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Biostatistics, Informatics, and Health Data Experts

Hakan Demirtas Heading link

Expert on: missing data, multiple imputation, stochastic simulation, random number generation, Bayesian computing, number theory.

Hakan’s work spans several major areas of statistics and is driven by an interest in developing statistically sound solutions to real-world problems. His methodological research interests can be categorized into the two broad areas: analysis of incomplete multivariate data and stochastic simulation. His concentration has been on biostatistics in recent years, however, the fundamental principles of these research topics can be applied to a wide range of disciplines including finance, econometrics, mathematics, engineering, quantitative social and behavioral sciences, among many others. Having engineering and business administration degrees, followed by a doctoral level training in mathematical statistics, and currently working in a public-health oriented environment, give him the tools and perspectives for striking a delicate balance between technique and judgment to make statistical, computational, and methodological contributions to a wide range of substantive areas.


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Li Liu Heading link

Expert on: Statistics methodologies in behavioral and cancer research, Longitudinal design, multilevel/clustered data, survival data, mediation analyses.

Li has extensive experience in the design and analysis of various types of data in longitudinal or multi-level studies. Her previous work included the design, execution, and analysis for multiple large-scale longitudinal efficacy trials, including prevention studies for adolescent behavior, efficacy trials for HIV prevention in global health, and prevention trials for preterm birth and early childhood health. In recently years, she is also supporting cancer research. The projects she served as a senior statistician included cancer biology and genomics data, cancer outcomes, Phase I and II clinical trials in cancer treatment, and cancer disparities.


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Michael Petros Heading link

Expert on:  clinical laboratory testing, screening policy, surveillance, genetics, data analysis

Michael Petros served 38 years at the City of Chicago and State of Illinois public health laboratories, first as a senior microbiologist and later, as chief of operations for the newborn /metabolic diseases screening section.  His research interests include developing decision-making strategies for genetic screening policy and his teaching interests include population genetics, public health policy, disease screening, data analysis and the visual display of data.

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Apostolis Sambanis Heading link

Expert on:  disaster management, environmental risk management.

Apostolis Sambanis’ current research interests involve risk visualization and decision support content for managing disasters or measuring resilience. Previously research includes evaluation of a geospatial health risk computer program funded by the USEPA known as Spatial Analysis and Decision Assistance (SADA), implementing the CDC BRACE climate change adaptation framework for the state of Illinois, and developing a Private Sector Integration Plan for the creation of logistical inventory software to be used during a disaster response event.

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Jiehuan Sun Heading link

Expert on: risk prediction, phenotyping.

Jiehuan Sun’s primary research interests are to develop statistical methods to deal with big longitudinal medical data including electronic health record (EHR) data and genomics data. His previous studies include building predictive models to calibrate the risk of congestive heart failure among type II diabetes patients using EHR data, developing high-throughput phenotyping algorithms to identify patients truly having certain disease based on medical history data such as ICD codes and clinical notes, and identifying disease-related genetic traits using both genetics data and EHR data.

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Rrita Zejnullahi Heading link

Expert on: Meta-analysis and causal inference

Rrita’s research interests are in the use and development of statistical methods and tools to support policymaking. This includes the quantification of uncertainty of point estimates from predictive models, the formulation of effect sizes and effect size estimators in randomized and quasi-experiments when adjusting for covariates, and extensions of meta-analysis methods to small sample situations. She is also interested in generating empirical evidence about how policymakers and practitioners make decisions currently and designing tools that better support their decision-making processes.

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