopes that through informatics technologies, he can catch unique linguistic patterns from patients and help with early detection or treatment monitoring.

Faisal Yaseen is a Ph.D. student in Biomedical and Health Informatics with a research interest to develop a comprehensive approach to managing metastatic non-small cell lung cancer by integrating immune checkpoint inhibitors, chemotherapy, and radiation therapy, with a focus on personalized treatment strategies. Previously, he received a bachelor’s degree in electrical engineering from the University of Management & Technology and a master’s degree in electrical engineering from Lahore University of Management Sciences. Additionally, he holds a master’s degree in health informatics from the University of California, Davis. He has actively participated in a broad range of projects utilizing graph signal processing and convex optimization for wireless network optimization, and deep learning applied to the analysis of radiological images in tuberculosis.

Avery is a PhD student in Biomedical and Health Informatics at the University of Washington. She had a MBBS degree in medicine and got an MHS in Epidemiology at Johns Hopkins Bloomberg School of Public Health. Avery has experience in developing pipelines for analyzing high-dimensional data, conducting large-scale GWAS, and applying machine learning techniques to address biomedical challenges. Her current research focuses on leveraging large language models (LLMs) to analyze electronic health records, generating summaries of clinical notes, and using unstructured data for predictive modeling.

Siyang (Sunny) Zeng received a BA in Applied Mathematics and Statistics from the University of California Berkeley and a MS in Health Informatics from the University of San Francisco, and is pursuing a PhD in the department of Biomedical Informatics and Medical Education (BIME). Her research focuses on utilizing electronic health records and informatics techniques to provide better understanding of disease progressions as well as clinical decision support. Her projects involve machine learning, data mining, and user studies.

Sihang Zeng is a PhD student in Biomedical and Health Informatics at University of Washington. Previously he received bachelor’s degree in Electronics Engineering with a minor in Statistics at Tsinghua University in China. Sihang’s research interests lie in the intersection of deep learning and biomedical informatics. His research mainly focuses on representation learning and large language models, including biomedical term representations, protein and variant representations, and patient representations, with applications including term clustering and rare disease diagnostics. Sihang developed a fine-grained term representation model CODER++, which achieved best performance in the task of term clustering and was used to construct a large-scale biomedical knowledge graph BIOS. Sihang is currently focusing on patient representation using temporal EHR data, with the application in prostate cancer survival analysis, and the broad use of large language models in biomedical domain.

Wenyu Zeng is a first-year Ph.D. student in the Biomedical and Health Informatics program at the University of Washington in Seattle. Wenyu received her Bachelor’s degree in statistics from the University of Pittsburgh, and her Master’s degree in data science from The George Washington University, where she cultivated her interest in the implementation of machine and deep learning techniques using biomedical data. Her past experiences at the National Center for Advancing Translational Sciences (NCATS) were focused on building predictive models using various kinds of biomedical data. She helped predict the toxicities of chemical compounds from the Tox21 dataset, as well as the severity levels for patients with COVID-19 using their pre-COVID metabolomic data. Zeng’s research interest focuses on integrating multi-omics data with machine learning algorithms to better interpret biomedical data.

Michael received his BS in chemical biology from Xiamen University in 2019 and MA in biomedical informatics from Columbia University in 2021. He is interested in informatics applications for patients and consumers, including those based on electronic health records. His recent research projects involved patient decision aids on breast cancer prevention, health information resources on chronic diseases, and automated risk calculation based on electronic health records.

Weipeng Zhou received a BA degree in Computer Science and Statistics at the University of Wisconsin-Madison in 2019. He is interested in applying natural language processing techniques (BERT, large language models, etc.) to solving medical problems. His recent research projects include multi-modal prediction of ICU in-hospital mortality prediction using patient records and clinical notes, multi-modal prediction of out-of-hospital cardiac arrest using patient records and clinical notes, segmentation of clinical notes, and coding circumstances preceding female firearm suicides from suicide reports.