Meet the Team
Dyann Daley, MD
Founder and CEO
Dr. Daley is the founder and CEO of the nonprofit organization, Predict-Align-Prevent, Inc., and is a board-certified pediatric anesthesiologist with 13 years of clinical experience. She found her passion for child maltreatment prevention while caring for severely abused and neglected children in the operating room and designed and published original research in the international journal Child Abuse and Neglect which demonstrated the effectiveness of predicting future child maltreatment locations using spatial risk modeling.
By identifying geospatial clustering of child death types related to child maltreatment and the pathophysiology of toxic stress, she successfully developed collaborative, cross-sector, citywide prevention initiatives to address core risk factors. Utilizing market segmentation, she assisted the largest foster care provider in Texas in the development of psychographically targeted recruitment strategy and a streamlined intake process.
Dr. Daley is an Associate Professor of Anesthesiology and Pediatrics with the University of Arkansas for Medical Sciences, and Arkansas Children's Research Institute in the division of Pediatrics and serves as an editorial board member for Elsevier.
In 2001 she received her Doctor of Medicine from Saba University School of Medicine, completed her residency in Anesthesiology with Baylor College of Medicine in 2005, and a fellowship in Pediatric Anesthesiology with UT Southwestern in 2006. Dr. Daley was the founder and executive director of Cook Children’s Center for Prevention of Child Maltreatment in Fort Worth, TX, from 2014 – 2017.
Danielle VanZorn, PMP, SPHR
Director of Operations
Danielle is an accomplished operations leader, with eighteen years of experience in organizational development, process improvement, project management, and program design. Her achievements include change leadership, educational development, and strategic HR build out for organizations such as AT&T, Medscape, and the U.S. Olympics. She has a Bachelor of Arts in Business Administration and Management and is certified as a Project Management Professional, Senior Professional in Human Resources and Six Sigma Black Belt. Danielle is a strong advocate for improving child welfare and committed to Predict Align Prevent’s mission.
Amanda is a skilled non profit professional with 15 years of experience in the areas of marketing, fundraising, event planning, project management and administrative work. She has supported causes as a volunteer and staff member in the areas of youth development, healthy living, music and arts and supporting individuals with developmental disabilities. As a mother she is an advocate for the safety of children and supporting families.
Dr. Ken Steif
Ken works at the intersection of data science and public policy. He combines technical knowledge of spatial analysis, machine learning and econometrics with an interest in housing policy, education, public health, the economics of neighborhood change, transportation policy and more. Dr. Steif is the Director of the Master of Urban Spatial Analytics at the University of Pennsylvania and teaches multiple courses in the City Planning Department at Penn. He partners with government, as well as the for-profit and non-profit sectors, helping stakeholders convert their data into actionable intelligence. As of late, his work has focused on spatial machine learning across a number of public policy realms including prediction in neighborhood real estate markets.
Grant Drawve, PhD
Grant Drawve is an Assistant Professor in the Department of Sociology and Criminal Justice at the University of Arkansas. Before joining UA, Grant was a Post-Doc at Rutgers University with a dual appointment between the School of Criminal Justice and Department of Psychology. His background focuses on the spatio-temporal patterns of crime and public health related issues. His research interest include: crime analysis, environmental criminology, neighborhoods and crime, secondary data analysis, recidivism, and public health. He has experience working on and managing multiple Project Safe Neighborhood initiatives as well as developing a number of researcher-practitioner partnerships aimed at crime and public safety reduction efforts. He has expertise in applying spatial analytical tools to diverse outcomes to better understand their occurrence for prevention strategies, including spatial crime forecasting. His recent research appears in Justice Quarterly, Journal of Criminal Justice, Deviant Behavior, and European Journal on Criminal Policy and Research. Additionally, Grant has a textbook, Foundations of Crime Analysis: Theory, Data, and Mapping, that was published early 2018.
Shaun Thomas, PhD
Shaun Thomas is an Associate Professor in the Department of Sociology and Criminal Justice at the University of Arkansas. He has a broad background in sociology, criminology, and demography with advanced training in data management and quantitative methods, including multilevel modeling techniques and cutting edge methods of spatial and temporal analysis. His research is rooted in developing our understanding of the epidemiology and etiology of violence, inequality, civic engagement, and other public health issues. Underpinning much of his research is a focus on the dynamic nature of neighborhoods, the manner in which neighborhoods change, the causes associated with these changes, and the rapidity with which these changes transpire. His research examines how both the social and physical environment influence spatial and temporal patterns in diverse outcomes across neighborhood. His recent research has assessed predictors of institutional isolation among youth, truancy, homicide, gun violence, drug markets, and coronary heart disease. Much of this research highlights practical implications for addressing critical social problems including the institutional isolation of youth and the importance of assessing both the physical and social determinants of the geographic distribution of violence and coronary heart disease. His recent research has appeared in a variety of outlets including Sociological Spectrum, Journal of Interpersonal Violence, Homicide Studies, Crime and Delinquency, Deviant Behavior, and Journal of Criminal Justice.
Jyotishka Datta, PhD
Jyotishka Datta is an assistant professor in the Department of Mathematical Sciences at the University of Arkansas. Jyotishka received his PhD in Statistics from Purdue University in 2014 and worked as an NSF postdoctoral fellow at Duke University and SAMSI (Statistics and Applied Mathematical Sciences Institute) from 2014-16 as a part of the 'Beyond Bioinformatics' program. His research interest spans developing Bayesian methodology and machine learning tools for high-dimensional or infinite-dimensional objects with structure such as sparsity, spatio-temporal dependence or mixed membership. He also conducted research in the area of applied statistics for modeling social outcomes with a particular emphasis on consulting with law enforcement agencies to develop spatial temporal risk assessments. He has contributed to the area of large-scale multiple testing, sparse signal recovery, computational aspects of big data analysis and time-series modeling with applications spanning cancer genetics and genomics, neuroscience and modeling global terrorism activities. In particular, his most recent research develops methods for predictive inference for quasi-sparse count data such as detecting hotspots for rare events and structural change-points in dynamic spatio-temporal data.
Maria Kaltcheva, PhD
Maria is a researcher in the Biomedical Sciences. She has a B.S. in Biochemistry from the University of Wisconsin – Madison and a Ph.D. in Biology from the Johns Hopkins University graduate partnership program with the National Institutes of Health. While completing her doctoral training at the National Cancer Institute, she focused on elucidating molecular pathway interactions during embryonic development. Maria is excited to apply her research training to help improve communities by combatting and preventing child maltreatment.