Epidemiology and Biostatistics
Elizabeth K. Rasch, PT, PhD
A physical therapist for nearly 40 years, Dr. Rasch was one of the first clinical specialists in neurology to be board certified by the American Board of Physical Therapy Specialties. From 2001 to 2007 she was a service fellow in the Office of Analysis and Epidemiology at the National Center for Health Statistics, CDC. During this time, she was actively involved in the Washington Group on Disability Statistics, an international group developing measures of disability suitable for censuses and surveys worldwide. As Chief of the Epidemiology and Biostatistics Section, Dr. Rasch has administrative responsibility for budget management, identifying and hiring scientific and administrative staff, planning and procurement of contracts, development and execution of institutional agreements, ensuring adherence to Federal regulatory requirements, supervision of staff, promoting staff development, as well as assigning, monitoring and coordinating work of the Section. She has been instrumental in conceptualizing and implementing an inter-agency agreement with the Social Security Administration to improve their disability determination process. As a result, NIH has received access to an unprecedented volume of data from SSA to develop novel, systematic, data-driven analytic tools to augment SSA’s determination processes. In addition, NIH is working collaboratively with Boston University to develop computer adaptive tests which could potentially inform SSA’s disability determination processes. Since SSA is responsible for administering the largest Federal programs serving people with disabilities, NIH has a unique opportunity to develop methods that may meaningfully improve the lives of millions of individuals with disabilities who apply for SSA disability benefits. Dr. Rasch has co-authored over 50 articles. She is a member of the Editorial Board for the Disability and Health Journal and the Physical Therapy Journal.
Chunxiao Zhou, PhD
Dr. Chunxiao Zhou, a Computer Scientist with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center, has over 20 years of experience participating in, supporting, and leading projects involving research. With expertise spanning data mining, machine learning, natural language processing, biostatistics, computer vision, image processing, and applied mathematics, Dr. Zhou’s work has provided him with a wide range of experiences and knowledge of research from design to implementation. Since 2011, Dr. Zhou is collaborating with SSA and developing advance analytical research with machine learning, data mining, and natural language processing (NLP) techniques in four areas: Case Prioritization and Eligibility, Adjudicator Support Tools, Quality and Productivity Tools, and Functional Assessment Tools. Dr. Zhou received his Ph.D. in Electrical Engineering and MS. in Statistics from the University of Illinois, Urbana-Champaign.
Pei-Shu Ho, PhD
Pei-Shu Ho is a health services researcher and biostatistician in the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. She has been under contract with the Epidemiology and Biostatistics Section to contribute to the RMD inter-agency agreement with SSA since 2009. In this capacity, she collaborates with her colleagues at NIH to develop analytic approaches to enhance the SSA Disability Determination Process. Currently, she supports research in natural language processing through the development of annotation guidelines and the annotation of functional information documented in clinical notes. She also provides analytic and scientific research support to staff and trainees in RMD. Her research interests include access to care, quality of care, and treatment outcomes among vulnerable populations including those with disabilities. She holds a bachelor’s degree in Chinese Literature from Soochow University, a master’s degree in Health Services Administration from the University of Arkansas at Little Rock, and a Ph.D. in Health Services Organization and Research from the Virginia Commonwealth University. Her work has been published in peer-reviewed articles and presented at national scientific conferences.
Jonathan Camacho, MD
Dr. Camacho is a medical annotator in the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. He received his MD degree from Universidad Mayor de San Simon in Bolivia and obtained his accreditation as an international medical graduate from the Educational Commission for Foreign Medical Graduates. He currently works on natural language processing research through the annotation of functional terminology in free text electronic medical records using the International Classification of Functioning Disability and Health. Dr. Camacho is also pursuing a Master of Public Health degree with a concentration in Public Health Practice and Policy at the University of Maryland.
Julia Porcino, MS
Julia Porcino works as a computer programmer with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. She joined the section in 2014 after completing her M.S. in Mathematics at the University of Oregon. She also holds a bachelor’s degree in Mathematics from Reed College in Portland, Oregon. Julia currently works on projects in support of the NIH-SSA inter-agency agreement, providing data analysis and statistical support, and helping to develop methods in computer technology such as machine learning, natural language processing, optical character recognition, and optical mark recognition.
Larry Tang, PhD
Larry Tang is an associate professor in the Department of Statistics at George Mason University. He is a statistician specializing in statistical methodology and collaborative research. His current methodological research areas include statistical methods in forensics, diagnostic medicine, group sequential designs and substance abuse research and criminology. He received his Ph.D. in Statistics from Southern Methodist University in 2005. He did postdoctoral training in the Department of Biostatistics at University of Washington. Through the Intergovernmental Personnel Act agreement with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center, he has conducted research on SSA-NIH related topics including accuracy assessment without gold standard and efficiency analysis of decision making units. His work in the former topic has generated peer-reviewed publications with potential applications to the accuracy evaluation of the Work Disability Functional Assessment Battery (WD-FAB) system. His work in the later topic has implemented novel applications of data envelopment analysis to identify best practice within SSA ODAR hearing offices via a user-friendly Python package.
Ao Yuan, PhD
Ao Yuan is an Associate Professor in the Department of Biostatistics, Bioinformatics and Biomathematics at Georgetown University, and a contract researcher with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. Dr. Yuan received his Ph.D. in statistics from the University of British Columbia, Canada. His research interests include the design and analysis of preclinical models and clinical trials; the evaluation of diagnostic tests, biomarkers, and drug combinations; the analysis of high-dimensional genomic data and statistical bioinformatics; adaptive design, subgroup analysis, predictive and prognostic models for precision medicine; longitudinal and Bayesian hierarchical/multilevel modeling.
John Collins, PhD
Dr. Collins is an Assistant Research Professor in the Department of Rehabilitation Science at George Mason University. He is a statistician with experience in experimental design, large data analysis, and mathematical modeling. Dr. Collins earned his doctorate in Mathematics from the University of Oregon in 2009 and his bachelor’s in Mathematics from Reed College in 2003. He then completed post-doctoral training in the Biostatistics and Epidemiology Section of the Rehabilitation Medicine Department at the NIH Clinical Center. Dr. Collins’ research interest centers on properties of measurement instruments in research design, including models to assess diagnostic test accuracy in the absence of known true status and dissemination of statistical best practices with Rehabilitation Science applications. Currently, he works with the Biostatistics and Epidemiology section of the Rehabilitation Medicine Department to develop methods for estimating predictive validity for the WD-FAB instrument as well as study design and power analysis for proposed studies and demonstration projects in support of the NIH-SSA IAA. He also provides statistical support for research conducted in the NIH Clinical Center.
Min Ding, PhD
Min Ding is a computer scientist with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. She previously held a senior research scientist position at Intelligent Automation Inc., Rockville, Maryland from 2013 through 2016 and served as an associate research staff member at NEC Labs North America, Princeton, New Jersey from 2009 to 2013. She graduated from George Washington University with a PhD degree in Computer Science in 2009. She has authored 20 peer-reviewed research publications in reputable conferences proceedings and scientific journals, with a citation of 1700 to date. Dr. Ding is also the inventor or co-inventor of eight US and international patents. Her R&D work at NEC labs won several awards, including the Performance Award in 2011, Spot award in 2012, and NEC Annual Excellent Invention Award 2014. Her research interests cover the areas of large-scale data analysis and modeling, time series data mining and management, natural language processing, machine learning, graph analysis and social media analysis, computer networks and sensor networks.
Josh Chang, PhD
Josh Chang is an applied mathematician (Ph.D UCLA 2012) with background in mathematical and statistical modeling. His research has used a synthesis of analytical and data-driven approaches in investigating phenomena throughout the natural sciences. Before joining NIH in 2015 as a postdoctoral fellow, he was an NSF postdoctoral fellow at the Mathematical Biosciences Institute in Columbus Ohio. His recent interests have included uncertainty quantification in both forward and inverse problems. In support of the SSA he has worked on graphical networks of queueing of tasks, specifically of cases going through adjudication. He is also interested in Bayesian methods of model selection, with recent application to item response theory in support of the functional assessment battery.
Thanh Thieu, PhD
Thanh Thieu is a computer scientist and post-doctoral fellow with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. He has a background in computer science and specializes in machine learning and natural language processing (NLP). He holds a PhD in Computer Science from the University of Missouri-Columbia. His research has focused on compositional and evolutionary learning using a mathematical framework representing abstract objects. His NLP research has resulted in a text mining system that extracts host-pathogen protein-protein interactions from biomedical literature. As part of the Epidemiology and Biostatistics Section, he researches NLP in clinical domains, and works on the extraction of functional terminology from clinical notes. In addition, he also works on evolutionary rule induction, a branch of machine learning that emphasizes interpretability while maintaining accuracy.
Ayah Zirikly, PhD
Ayah Zirikly is a postdoctoral fellow who holds a PhD in computer science with a focus on Natural Language Processing from the George Washington University. Her work focuses on Named Entity Recognition (NER) for low-resource languages, in addition to developing transfer learning techniques for high-low resource settings especially in the area of NER. Additionally, Ayah has developed systems that aim to identify mental health issues in social media (e.g. suicidal posts). At the NIH, she is focusing on identifying entities in free text that would help in the disability determination process at SSA.
Denis Newman-Griffis is a PhD candidate (2014-) in the Speech and Language Technologies (SLaTe) lab at The Ohio State University, in the department of Computer Science and Engineering. Denis also serves as a Pre-Doctoral Fellow with the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. His research includes exploration of how Natural Language Processing (NLP) techniques can be utilized to help model a patient's level of functioning based on their free text clinical records. His research deals primarily with learning semantic representations for words and concepts/entities, and using these as a tool in transferring machine learning knowledge between domains. In particular, his interests include adapting both general-domain NLP and biomedical NLP techniques to the unique challenges of extracting information about patient functioning.
Suzanne Tamang, PhD
Suzanne Tamang is an Instructor at Stanford’s Department of Biomedical Data Science and the Assistant Faculty Director of Data Science at the Stanford Center for Population Health Sciences. Through an Intergovernmental Personnel Agreement, she is also an intramural researcher at the National Institutes of Health, Rehabilitation Medicine Department, and affiliated with the Department of Veterans Affairs, the University of California, San Francisco, Aarhus University, Denmark and the National Bureau of Economic Review. Suzanne received her Ph.D. in Computer Science from the City University of New York and completed her postdoctoral training at Stanford's Center for Biomedical Informatics Research. Suzanne's work involves the application of data mining and machine learning methods to study important population health problems such as suicide, the opioid epidemic, and worker disability as well as the development of tools for health, health risk and quality of care assessment. For example, Suzanne's work with the NBER’S Disability Research Consortium seeks to provide a more comprehensive understanding of the health and economic impact of disability, through the analysis of a large US aluminum manufacturing cohort, tracked over 20 years.
Alex Marr is a graduate of George Mason University with a Bachelor’s of Science in Computational and Data Sciences. As such, he has a foundational understanding and functional practice of mathematical computations and simulations, data network analysis, agent based modeling, data mining, and machine learning. He is versed in industry standard scripting and programming languages such as Java, Python, MATLAB, Octave, FORTRAN, and Bash. He has worked at MITRE Corporation as a junior Data Scientist contributing to various proprietary projects focused on aviation data visualization and predictive analytics. Additionally, he has developed tools and committed changes to an open-source, data provenance modeling framework that is currently hosted on GitHub and available to the public.
Pam Mazerski, MPA
Ms. Mazerski is a contract senior technical consultant in the Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. She holds a Bachelor's Degree in Jurisprudence, and a Master of Public Administration from American University. She served in numerous senior executive positions at the Social Security Administration (SSA) where she directed SSA's $80 million disability and income security research budget, including the administration of contracts, grants, and interagency agreements. Ms. Mazerski has extensive background working across federal agencies, as well as state and local entities. She also led the development and implementation of numerous research demonstration projects aimed at creating employment opportunities for adults with disabilities and youth, including SSA's Youth Transition Demonstration Projects. She also served as a professional staffer on the House Ways and Means Social Security Subcommittee where she worked on the Ticket to Work and other disability and employment related legislation.
Daniel Hobbs, MS
Daniel Hobbs serves as a Management Analyst in Epidemiology and Biostatistics Section of the Rehabilitation Medicine Department at the NIH Clinical Center. He completed a Bachelor of Arts in Government from the University of Virginia, and a Master of Science in Organizational Theory and Leadership from Northeastern University. His previous positions include serving as a legislative and constituent services staff member for fmr. Representative Rick Boucher from Virginia's 9th US Congressional District, as a Senior Executive Assistant to the leadership team of the Biomedical Advanced Research Development Authority (BARDA), and as the Office Manager for the US Department of the Navy's Strategic Systems Program Office. Prior to joining the Epidemiology and Biostatistics section, he served as Assistant to the Chief of the Rehabilitation Medicine Department. His current role is to provide administrative support for all the Section's research projects, with special emphasis on the SSA projects and associated regulatory processes involved. He also provides general administrative support in areas such as budget management, information security compliance, scheduling, travel coordination, editing, meeting planning, and procurement activities.
Margaret Glos, MPH
Margaret Glos serves as a Management Analyst for the Epidemiology and Biostatistics Section and serves as a Contracting Officer’s Representative (COR) on various personnel and research contracts. She completed her undergraduate education at Brown University, and received a Master of Public Health degree from Johns Hopkins University. She previously held research support positions at the Advisory Board Company and the Health Resources and Services Administration (HRSA).
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This page last updated on 04/03/2018