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.
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.
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.
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 began her career in clinical research studying mood and anxiety disorders; since then, she has held policy, research, and research administration posts in both the private sector and federal government. She completed her undergraduate education at Brown University and holds a Master of Public Health from the Johns Hopkins University Bloomberg School of Public Health.
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This page last updated on 06/25/2019