Faculty

Eunhye Ahn’s research focuses on leveraging data to improve the outcomes of children and families and advance broader social goals of equity and justice. She is particularly interested in informing child welfare policy and practice by utilizing data science and promoting equitable child welfare outcomes through rigorous examination of racial and socioeconomic disparities.

Dennis Barbour’s research interests include auditory processing, cognitive neuroscience, machine learning and medical informatics. Most recently he has developed new machine learning methods for rapidly and thoroughly evaluating perception and cognition. These new tests are not only useful for exploring normal nervous system function, but also for diagnosing disorders. The principles contributing to these successful diagnostics may also lead to effective neurotherapeutics, alone or when coupled with drug or device therapies.

Dr. Barch’s current research is focused on understanding the interplay among cognition, emotion, and brain function to better understand the deficits in behavior and cognition found in illnesses such as schizophrenia and depression. She is involved with a number of “population neuroscience” projects such as the Adolescent Brain and Cognitive Development study and the Human Connectome Projects that afford the opportunity to develop and apply novel data analytic approaches to understand brain-behavior relationships both within densely-sampled individuals and across large populations.

Janine Bijsterbosch’s multidisciplinary background combines advanced computational MRI analysis tools with experience in clinical applications such as depression and anxiety. She heads the Personomics Lab, which aims to understand how brain connectivity patterns differ from one person to the next, by studying the “personalized connectome.” Using population datasets such as the UK Biobank, the lab adopts cutting-edge analysis techniques to study functional connectivity networks and their role in behavior, performance, mental state, disease risk, treatment response and physiology.

Bogdan’s research examines how genomic variation and environmental experience contribute to individual differences in neural phenotypes, behavior, and psychopathology.

Dr. Braver’s research focuses on the neural, computational, and psychological mechanisms that give rise to cognitive control (regulation of thought and behavior based on actively maintained cognitive goals). Current projects examine individual differences in cognitive control function and interactions with affective and motivational processes, through acquisition of large-scale neuroimaging datasets (which include longitudinal and twin-based assessment). These datasets provide unique opportunities to identify trait and state markers of cognitive control via novel data analytic approaches, including neural pattern classification and activation-based dynamical signatures.

Michael Brent’s research lab is developing and applying mathematical and computational methods for mapping gene regulation networks, modeling them quantitatively, and synthesizing new network designs in living cells. This work includes basic research aimed at understanding how cells process and respond to information as well as applications to the study of infectious disease and the study of human genetics. See the lab webpage, https://mblab.wustl.edu for more information on their research.

Derek Brown’s research focuses on costs, access to care, quality, and disparities among Medicaid populations—including physician payment, housing instability, and child maltreatment. He also uses discrete choice experiments to analyze preferences for health care and outcomes, such as screening and vaccination.

Butler-Barnes received her PhD and MA from Wayne State University in psychology and a BS in psychology from Michigan State University. The two lines of her research agenda include 1) Strengths-Based Assets of Black Adolescents Project which explores how Black youth draw on personal and cultural assets and resources to thrive despite challenges to their identities from institutional and personal experiences of racism; and, 2) Celebrating Strengths of Black Girls Project focuses on advancing equitable learning environments for Black women and girls.

David Carter’s research is in the field of international relations, with a focus on interstate conflict substate political violence.

Yixin Chen’s research interests are in the general areas of nonlinear optimization, artificial intelligence, data warehousing and data mining. He is particularly interested in large-scale constrained nonlinear optimization in discrete, continuous and mixed-integer spaces.

Dino Pinterpe Christenson studies American political behavior and quantitative methods. His recent work explores presidential voting behavior, campaign dynamics in primary elections, the coalition strategies of interest groups, and public opinion and the media environment of institutional outcomes

Our group studies how information encoded in the human genome specifies when, where, and to what levels genes are expressed. We seek to understand the features of DNA sequences that control how genes are expressed. To this end, we are borrowing techniques from artificial intelligence to model how strings of short DNA sequences interact to form larger functional units that control gene expression.

Dr. Ding’s research focuses on integrating cancer proteogenomics (including single cell and spatial omics), patient-derived cancer models, functional genomics, and drug  development to advance cancer biology and precision medicine. This work spans many cancer types, including acute myeloid leukemia, breast cancer, kidney cancer, pancreatic cancer, prostate cancer, glioblastoma, and multiple myeloma, with key findings including the identification and characterization of pathogenic germline variants and somatic cancer drivers, and discovery of age-related clonal hematopoiesis. Her research team has also developed widely-used computational tools, including VarScan, SciClone, BreakDancer, MSIsensor, Pindel-C, HotSpot3D, and MuSiC and she plays significant roles and co-chairs various groups within The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the Patient-Derived Xenografts Development and Trial Centers Research Network (PDXNet), Patient Engagement and Cancer Genome Sequencing (PE-CGS), Cellular Senescence Network (SenNet), and Human Tumor Atlas Network (HTAN). She also serves on the Steering Committees of CPTAC, TCGA, PDXnet, PE-CGS, SenNet, and co-chairs the HTAN Steering Committee. Dr. Ding has been recognized several times among Thomson-Reuters’ surveys of influential researchers, including listing among “The World’s Most Influential Scientific Minds”, and was a member of a group honored with the American Association of Cancer Research Team Science Award for contributions to TCGA. Her work continues to be inspired by the ultimate goal of making personalized medicine a reality for cancer patients.

Brett Drake researches matters of child welfare with a focus on early intervention cases of child neglect as well as the connections between socio-environmental conditions and child neglect. His current research analyzes census and child protective data to assess the efficacy of protective and preventative services.

I am an Assistant Professor in Political Science at Washington University in St. Louis. I hold a PhD in Politics (Political Economy program) from Princeton University, where I was affiliated to the Program for Quantitative and Analytical Political Science (Q-APS), the Research Program in Political Economy, and the Fellowship of Woodrow Wilson Scholars. My fields of specialization are Political Economy and Political Methodology. 

Tammy English’s research focuses on better understanding the role of cognitive, motivational, and social factors in supporting well-being across adulthood into old age. Her work leverages experience sampling and passive sensing with smartphones to assess the experience and management of emotions in a variety of daily life contexts.

Patrick Fowler’s research aims to inform developmentally sensitive public and programmatic policy that supports socioeconomically marginalized families. This work applies rigorous methodology to understand the mechanisms through which housing stabilization promotes healthy child development.

Professor Garnett’s main research interest is developing new Bayesian machine-learning techniques for sequential decision making under uncertainty.

Aimilia Gastounioti’s research combines elements of image analysis, machine learning/artificial intelligence, and informatics to build technologies with a potential for clinical impact in advancing breast cancer screening and prevention. She heads the Breast Image Computing Lab, that brings together a multidisciplinary team of scientists and physicians with the aim to advance computational breast imaging research.

Professor Gill’s research focuses on assuring properties of distributed real-time and embedded systems in which software complexity, unpredictable environments, and heterogeneous platforms demand novel solutions that are grounded in sound theory.

Dr. Gordon’s research focuses on understanding healthy aging and neurodegenerative diseases such as Alzheimer Disease. His primary research questions are 1) what can neuroimaging (MRI and PET) and biofluid (CSF and blood) data tell us about biological changes occurring in the brain? 2) How do these changes relate to cognition and the risk for developing dementia? 3) What genetic, environmental, and lifestyle factors influence the aging brain? The high dimensionality of imaging data, the richness of multimodal measures, and the longitudinal nature of clinical cohort studies present challenging analytical questions that can be best answered using the novel analytical approaches which are at the heart of the Computational & Data Sciences PhD program.

Roch is widely recognized for his contributions to understanding the fundamentals of data network design and how networks can be designed to provide desired quality of service guarantees.

Shenyang Guo is the author of numerous research articles in child welfare, child mental health services, welfare, and health care. He has expertise in applying advanced statistical models to solving social welfare problems and has taught graduate courses that address event history analysis, hierarchical linear modeling, growth curve modeling, propensity score analysis, and program evaluation. 

Hammond’s research applies complex systems tools to generate new insights into the social dynamics that drive many difficult policy problems, as well as to identify potential leverage points or windows for intervention. 

Jenine Harris focuses on closing the gap between discovery and delivery in her methodological and substantive areas of research, and in her teaching and service. Her methodological area of expertise is social network analysis, with a particular interest in statistical modeling of social networks, which she has used to identify gaps in path between discovery and delivery in scientific research and public health practice.

Professor Ho’s research centers on the design and analysis of human-in-the-loop systems, with a focus on eliciting and aggregating human-generated data.

Jiaxin Huang’s research broadly lies in the intersection of natural language processing and machine learning. She is especially interested in understanding the properties of language models and improving their training and inference, especially in a low-resource setting. Her current research focuses on improving the trustworthiness of language models with retrieval augmentation and uncertainty estimation, and developing methods to enhance the generic reasoning ability of language models.

Josh Jackson’s research focuses on the assessment of individual differences in an effort to better understand the link between individual differences and life outcomes (e.g., premature mortality, job market success). His research routinely combines existing large scale longitudinal datasets with additional multimodal data sources ranging from broad country/county/census tract data to more “boutique” datasets such as intensive time series data and biomarker assays. Frequently used quantitative methods include: Bayesian multi-level models, IRT, SEM, and network models.

Nathan Jacob’s research centers on developing learning-based algorithms and systems for extracting information from large-scale image collections. He has applied this expertise in many application domains, with a particular focus on geospatial and medical applications. He is a member of the Institute for Biomedical Informatics and owner of Multidomain Vision Research LLC, a research and software development firm that specializes in novel algorithms in computer vision, medical imaging and remote sensing.

Dr. Johnson’s research is primarily focused on disparities in access to care among pediatric and young adults diagnosed with cancer. She has published extensively using large administrative data including surveillance data from the Surveillance, Epidemiology and End Results program. She has also recently become engaged in activities that aims to improve the reproducibility of scientific results. In addition to her research activities, she leads the Brown School epidemiology and biostatistics specialization and teaches two epidemiology courses in the Brown School Masters of Public Health (MPH) program: Foundations in Public Health: Epidemiology and Advanced Data Analysis.

Dr. Jonson-Reid’s primary interest is understanding how responses to childhood abuse and neglect can be improved to support positive behavioral, educational and health outcomes. To look for targets of opportunity for services and policy change, much of her research involves the construction and analysis of integrated multi-agency, longitudinal administrative data. Her work has also included linking administrative data sources with census, policy and primary data collection elements.

Brendan Juba’s work seeks to determine when and whether various integrated learning and inference tasks can be solved by efficient algorithms. Frequently, such an integrated task can be solved well in various respects even though the individual learning or inference tasks may be ill-posed or intractable. Beyond classical deductive inference tasks, this includes richer tasks such as context-specific inferences, and tasks to formulate plausible hypotheses, or conditions and/or subpopulations under which structures of interest appear in the given data. For example, this  includes problems of identifying conditions where data is fit well by a model from a given, simple family. He has also worked on a theory of communication without common standards.

Thomas Kannampallil’s research interests are at the intersection of computer science, cognitive science, and clinical informatics. Specifically, his research focuses on developing and evaluating computational tools using artificial intelligence tools for improving clinical decision making, physician well-being, and patient safety. His research is currently funded by National Institute of Aging, National Library of Medicine and Agency for Healthcare Research and Quality.

Gregory Kehne’s research centers on algorithm design and computational social choice, as well as problems at the intersection of computer science and economics more broadly. His work is particularly focused on voting rules and other methods for preference and judgement aggregation, leveraging theoretical and quantitative tools to both better understand how methods perform in practice and design new methods with provable guarantees.

Caitlin Kelleher is a researcher in Human-Computer Interaction and focuses on designing new kinds of programming environments and languages that democratize programming.

Maura Kepper conducts research to reduce the burden of the social, economic, and environmental conditions that can negatively impact the health of individuals and communities. Her research intersects behavioral health, informatics, and dissemination and implementation research.

Kool’s research explores the interaction between cognitive control, decision making, and reinforcement learning. In the Control and Decision-Making Lab, his team approaches these issues by combining insights from behavioral experiments, computational modeling, and neuroimaging. Recent research from the lab examines how the brain implements effort-based decision making, how the mind arbitrates between multiple choice strategies, and how people learn how to adapt such arbitration processes in dynamic learning contexts.

Kreuter is a leading national public health expert in the field of health communications. He joined the Brown School in 2008 and advances the school’s Master of Public Health degree as associate dean for public health.

Shinjini Kundu, MD, PhD is a neuroradiologist and Assistant Professor of Radiology, with affiliate faculty appointments in Electrical Engineering and Biomedical Engineering at Washington University in St. Louis. Her lab develops new artificial intelligence approaches to revolutionize medical imaging. Her work has been published in journals such as Nature Medicine and Science Advances, and she was recognized on the MIT Technology Review’s list of 35 innovators under 35. She holds a PhD in AI for medical imaging from Carnegie Mellon University and MD from University of Pittsburgh. She then completed her residency and fellowship in radiology at Johns Hopkins Hospital. Earlier, she obtained her BS and MS in electrical engineering from Stanford University. Her work stands to accelerate new precision medical diagnostic technologies capable of detecting diseases earlier, with greater accuracy and suggestions for tailored treatments.

Dr. Lai is the Deputy Director, Institute for Informatics (I²) and Chief Research Information Officer for the School of Medicine. He is also an Associate Professor of General Medical Sciences at the Washington University School of Medicine in St. Louis. He specializes in the development of research informatics infrastructure and is well recognized in the fields of clinical research informatics and consumer health informatics. His recent research has focused on the application of natural language processing, temporal reasoning and information fusion to generate a longitudinal computable phenotype to support clinical trial prescreening.

Seanna Leath’s research focuses on variation in the family and school-based experiences of Black women and girls, with a particular focus on identity development and wellbeing. She uses healing justice frameworks and community-based methodologies to examine topics related to intersectionality, discrimination and stigma, and sociocultural practices within Black families.

My lab works on nanoscale computational imaging, in which joint design and optimization of a hardware imaging system and software algorithms are essential for unlocking new capabilities for studying biology, chemistry, and physics at the nanoscale. My group, among other things, develops new computational tools for image reconstruction, as well as the reliability and statistical confidence of these reconstructions. Our methodologies fuse physics-based models and data-driven models to accomplish these tasks.

Prof. Jr-Shin Li works at the intersection of dynamical systems, control, and data science with broad applications ranging from biology and quantum physics to public health. His research is focused on control analysis, computation, optimization, and learning of high-dimensional complex systems. Current research projects at Prof. Li’s lab include the exploration and development of theory- and data-driven methods for controlling and analyzing population systems and complex networks, computational algebra and geometry approaches to learning dynamic structures and features from high-dimensional data, and topological data science and engineering techniques for data mining, reduction, and manifold learning.

Fangqiong Ling leads a computational and experimental lab to study bacterial colonization and transmission at the boundary of built and natural environments. She explores microbiomes as environmental sensors and public health sentinels.

Chenyang Lu’s research interests include real-time embedded systems, wireless sensor networks, Internet of Things and cyber-physical systems.

Christopher Lucas develops and applies models for the analysis of massive quantities of text, imagery, audio, and video, in order to uncover how and what people learn about politics. Substantively, he is broadly interested in large-scale summaries of political information online, and is currently studying how online newspapers report on crime and corruption, and, separately, how candidates for elected office communicate with voters through advertisements.

Douglas Luke is a leading researcher in the areas of public health policy, systems science and tobacco control. Luke directs work focused primarily on the evaluation, dissemination and implementation of evidence-based public health policies.

José Marcio Luna is an assistant professor of radiology for Mallinckrodt Institute of Radiology (MIR) at Washington University School of Medicine in St. Louis. Luna’s research focuses on the development of translational tools for prediction and diagnosis of cancer from a multi-omics perspective in the era of precision medicine.

Phillip Marotta’s research focuses on the impact of the criminal justice system on disparities in public health, with an emphasis on substance use treatment interventions in justice-involved populations and the HIV care continuum for justice-involved persons with substance use disorders. Marotta examines medication-assisted treatments for opioid use disorder (OUD) in jails and prisons. A current project evaluates the effects of methadone maintenance during custody on recidivism and overdose among recently discharged individuals.