Today is Friday, Sep. 22, 2017

Department of

Biomedical Informatics

Faculty Research Highlights

Primary Faculty

Bruce AronowBruce Aronow, PhD

Professor, Biomedical Informatics.  Dr. Aronow is a computational geneticist and developmental biologist. His group carries out analyses of many different kinds of data, develops algorithms, and builds websites and databases. These tools allow researchers from varying disciplines and backgrounds to analyze genetic and genomic data—either their own or that gathered from published sources—to better understand, model, and carry out new research about normal development and disease. His group is highly collaborative with clinical and basic researchers across a broad range of research projects encompassing many areas of biology and disease, including normal and abnormal development, in vivo and in vitro disease models, and large-scale clinical studies. Data of interest includes genetic, genomic, proteomic, metabolomic, imaging, and therapeutic agent response measures. Recent areas of interest include large-scale clinical sample analyses, single cell-based dissection of developmental and disease tissues, and in vitro stem cell-based modeling of normal and disease-affected tissues including abnormal neurological, immunological, cardiac, and cancer tissues. The lab’s recent efforts focus on predicting new therapeutic approaches based on disease mechanisms in the areas of inflammatory bowel disease, eosinophilic esophagitis, sickle cell anemia, cardiac development, and neurological and psychiatric diseases. His group is working on efforts to define the transcriptome of the developing kidney, lung and brain. They are using stem cell-derived cells and organoids to dissect mechanisms that underlie organ development and function as well as oncogenesis. They are also working to infer novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships.
Google Scholar  |  ResearchGate  |  Lab Website

Brett HarnettBrett Harnett, MS-IS

Assistant Professor, Biomedical Informatics. Professor Harnett serves as the Director for the UC Center for Health Informatics (CHI) within the Department of Biomedical Informatics. The CHI is the institutional Honest Broker that provides clinical data for research. Other service lines include developing enterprise-class tools for organizing, displaying and visualizing data using analytics. His teaching and research includes medical informatics, patient-centered applications and telehealth. Brett also serves on the UC Institutional Review Board (IRB).
PubMed  |  ResearchGate

Anil JeggaAnil Goud Jegga, DVM, MRes

Associate Professor, Biomedical Informatics.  Anil Jegga, DVM, MRes, is a biological and medically-oriented computational biologist. The mission of the Jegga Lab is to design, develop and apply novel and robust computational approaches that will accelerate the diffusion of genomics into biomedical research and education and convert the genomics data deluge into systematized knowledge to help us understand the molecular basis of disease. The lab continues with their focus on integration and mining of multiple sources of genomic, genetic and biomedical data to derive models for pathways and processes underlying development, disease and drug response. Independently and collaboratively, they have previously developed and published tools that allow biologists with minimal computational experience to integrate diverse data types and synthesize hypotheses about gene and pathway function in human and mouse. These tools are designed to answer several straightforward questions that biologists frequently encounter while trying to apply systems-level analyses to specific biological problems. His team is currently focusing on developing and implementing systems biology-based novel computational approaches to identify drug candidates for rare lung disorders. They are also working to integrate and mine genomic and compound screening-based big data to identify drug repositioning and novel drug candidates.
Google Scholar  |  ResearchGate  |   Lab Website

Michal KourilMichal Kouril, PhD

Assistant Professor, Biomedical Informatics.  Dr. Kouril collaborates with several Cincinnati Children's divisions on a number of innovative technology-related projects. One notable collaboration is the five-year R01 grant with the Division of Behavioral Medicine and Clinical Psychology (Jennie Noll, PI). The project is monitoring online behavior of abused and non-abused adolescents to look for inappropriate and risky behavior. In addition, Dr. Kouril oversees the Cincinnati Children's Research IT group, which maintains petabyte-size storage in a number of performance tiers including the fastest SSD-based systems used for the most demanding applications, such as research data warehousing, virtual desktop infrastructure and some production servers. His team built out the research disaster recovery infrastructure to accommodate applications that are required from the business continuity perspective. In addition, they have expanded the computational cluster and added cutting-edge technology such as large graphics processing unit capability and high-core density teraFLOPS-speed Intel Phi cards.
Google Scholar  |  ResearchGate

Long Jason LuLong (Jason) Lu, PhD

Associate Professor, Biomedical Informatics.  Long (Jason) Lu, PhD, focuses on bringing quantitative approaches from disciplines such as computer science and applied mathematics to study the molecular mechanisms of human diseases. His expertise includes biomolecular network predictions and analysis, machine learning and statistical inference, genomic and transcriptomic sequence analysis, and medical image analysis. Dr. Lu developed a network-based approach that combines proteomics experiments and computational predictions to discover high-density lipoprotein (HDL) subspecies and correlate them with cardiovascular protection function. His approach identified 38 candidate HDL subparticles. Further biochemical characterization of these putative subspecies may facilitate the mechanistic research of cardiovascular disease and guide targeted therapeutics aimed at its mitigation. In studying pediatric brain disorders, Dr. Lu developed a set of novel algorithms for analyzing brain anatomical and functional MRI images. These algorithms will be important tools in aiding physicians in diagnosis and developing treatment plans. Dr. Lu has also introduced a new perspective to characterize gene essentiality from protein domains, which addresses the limitations of traditional gene-level studies of essentiality. To identify such essential domains, he developed an Essential Domain Prediction (EDP) Model and presented the first systematic analysis on gene essentiality on the level of domains. Dr. Lu’s research accomplishments have been recognized nationally and internationally by serving on grant reviewer panels for the National Institutes of Health and the National Science Foundation in the United States, the Natural Sciences and Engineering Research Council of Canada (NSERC), French National Research Agency (ANR), and National Science Centre of Poland (NCN).
Publications List  |  Lab Website

Jun MaJun Ma, PhD

Professor, Biomedical Informatics.  Research performed by Dr. Ma’s team focuses on understanding developmental processes at a quantitative and systems level. They aim to establish quantitative models—with predictive power—of how embryonic patterns emerge in a manner that is proportional to embryo size.  They perform experimental studies to facilitate model building, and use models to make predictions for experimental validations. They use Drosophila (fruit fly) embryos for their studies.  The research by Dr. Ma’s team was supported by grants from the National Institutes of Health and the National Science Foundation.
PubMed

Keith MarsoloKeith Marsolo, PhD

Associate Professor, Biomedical Informatics.  Dr. Marsolo’s research interests include methods to characterize the quality and suitability of electronic health record (EHR) data; approaches to collect and extract research data from the EHR at scale; the design and instantiation of common data models to facilitate distributed research queries; and the development of informatics architectures and standards that can support multi-center learning health systems. Dr. Marsolo serves as faculty advisor for BMI Data Services, which provides services in these areas. He is currently building on several grants from the Agency for Healthcare Research and Quality to design and implement an EHR-linked registry architecture for ImproveCareNow, a 94-center quality improvement and research network that focuses on improving the care and outcomes of children with inflammatory bowel disease (IBD). He and his team are extending the platform to support a pragmatic clinical trial that is being funded by the Patient-Centered Outcomes Research Institute (PCORI).  This pragmatic trial will serve as an initial use case for a recently funded grant from the Office of National Coordinator for Health Information Technology (ONC) to pilot the use of interoperability standards and embed case report forms in the EHR, decreasing the amount of time spend on double data entry during research study visits.  Other recent highlights include work on Phase I and II of PCORI’s National Patient-Centered Clinical Research Network (PCORnet), including a pediatric-focused Clinical Data Research Network (CDRN), and a Patient-Powered Research Network (PPRN) with ImproveCareNow. In addition, Dr. Marsolo is a co-investigator within the Distributed Research Network Operations Center of the PCORnet Coordinating Center, served as one of the co-chairs of the PCORnet’s Data Standards, Security and Network Infrastructure (DSSNI) Task Force during Phase I of the project, and is a member of the Data Committee as part of Phase II.
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Yizhao NiYizhao Ni, PhD

Research Instructor, Biomedical Informatics.  Yizhao Ni’s research interest lies in the development of machine learning, natural language processing (NLP) and information retrieval techniques to assist clinical decision making. His research is application-oriented and the overall objective is to improve the quality of health care by: providing more effective provisioning of usable data (efficiency); helping clinicians generate more objective clinical decisions (effectiveness); and providing more reliable proactive prediction of clinical outcomes (safety). To achieve these objectives, he collaborates with clinical providers, information service administrators and biomedical and computational scientists. Dr. Ni’s team has participated in a variety of research projects, including the Electronic Medical Records and Genomics Network (eMERGE) project, and , medication safety in intensive care units (R01), detection of surgery cancellation (R21), and sustainable surveillance of diabetes (U18). He has active collaborations with the divisions of Emergency Medicine, Hospital Medicine, Center for Autoimmune Genomics and Etiology, Neonatology & Pulmonary Biology, Anesthesia, Psychiatry and Oncology at Cincinnati Children’s; and with Neurology and Rehabilitation Medicine in the UC College of Medicine. In addition to his research, Dr. Ni is serving as a machine learning specialist in multiple quality improvement projects such as the safety and situation awareness project.
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John PestianJohn Pestian, PhD, MBA

Professor, Biomedical Informatics.  Dr. Pestian's lab focuses on developing advanced technology for the care of neuropsychiatric illness. Using artificial intelligence, his team integrates analyses of trait and state characteristics for early identification of both neurological and psychiatric illness. His lab developed and implemented an automated, electronic health record surveillance system that processes clinician notes to identify epilepsy surgery candidates up to two years earlier than traditional approaches. The lab also focuses on earlier identification of individuals at risk of suicide, depression, and bipolar and anxiety disorders using verbal and non-verbal language.  Current projects include fusion of linguistic, acoustic, and visual cues that are being tested in selected Cincinnati Public Schools and Cincinnati Children’s clinics. Dr. Pestian and his lab have 18 issued patents and he is active in the entrepreneurial community. This activity has yielded over 500 jobs and one-half billion in revenue have been created.  One invention, Processing Text With Domain-Specific Spreading Activation Methods, is a platform for neuropsychiatric research.  Another, Optimization and Individualization of Medication Selection and Dosing, has been used for optimal mental health drug selection on more than 420,000 people. He and his colleagues have published more than 80 peer reviewed publications that focus on applied and translational sciences apropos to artificial intelligence. He currently mentors five junior faculty, of which three have recently received funding from the National Institutes of Health (NIH). Dr. Pestian is an alumni of the NIH’s standing Study Section, Biomedical Library and Informatics Review Committee (BLIRC) of the National Library of Medicine,as well as the National Institute for Mental Health’s, Pathway to Independence (K99) study section.
Google Scholar  |  PubMed  |  Lab Website

Nathan SalomonisNathan Salomonis, PhD

Assistant Professor, Biomedical Informatics.  Dr. Salomonis and his group are on the cutting edge of developing new software and algorithms to identify complex functional relationships from whole transcriptome data. They have developed several open source analysis tools including AltAnalyze, LineageProfiler, GO-Elite, and NetPerspective. The advent of single-cell genomic profiles has created many new opportunities for understanding stochastic decisions mediating stem cell differentiation to distinct cell fates and the regulation of distinct gene expression and splicing programs. They are capitalizing on this new technology to explore these decision-making processes at a resolution never previously possible. Last year, they worked collaboratively with a dozen investigative research teams within Cincinnati Children's to develop new methods for evaluating whole genome transcriptome datasets. These methods include: 1) the detection of distinct gene and splicing populations from bulk and single cell genome profiles, 2) predicting implicated cell types present in complex fetal maternal biological samples and 3) identifying new disease regulatory networks related to pediatric and adult cancers, cardiovascular disease and spinal cord injury.
Google Scholar  |  Research Gate

Andy SpoonerS. Andrew Spooner, MD, MS, FAAP

Professor, Biomedical Informatics and Chief Medical Information Officer. Dr. Spooner practices general academic pediatrics and serves as the Chief Medical Information Officer for Cincinnati Children’s. He is also actively involved in patient-centered research. He and his research group have created a data warehouse focusing on medication alerts stretching back five years, into which they have built several metrics of user alert-response behavior. They are using this warehouse to answer questions about how clinical users manage the load of decision-support alerts in the system and how they detect potential harmful overdose errors. They are collaborating with an external machine-learning vendor that is working with the hospital’s safety leaders on safety analytics to bring more powerful tools to bear on the problem of alert fatigue and user overload. On other fronts, Dr. Spooner is researching decision support for weight data-entry errors that can have profound effects on medication safety. His group is working with business intelligence systems interfaced to the electronic medical record to tune decision support to unprecedented specificity and sensitivity. Google Scholar

Michael WagnerMichael Wagner, PhD

Associate Professor, Biomedical Informatics.  Dr. Wagner has a long-standing interest in applications of machine learning techniques to bioinformatics problems such as protein structure prediction, disease classification and protein identification. He is also involved in a number of projects that implement complex software and data infrastructure. For the National Heart Lung and Blood Institute-funded Pediatric Cardiology Genomics Consortium, part of the Bench to Bassinet project, he plays a leadership role in the development and maintenance of the Data Hub (a.k.a. HeartsMart), which now houses tens of thousands of whole exome and thousands of whole genome sequencing data sets. He is co-principal investigator on the Longitudinal Pediatric Data Resource (LPDR) project funded through the Newborn Screening Translational Research Network and National Institute of Child Health and Human Development. The LPDR is being used by researchers nationwide to mine health outcome data over the lifespan of children who screen positive for rare and often devastating genetic disorders. Dr. Wagner also leads the Rheumatology Disease Research Informatics Core of the Cincinnati Rheumatic Diseases Core Center, which is funded by the National Institute of Arthritis and Musculoskeletal and Skin Diseases. Google ScholarMyNCBI

Peter WhitePeter S. White, PhD

Professor, Biomedical Informatics.  Peter White, PhD, is the Riveschl Professor and Chair of the Department of Biomedical Informatics at the University of Cincinnati College of Medicine, and Division Director of Biomedical Informatics at Cincinnati Children's. In these roles, he oversees informatics research and resources at both institutions, including academic, educational, data services, technology development, and Research IT missions. As co-director of the Center for Pediatric Genomics, he also serves in a leadership capacity for establishing enterprise-level solutions to genome-based precision medicine at Cincinnati Children’s. In his research career, Dr. White has explored the development and application of novel approaches for disease gene discovery, including identifying causative genes for neuroblastoma, ADHD, autism, and congenital heart defects. He has also developed innovative approaches for integrating and disseminating clinical, phenotypic, and molecular data to researchers for promoting discovery and hypothesis validation. Dr. White has recently played a lead informatics role on a number of national network research programs, including the NCATS Clinical and Translational Science Award for UC and Cincinnati Children’s, the NICHD Newborn Screening Translational Research Network, the NHLBI Bench to Bassinet Program, the NHGRI Clinical Sequencing and Exploratory Research and IGNITE Consortia, and the Genomic Research and Innovation Network.
ResearchGate

wu-dannyDanny T. Y. Wu, PhD

 Assistant Professor, Biomedical Informatics Danny T.Y. Wu recently joined the University of    Cincinnati Department of Biomedical Informatics (BMI) as an Assistant Professor. His research  draws on human-computer interaction, data mining, information retrieval, and natural language processing to maximize the value of clinical data stored in electronic health records to improve care quality and support clinical and translational research. Wu received both his PhD and master’s degree from the University of Michigan School of Information prior to joining UC. Before going to graduate school, he worked as a software engineer for four years. In addition to research, Wu is dedicated to education, service, and practical engagement. He was appointed as a Lecturer in the Department of Health Management and Policy at the University of Michigan in Fall 2015, teaching a graduate-level course on database systems and Internet applications. He was a senior analyst leading a programming team to develop, implement, and innovate dynamic data capture systems at the University of Michigan Congenital Heart Center. Wu served on the student editorial board of the Journal of American Medical Informatics Association in 2015 and 2016.
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    Secondary Faculty

Judith DexheimerJudith Dexheimer, PhD

Assistant Professor, Emergency Medicine  Dr. Dexheimer has a background in developing, implementing and evaluating clinical information systems including clinical decision systems, organizational and workflow aspects of informatics applications, computerized applications for emergency medicine and implementation of artificial intelligence techniques, computerized guideline applications and evidence-based medicine, public health informatics, and preventive care measures. Her research focuses on the design, implementation and evaluation of clinical decision support systems in pediatric emergency medicine to improve clinical care.
ResearchGate  |  PubMed

Kevin DufendachKevin Dufendach, MD, MS

Attending Neonatologist, Assistant Professor, Neonatology & Pulmonary Biology  Kevin Dufendach, MD, MS is an Assistant Professor the divisions of Neonatology and Pulmonary Biology and Biomedical Informatics. His research focuses on user-centered design of electronic health record system software. Specifically, he is interested in incorporating human factors principles into the design of human-computer interfaces to better improve information communication for both provider-facing as well as patient-facing applications. Dr. Dufendach’s current research seeks to improve parental engagement in the neonatal intensive care unit through a neonatal-specific inpatient portal application.
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Eric HallEric S. Hall, PhD 

Associate Professor, Neonatology & Pulmonary Biology  Dr. Hall leads data management and integration supporting research and quality improvement within the Cincinnati Children's Hospital Medical Center Perinatal Institute. His research expertise includes geospatial and cost analyses, as well as the development of risk models to identify patients at highest risk for adverse outcomes. Additionally, he leads the implementation of a population-based maternal and infant “data hub.” The data hub integrates regional maternal and child health data originating from hospital electronic health records and vital records as well as geospatially-based measures from the census and other public data resources to facilitate analyses of high-risk perinatal populations.

Google Scholar  |  PubMed  |  Research Gate

Kenneth KaufmanKenneth Kaufman, PhD

Professor, Center for Autoimmune Genomics – CAGE  A large portion of Dr. Kaufman's research career has been on the genetics of systemic lupus erythematsus. Their work has screened 10's of thousands of lupus cases and controls with millions of polymorphic markers. This work has resulted in the identification, replication and/or fine mapping of over 70 genetic associations with systemic lupus erythematsus. Recently, they have taken advantage of next generation DNA sequencing to identify variants that directly cause disease. They have developed a number of bioinformatic pipelines that can be applied to any phenotype. These automated pipelines are part of the Cincinnati Analytical Suite for Sequencing Informatics (CASSI) which has been applied to more than 20 different diseases and provides a list of candidate causative variants that lead to disease.
PubMed

Eric KirkendallEric Kirkendall, MD, MBI, FAAP

Associate Chief Medical Information Officer, Associate Professor, Hospital Medicine  Dr. Kirkendall works to use health information technology to maximize patient safety and quality in clinical care delivery, data management, and novel application/software development. He is the first Associate Chief Medical Information Officer in Cincinnati Children's history. In that role he oversees the design, implementation, and optimization of the electronic health record and other associated technologies. Dr. Kirkendall co-leads the Decision Support Analytics Workgroup (DSAW), which investigates the links between the effectiveness of clinical decision support (CDS), patient safety, and user efficiency. His research has demonstrated ties between decreasing alert burden on clinicians, increasing CDS alert salience, and improving patient outcomes. Many of Dr. Kirkendall’s research projects have also incorporated artificial intelligence techniques (e.g., natural language processing) and other innovative methods to detect adverse events/harm across multiple hospital environments. The results have shown vast improvements in detecting errors related to medication administrations. Dr. Kirkendall has also worked with the Center for Acute Care Nephrology to develop a catalog of detection and risk-stratifying electronic triggers that have resulted in NTMx-AKI reductions of 25-50% across four novel metrics, preventing over 400 children from developing AKI.
ResearchGate  |  PubMed

Kakajan KomurovKakajan Komurov, PhD

Assistant Professor, Experimental Hematology & Cancer Biology  Komurov lab focuses on the systems biology of cancer. We develop and employ computational data mining tools to interrogate clinically exploitable cancer mechanisms from cancer genomics data, and use experimental approaches in vitro and in animal models for their molecular characterization. Specifically, we are studying the core aberrations in the genomic, RNA and protein homeostasis networks in cancers, their role in cancer pathogenicity and therapy response, and the synthetic vulnerabilities imposed by these defects on the tumor cell. In addition, we are developing computational methods and software to enable intuitive and effective functional mining of genomic data.
Lab Website  |  PubMed

Mario MedvedovicMario Medvedovic, PhD 

Professor, Environmental Health  Dr. Medvedovic is developing and applying new statistical and computational methods for the analysis of “big data” in the context of biomedical research. His recent work is focused on the reconstruction of regulatory networks using libraries of genome-scale signatures of cellular perturbations. He is also developing protocols for analyzing next-generation sequencing data, and working on development and application of unsupervised statistical learning approaches based on the non-parametric Bayesian models. He is also the director of the Division of Biostatistics and Bioinformatics in UC's Department of Environment Health.
Lab Website  |  Google Scholar

Jarek MellerJaroslaw (Jarek) Meller, PhD 

Graduate Program Director, Associate Professor, Environmental Health  Dr. Meller serves Graduate Program Director for Biomedical Informatics and also pursues several lines of research in molecular modeling, structural bioinformatics and computational genomics, at the intersection of data science and biomedicine. Dr. Meller and his group have developed a number of successful methods for the prediction of protein structure, protein-protein interactions and functional hot spots in proteins. Several web servers developed by the group, including Sable, Sppider, Minnou and Polyview have widely been used, with a total of over 1 million submissions from more than 30,000 users in many countries. Dr. Meller has also been active in the development and applications of methods for knowledge extraction from high dimensional genomic data. He and his group have been involved in many collaborative projects with direct medical relevance. Examples include identification of markers associated with disease subtypes in cancer and autoimmunity, modeling of signal transduction pathways in differentiation and development, and developing inhibitors of critical protein-protein interactions in autophagy, bone marrow transplants, and pathogen-host interactions.
Lab Website  |  Google Scholar  |  PubMed

Emily MiraldiEmily R. Miraldi, PhD 

Assistant Professor, Immunobiology  The focus of Dr. Miraldi’s research is development of computational methods to build predictive, mathematical models of the immune system from high-dimensional genomics measurements. She collaborates with experimental immunologists to learn how diverse immune cells sense and respond to their environment in both physiological and disease contexts. Miraldi designs studies and develops methods that leverage breakthroughs in biotechnologies, including chromatin accessibility and single-cell gene expression measurements. The resulting genome-scale models (e.g., of transcriptional regulation) provide unbiased, experimentally testable hypotheses. Miraldi’s long-term goal is to use these models to specifically re-engineer immune-cell behavior in the context of autoimmune and other diseases.
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PorolloAlexey_ml-6315Alexey Porollo, PhD 

Assistant Professor, Center for Autoimmune Genomics and Etiology - CAGE  Dr. Porollo is a computational biologist with research focused on the development of new prediction and analytical methods in structural bioinformatics. Applications of computational approaches include structural and functional characterization of proteins and their mutations, rational protein engineering, analysis of biological pathways, identification of new drug targets, virtual drug screening, and microbiome analysis.
Google Scholar  |  Lab Website

Alex TowbinAlexander Towbin, MD 

Associate Chief, Department of Radiology, Associate Professor, Radiology and Medical Imaging  Dr. Towbin is a radiologist, the Neil D. Johnson Chair of Radiology Informatics, and Associate Chief of Radiology, Clinical Operations and Radiology Informatics at Cincinnati Children's. He is a recognized leader in enterprise imaging, structured reporting, and workflow efficiency. In his clinical role, Dr. Towbin specializes in abdominal imaging. His research focuses on radiology clinical informatics, quality improvement, cancer imaging, and imaging of the liver.
PubMed  |  ResearchGate

Matthew WeirauchMatthew Weirauch, PhD 

Assistant Professor, Center for Autoimmune Genomics & Etiology - CAGE  Dr. Weirauch is a computational biologist. His lab seeks to understand the mechanisms of gene transcriptional regulation. Current projects focus on characterizing transcription factor binding specificities, and developing methods for modeling their interactions with DNA, both in vitro and in vivo. His lab applies insights from basic research on transcription factor-DNA interactions to study the mechanisms underlying complex diseases.
Lab Website  |  Google Scholar  |  ResearchGate

Yan XuYan Xu, PhD

Associate Professor, Department of Pediatrics Dr. Xu’s research interest is to develop and apply bioinformatics and systems biology approaches to gain a better understanding of molecular mechanisms behind big data sets. Her current lines of research are focusing on the identification of gene signatures, regulatory networks, and biological pathways controlling lung maturation and diseases. She is actively involved in using high-throughput single cell genomics for the development of LungMAP, a web-based data resource funded by the National Heart, Lung and Blood Institute to provide useful tools and resources for the lung research community.
PubMed

     Associated Graduate Program Faculty

AtluriGowtham Atluri, PhD

Assistant Professor, Electrical Engineering and Computer Systems.  The focus of his research is to develop novel data science insights and methodologies that will accelerate the pace of scientific discovery. Specifically, his main thrust is in developing techniques for discovering untapped information in space-time data that is becoming ubiquitous in several domains, including neuroscience, climate science, mobile health, and social sciences. Development of novel frameworks for knowledge discovery is crucial to tackle the challenges introduced by the characteristics of the data and the new data science problems that arise in these domains. Some key directions in his research that are motivated from the above disciplines include studying networks in space-time data, comparing space-time instances, discovering patterns, and integrating data from different sources. With his work in these directions, he hopes to advance data science and have a far reaching impact in the form of scientific discoveries in several application domains.
Lab Website |  Publications

Raj BhatnagarRaj Bhatnagar, PhD

Professor, Electrical Engineering and Computer Systems.  The main focus of Dr. Bhatnagar's research has been on data mining and pattern recognition problems. More recent studies have developed data mining algorithms for very large and distributed database problems and have applied these algorithms to many application domains including the bioinformatics area. Recent projects include subspace clustering and formal concept analysis for very large datasets, which seeks to develop efficient algorithms on hadoop, using map-reduce paradigm, for mining multi-domain subspace clusters from multiple datasets; mutual K-means clustering algorithms for density-based clusters, and content-based retrieval of images from large spatio-temporal image databases. 
Lab Website  |  Publications

Mark EckmanMark Eckman, MD

Professor, Internal Medicine. Dr. Eckman is a general internist and decision scientist. His research interests lie in combining both clinical and theoretic applications of decision analysis to the care of individual patients and to broader issues of health policy. In particular his methodological interests have included the development of patient-specific decision support tools, cost-effectiveness analysis, and the continued study and development of new decision analytic methods. He uses quantitative methods to help make decisions about the allocation of increasingly scarce health care resources. He also has a long-standing interest in decision analytic issues surrounding anticoagulation therapy within a variety of clinical situations, including atrial fibrillation, venous thromboembolism, and thrombophilic states.
Google Scholar  |  ResearchGate  |  PubMed

UntitledTesfaye B. Mersha, PhD

Associate Professor, Asthma Research. Dr. Mersha's research combines quantitative, ancestry and statistical genomics approaches to unravel genetic and non-genetic contributions to complex diseases and racial disparities in human population, particularly asthma and asthma-related allergic disorders. Current research in his laboratory include: 1) admixture and association analysis; 2)transcriptome profiling studies; 3)  microbiome/epigenome analysis, and 4) developing web-based bioinformatics tools specifically designed to integrate omics from public databases (e.g., 1000 Genomes Project, ENCODE and Epigenome Roadmap). Dr. Mersha's long-term career goals are to develop a program that will lead to an in-depth understanding of the complex interplay between genomic variations and environmental exposure risk factors in the etiology of complex diseases, including asthma.

Lab Website  |  PubMed

Marepalli RaoMarepalli Rao, PhD

Professor, Environmental Health and Biomedical Engineering. Dr. Rao’s research interests include Biostatistics; Statistical Genetics; Survival Analysis; Internet Health Data; Data Mining; Machine Learning; Tissue Engineering; Medical Imaging; and Data Science.
PubMed

Daniel SchauerDaniel P. Schauer, MD, MSc

Associate Professor, Internal Medicine. Dr. Schauer has expertise in the decision sciences, patient-centered outcomes and comparative effectiveness research. Much of his current research is focused on obesity and outcomes associated with bariatric surgery. He is the Principal Investigator on a grant funded by the National Cancer Institute that is examining the relationship between obesity, cancer and intentional weight loss. He has experience using many of the large publicly available datasets including the National Health Interview Survey that is linked to the National Death Index and the Nationwide Inpatient Sample in his research. He has also collaborated with the HMO Research Network using their data sources. Additionally, as associate program director for resident research, he oversees all of the resident research in the Department of Internal Medicine.
Publications  |  ResearchGate