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Cameron Miller Cameron Miller holds a PhD in Biostatistics from the Medical University of South Carolina. He enrolled at MUSC in 2012, where he began in the Biochemistry Department, studying the regulation of fatty acid synthesis in Pseudomonas aeruginosa in order to identify novel drug targets. However, the allure of data science was too strong, and after two years he restarted his doctoral program to seek a PhD in Biostatistics. Cameron's dissertation focused on modeling imaging mass spectrometry data, which is marked by high dimensionality, high proportions of zeros, and inherent spatial autocorrelation. As a BD-STEP fellow, Cameron is working with Elizabeth Hauser as part of the Cooperative Studies Program #380. This prospective study has followed a large veteran cohort under colonoscopy surveillance in order to identify risk factors associated with the development of colorectal cancer, as well as precancerous stages. Using polyp-level genetic information, Cameron hopes to uncover novel genetic associations with polyp subtypes. |
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Linden Huhmann Linden Huhmann has a PhD in Environmental Engineering from MIT and spent the year after graduation working at a startup that builds web applications for public health and natural hazards preparedness. They are passionate about applying data and building tools to support effective decision-making, and they recently designed and built the back end of a web application to provide emergency managers with information about a hospital's power status after a hurricane or other outage-triggering event. They have provided analysis in support of federal policy-making and contributed to legislation to protect Iowans from contaminated well water. Linden's prior research has focused primarily on environmental contaminants with the potential to impact human health. As a PhD student at MIT, they studied the effects of naturally occurring arsenic in groundwater in Bangladesh on rice agriculture and worked to better quantify human exposure to arsenic in a Bangladeshi village. They have grown increasingly interested in health research and decision-making after collaborating with public health researchers during their PhD and contributing to web applications to aid in CDC decision-making during their year at a startup. As a BD-STEP fellow, Linden will collaborate with Dr. Nikhil Munshi and Dr. Nate Fillmore to build a predictive model of progression to multiple myeloma, a cancer of the plasma cells, from monoclonal gammopathy of undetermined significance (MGUS), a precursor condition to myeloma. Outside of work, Linden is a Commissioner on the City of Cambridge LGBTQ+ Commission and enjoys many varieties of social dance. |
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Mayada Aljehani Mayada Aljehani, DrPH, MSPH, is a Cancer Epidemiologist at the Lawrence J. Ellison Institute for Transformative Medicine of University of Southern California. Mayada received her MSPH in biostatistics and DrPH in epidemiology from Loma Linda University in California. She conducted her dissertation thesis within the California Cancer Registry (CCR), where she focused on the assessment of the impact of tumor and patients' characteristics on colon cancer survival in the state of California. Using CCR database, she demonstrated differential impact of Bevacizumab and Cetuximab depending on whether the primary tumor location was in the left or the right colon. In 2019, Mayada joined the BD-STEP fellowship at the Boston VA to work on the utilization of cancer case finding project. Her research focus on evolution of cancer treatment guidelines, health outcomes research, health economics, and quality of cancer care. |
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Jacqueline Ferguson Jacqueline Ferguson is postdoctoral research fellow working with Dr. David Rehkopf at The Center for Population Health Sciences through the Big Data-Scientist Training Enhancement Program (BD-STEP) at the Palo Alto VA. She specializes in using secondary data sources such as occupational records, insurance claims, and electronic health records to study the relationship between environmental exposures and population health. Her current research examines patterns of work and sleep disruption on human health, particularly among communities of color and US veterans. This research combines her interests and experience in utilizing secondary data sources to evaluate disproportionate impacts of occupational exposures on disadvantaged communities. Jacqueline's doctoral research examined the impact of specific components of shift work on worker health, and identified night and rotational work as risk factors for hypertension and Type II diabetes. Jacqueline received her doctoral degree in Environmental Health Sciences from the University of California, Berkeley and a Masters of Health Science in Environmental Health from the Johns Hopkins Bloomberg School of Public Health. |
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Linda Diem Tran Linda (Diem) Tran has a BA in Economics, a Master of Public Policy degree, and PhD in Health Policy and Management. Her study on the social determinants of health provides a contextual framework for understanding health care delivery problems, and her training in health outcomes and translational science as a Clinical & Translational Science Institute (CTSI) pre-doctoral fellow has prepared her to conduct applied research in a health care delivery setting. Diem's dissertation compared quantitative strategies for identifying gentrified neighborhoods and examined the impact of gentrification on adult mental health. Her research interests center on the underutilization of care, inequitable access to health care, and disparities in health care stemming from public policies and inefficiencies in delivery systems. Her interests specifically extend to disparities in cancer screening across social stratifications and health care delivery models. |
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Hoda Magid Hoda Abdel Magid, PhD, MHS, is a Post-Doctoral Fellow in the Division of Epidemiology in the Department of Health Research and Policy at Stanford University and the Palo Alto VA. Dr. Abdel Magid received her Doctorate in Epidemiology from the University of California, Berkeley in 2018 and her Masters of Health Science in Environmental Epidemiology from The Johns Hopkins Bloomberg School of Public Health in 2015. Dr. Abdel Magid is eager to acquire the skills, experience, and knowledge to become a leading independent health disparities investigator. Her graduate career research promoted the understanding of new and emerging tobacco products use among adolescents and young adults – a vulnerable population sub-group. Dr. Abdel Magid's current and future research goals will build upon her previous training to further understand high risk chronic disease behaviors to gain insights pertaining to health disparities among safety net populations. |
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Alice Nono Djotsa Alice NoNo Djota, PhD is a postdoctoral research fellow jointly appointed through the Big Data-Scientist Training Enhancement Program (BD-STEP) at Baylor College of Medicine (BCM)and at Michael E. DeBakey VA in Houston, Texas. At BCM, she works with Dr. Chris Amos to investigate the genetic correlation between pancreatic cancer and multiple non-cancer traits using LD Score Regression analysis and cross-trait meta-analysis. Her research with Drs. Michael Kelley and Sara Ahmed at the National Precision Oncology Program (NPOP) focuses on Non-small-cell lung cancer (NSCLC) patients where she investigates the patterns of molecular testing, targeted therapy and therapeutic outcomes in this VA population. Alice holds a PhD in Epidemiology, Human Genetics and Environmental Sciences from UTHealth School of Public Health in Houston. Her dissertation's research focused on functional genomic where she developed a computational gene-based permutation approach to identify cancer driver genes. She applied the method to multiple cancer types using whole genome sequencing data and was able to identify known driver genes and potential new candidate driver genes. |
Tafheem Malik Tafheem Malik honored with a PhD in Biochemistry from the Karachi University, Pakistan in 2012, where Dr. Malik studied neuropharmacological regulation in neurotransmitters synthesis, metabolism and toxicity to identify pathways of the development of adverse effects on human models of Parkinson's disease. Following exploring pathways, Dr. Malik used natural products to target the adverse effects of the neuroactive agents. Tafheem' s dissertation largely focused on identifying the neuroprotective pathways at the treatment of neuroactive agents. Later, at Rush Medical Center, Dr. Malik analyzed a large data set of neuronal network connectivity to understand the pre-progression stages, and the progression of neurodegenerative disorders, includes Alzheimer's disease, and later at the Northwestern University, Feinberg School of Medicine, on Parkinson' s disease in prospective of understanding dementia progression in human. As an adjunct faculty at the Roosevelt University, Tafheem taught Genetics Bases of Disease and Biology, later she joined as an Assistant Professor in the Department of Basic Sciences, National University of Health Sciences, IL., where Dr. Malik taught Physiology for three years and fused on studying neuropharmacological bases of neuroprotective mechanisms in the central nervous. Additionally, Dr. Malik's contribution admired as a Scientist advocate to the legislator at Washington D.C. and carried initiatives that supported design of effective public health policy where she represented the American Cancer Society, Michael J Fox Foundation of Parkinson's Disease, and Society for Neuroscience Nationwide. Dr. Malik currently is a Postdoctoral Research Fellow in the Michael E. DeBakey VA Medical Center, Baylor College of Medicine & Center for Innovation in Quality, Effectiveness and Safety (IQuESt) Houston VA HSR&D; Center of Innovation, as part of the Big Data-Scientist Training Enhancement Program (BD-STEP). As a BD-STEP Advance, Allied Health Fellow, Tafheem' s work with National Precision Oncology Program (NPOP), largely focusses on a large veteran cohort of colorectal cancer and using genetic variance at the cancer stages. Dr. Malik hopes to uncover unique pharmacological targets of the cancer treatment and their subtypes. Tafheem's focus on the mortality rate in HIV/AIDS patient of prostate cancer, additionally, discovering the relationship of Chronic Hepatitis C (CHC) to the Parkinson's Disease using large data of HCV/ CHC surveillance, Direct Acting Antiviral treatment group and hoping to help to improve targets of the new interventions, understand association of neurological disease and contribute to reduce burden of cancer and neurological disorders. |
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Theresa H. Nguyen Theresa H. Nguyen, MD is a T-32 Gastroenterology fellow at Baylor College of Medicine in Houston, TX. She is also an MPH candidate with a major in epidemiology and minor in data science at the University of Texas School of Public Health in Houston. Theresa attended the University of Notre Dame for her undergraduate studies and Baylor College of Medicine for medical school. She also completed her internship and internal medicine residency at Baylor College of Medicine prior to her gastroenterology fellowship. Theresa's research interests include GI cancer epidemiology and outcomes research, specifically in Barrett's esophagus, esophageal adenocarcinoma, and gastric. cancer. In 2019, Theresa joined the BD-STEP Fellowship at the Houston VA to learn the skillset needed to work with big data and data analytics. As a BD-STEP fellow, her goal is develop a database of patients with Barrett's esophagus and esophageal adenocarcinoma and to develop a predictive model for the development of esophageal adenocarcinoma in Barrett's esophagus. |
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Hyunseok Kim Hyunseok Kim, MD, MPH is a gastroenterology and hepatology fellow at Baylor College of Medicine, supported by the NIH T32 grant. He is also a PhD candidate in genetic epidemiology at University of Texas School of Public Health and a fellow at the VA Big-Data Scientist Training Enhancement Program (BD-STEP). He graduated from medical school in South Korea (Yonsei University College of Medicine) in 2012 and did MPH in Epidemiology and Biostatistics at Johns Hopkins in 2013. After his MPH, he spent additional two years as an NCI-funded postdoctoral research fellow at Johns Hopkins before he moved to New Jersey to complete his internal medicine residency at Rutgers New Jersey Medical School. His research interest is genetic epidemiology in liver cancer prevention. He is especially interested in genetic contribution to racial/ethnic disparity in liver disease, cirrhosis and liver cancer risk. His PhD thesis proposal is about admixture mapping of African American cirrhosis patients to identify African American specific genetic loci of cirrhosis-related complications including liver cancer. As a BD-STEP fellow, he would conduct the National Veterans Affair dataset to investigate racial/ethnic disparity in liver disease and how genetic information can be integrated into clinical practice. His ultimate career goal is to become an independent physician-scientist in the field of gastroenterology and hepatology by conducting epidemiological studies to accurately assess the burden of diseases and investigate genetic biomarkers to help ensure that each patient receives an appropriately personalized management. |
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Kent Heberer Kent Heberer's research background and work experience are in biomedical engineering, data science, and clinical research. He received his PhD in biomedical engineering from the University of California, Los Angeles in 2016, while doing research in the biomechanics of pathological gait for a clinical gait and motion analysis laboratory. His dissertation focused on the biomechanics of gait in boys with Duchenne muscular dystrophy, which has potential applications as outcome measures for clinical trials. He provided consulting services in the clinical testing and validation of a class II medical device, which ultimately received FDA approval. During his post-doctoral fellowship, he has received training in big data methods and has become familiar with doing research with electronic medical records. His current research projects are in the fields of pharmacoepidemiology, endocrinology, and oncology. Specifically, he is focused on the generalizability and effectiveness of chemotherapeutics for the treatment of cancers (such as prostate cancer) in the Veterans Health Administration. This project aims to determine if the results published by clinical trials (such as overall or progression-free survival) are comparable to observational data in a more general population of the VHA. The results could then be used to inform clinical practice guidelines and improve the quality of healthcare. He is also involved in research projects that are investigating pharmaceuticals associated with patients with diabetes and at risk for developing diabetes. The analyses aim to use real-world electronic health and pharmacy data to assess the incidence of long-term adverse events (such as cancer) and potential associations with typically-prescribed pharmaceuticals for management of diabetes. |
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Javad Razjouyan Dr. Javad Razjouyan got his MSc and PhD in Biomedical Engineering. He had post-doctoral research training from University of Arizona, college of medicine and Baylor college of medicine before joining the BD-STEP fellowship program. Javad has experience in image processing, signal processing, wearable sensors, machine learning and data mining. He worked in multiple clinically important projects such as fall risk in acute settings, physiological stress assessment, physical activity monitoring, big data mining in knee osteoarthritis, sleep assessment, determining frailty status by machine learning, etc., along with pure mathematic algorithm development like Lyapunov exponent estimation by fuzzy neural network and Poincare map development in recurrent plot analysis. His current project as a BD-STEP fellow is understanding the effect of cancer therapy on the mortality and readmission of patients with congestive heart failure admission. Furthermore, he is interested in the cardiotoxicity of different cancer therapies on cardiac compensatory reserve. |
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Wilmon McCray Dr. Wil McCray received his PhD in Systems Science with an emphasis in Software and Systems Engineering from the Thomas J. Watson School of Engineering and Applied Science, Binghamton University and a Master's in Engineering Management from the State University of New York at Buffalo. Dr. McCray is a second year postdoctoral BD-STEP Fellow at the Western New York VA Healthcare System. Dr. McCray conducts cardiology and oncology research using a data-driven approach. Dr. McCray's research studies and operational projects are as follows: The Natural Language Processing (NLP) Approach to Lung Cancer Stage and Grade Using Electronic Health Record (EHR) Data, The Use of a Data-Driven Approach to Address Healthcare Disparities, and supporting an operational project in Precision Oncology. During his first year of the BD-STEP Fellowship, Dr. McCray developed a body of knowledge in the VA Oncology Management System. Dr. McCray is also a professional photographer and videographer; he uses his media skills to create video documentaries. In his leisure time, he enjoys conducting historical research, genealogy research, and oral video interviews to document history. |
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Andrew Chang Before becoming a BD-STEP Fellow at VA Palo Alto and Stanford University, Andrew was a structural biologist and also a student. He studied RNA tertiary structures and molecular dynamic simulation for his PhD in biochemistry and cell biology at Rice University. Afterwards, while continuing at the same lab as a research technician, he enrolled at UTHealth to acquire his MS/MPH dual-degrees in health informatics and public health. Besides his graduate degrees, he also holds a BS degree in Clinical Laboratory Science from National Taiwan University. Andrew's research during the BD-STEP fellowship focused on the association between glycemic dysregulation and cancer, which he tried to prove by doing observational study and genetic epidemiology by using both electronic health records and genomic data. His long-term goal is to use modern techniques in data and information science, such as machine learning, to further improve the population health through early disease detection and prediction. After finishing his BD-STEP fellowship training, he took a position as a data scientist with CSP2012, Leaver Electronic Health Information to Advance Precision Medicine (LEAP) at CSPCC of VA Palo Alto. He will continue working on his current projects, including one that studies the genetic association between diabetes and pancreatic/liver cancers using data from the Million Veteran Program (MVP). |
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Steven Cogill Dr. Steven Cogill received his PhD in Genetics with a bioinformatics focus from Clemson University in 2016. He also has a master's degree in biochemistry from Indiana University. He has successfully transitioned from bench to computational science. His research interest is in improving patient outcomes through the application of data mining with machine learning both at the macro and molecular level. Currently, he is working on predicting rare diagnostic events through patient history analysis, broad and personalized epigenomic responses to inflammatory stimuli, rapid bacterial identification assay, and improving machine learning approaches with small high dimensional datasets. He currently holds a dual appointment at the VA and Stanford. Steven's research during the BD-STEP program focused on the characterization of sepsis and the creation of new methods for the prediction of adverse events. During BD-STEP Steve was able to focus on publishing his research. Steve was a contributing author on several machine learning studies that classified infections, and he was a lead author paper under review in Plos Comp Bio that used a generative method to predict infection signals. Additionally, he has a paper to be reviewed by Lancet Digital Health on EHR analysis. After completion of his BD-STEP fellowship, Steve took a position as a data scientist for CSP 2012 (LEAP) at the Palo Alto VA. Under Dr. Jennifer Lee's guidance, he will help develop and run study designs that are aimed to have more direct and faster returns on clinical impact. They will use the electronic health records for predictive analytics and trial-like designs, create pragmatic and adaptive trials to demonstrate real-world effectiveness, and develop precision prediction tools and analytics. |
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Rafael Fricks Prior to the BD-STEP fellowship, Rafael received his BS in Biomedical Engineering from the University of Texas at Austin, specializing in imaging and instrumentation with a capstone project that integrated sensors into typodonts used to train dental students. In his graduate studies at Duke University he applied probabilistic modeling techniques to dependability and operations problems in health care, whereby he received his MS and PhD in Biomedical Engineering. During this period Rafael interned with Siemens Healthineers, using models to predict maintenance cycles for medical imaging systems across various modalities. In his dissertation research he simulated patient flows at Duke Eye Clinics to optimize schedules at multi-clinic centers where individual doctors may see as many as 80 patients in a day. During Rafael's fellowship he developed and applied computer vision algorithms for visual tasks in medical imaging. By exploiting features of classification neural networks trained on natural images, he automated labeling of test patterns used to assure CT imaging system quality. Since the outbreak of the COVID-19 pandemic, Rafael pivoted to contribute to various COVID-19 projects, with 5 publications underway, including a method for improving COVID-19 classification in clinically acquired chest x-rays by supplementing new datasets with previous archives of common imaging findings. In Rafael's new role as a computer engineer at the National Artificial Intelligence Institute, he will organize tech sprints to engage various academic and industry partners in producing AI solutions for key issues in the Department of Veterans Affairs. Additionally, he will continue his research in deep learning in radiology by collaborating with the MAVERIC group in Boston, as well as continued involvement with the Carl E Ravin Advanced Imaging Laboratories at Duke University. |
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David Winski David Winski earned a PhD in Computational Biology and Bioinformatics from Duke University where for his dissertation research he studied gene expression during the eukaryotic cell cycle. This research leveraged methods from image/signal processing, statistics and machine learning to analyze the dynamics of gene expression in growing and dividing single-cells. During his time in graduate school he also assisted in teaching undergraduate and graduate courses in computer programming, microbiology and research methods. As a BD-STEP Fellow, David worked on a variety of research and operational projects with the VA's Precision Oncology Program. His research included a retrospective study of outcomes in veterans with non-small lung cancer treated with immune checkpoint inhibitors and a collaborative study examining the mutational profiles of tumors in veterans with head and neck cancer. He also helped to develop extract, transform and load procedures to support the operation of the Precision Oncology Program database. Since completing his fellowship, David has accepted a position as a data scientist at the Boston VA working with the VA's Cooperative Studies Program. In this role he will apply and develop methods from causal modeling and natural language processing to support clinical trials and epidemiological studies being conducted at the VA. He will also remain involved in BD-STEP by developing data science curriculum and serving as a mentor to new fellows. |
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Kyle Lafata Kyle Lafata was a postdoctoral associate and physicist at Duke University working on medical imaging, applied mathematics, and data science. He participated in the BD-STEP fellowship with outside funding. His primary research interests were in the applied analysis of differential equations, stochastic models, and data-driven techniques to biomarker discovery, image reconstruction, and predictive analytics in cancer medicine. Kyle received his PhD in Medical Physics from Duke University in 2018 for his research in the emerging field of Radiomics. Radiomics attempts to identify computational biomarkers hidden within high-throughput medical imaging data, and may be able to noninvasively detect the underlying phenotype of certain tumors. As a BD-STEP Fellow at the Durham VA, Kyle is working to develop robust radiomic signatures that capture and encode disease response to radiation therapy. If translated into clinical practice, these image-based biomarkers would allow clinicians to identify cancer recurrences sooner, leading towards individualized modification of therapy. Kyle graduate in 2020 and became an Assistant Professor of Radiation Oncology at Duke University. |