Data Analytics in Biomedical Informatics

High Dimensional Statistics

Web and Social Media Analytics

Data Science in Oncology

Data Science in Health Sciences

Neural Decision Making

Cancer Genome Institute

Center for Data Analytics in Biomedical Informatics

The Center for Data Analytics and Biomedical Informatics (DABI) consists of eight state of the art research laboratories at the interface between information management, retrieval, analysis and various applied disciplines. Areas of advanced research at DABI Center include data mining, machine learning, databases, pattern recognition, computer vision and Web search. Current projects focus on knowledge discovery in large databases by designing more accurate and efficient methods for data summarization, trend analysis, anomaly detection, clustering, prediction, text, image, video and stream mining. Investigators at the DABI Center are applying their novel solutions to challenging problems in health informatics, geosciences/remote sensing, computer vision, robot mapping and social sciences.


In this talk, Dr. Panov will present his work on an ontology for representing entities from the domain of data mining (OntoDM). The OntoDM ontology defines the most essential data mining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. The ontology comprises of three modular subontologies, covering different aspects of data mining and knowledge discovery process. These include an ontology of core data mining entities (OntoDMcore), an ontology of datatypes (OntoDT), and ontology for representing the knowledge discovery process (OntoDMKDD). OntoDM is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations and others. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with various domain resources and is easy to extend. Finally, the ontology is freely available at

Current Projects

AgencyAward AmountGrant TitleProject Period StartProject Period End
National Science Foundation$100,000BD Spokes09/01/201608/31/2019
Air Force Office of Scientific Research$2,060,356Prospective Analysis of Large and Complex Partially Observed Temporal Social Networks08/01/201207/31/2017
Office of Naval Research160,002Structured Regression in Complex Networks by Fusion of Qualitative Knowledge07/01/201606/30/2017
Space and Naval Warfare Systems$1,196,148Predictive Modeling of Patient State and Therapy Optimization09/01/201105/30/2017
Harvard University$340,512Reverse Engineering Host Resilience05/25/201605/24/2017

Funded Projects

  • 2012 – 2016, “Prospective Analysis of Large and Complex Partially Observed Temporal Social Networks,” Defense Advanced Projects Agency, DARPA-GRAPHS, AFOSR award number FA 9550-12-1-0406 $2,907,908. Obradovic, Z., Scheinberg, K., Fox, E.
  • 2013 – 2015, “Auxiliary System Sensor Fusion – Phase Two,” US Dept. of the Navy, Office of Naval Research, Subcontract to Technical Documentation Inc., SBIR Phase Two Contract N000141-13-C-0118, $316,547 (Temple University share).Obradovic, Z.
  • 2012 – 2014, “Supplement to DARPA-N66001-11-1-4183: Emerging Opportunities to Complement Research onPredictive Modeling of Patient State and Therapy Optimization," Defense Advanced Projects Agency, DARPA-N66001-11-1-4183, $209,930. Obradovic, Z.
  • 2011 – 2015, “Predictive Modeling of Patient State and Therapy Optimization," Defense Advanced Projects Agency, DARPA-N66001-11-1-4183, $987,050. Obradovic, Z.
  • 2011 – 2014, “A Discriminative Modeling Framework for Mining of Spatio-Temporal Data in Remote Sensing,” National Science Foundation, NSF-IIS-1117433, $499,959. Vucetic, S., Obradovic, Z.
  • 2013 – 2014, “Distributed Knowledge Discovery in Big and Evolving Graphs for Modeling Complex Systems,” Extreme Science and Engineering Discovery Environment (XCEDE), IRI130014, 65000 SU on Blacklight and 1,358,947 SU on Stampede. Obradovic, Z..

Center Director

Zoran Obradovic, PhD Laura H. Carnell Professor of Data Analytics Professor, Computer and Information Sciences Department Director, Center for Data Analytics and Biomedical Informatics College of Science and Technology, Temple University

Center for High Dimensional Statistics

High-Dimensional Statistics has emerged as a new field as a result of the confluence of recent advances made in Statistics in response to the urgent need for the development of newer and more appropriate statistical tools to analyze Data Science with high-dimensions. The center will engage the shared research interests and expertise of the Department of Statistics faculty to create an integrated and more vibrant place for research in a field of Statistics that is critically important in Data Science statistical analysis. The center will have the strategic advantage to develop and expand school and university-wide collaborative Data Science research opportunities and will leverage current and future research funding from government agencies and industries that have supported past research.

AgencyAward AmountGrant TitleProject Period StartProject Period EndPI
National Science Foundation$250,000BIGDATA: Statistical Theory and Methods Beyond the Dimensional Barrier09/01/201608/31/2019Zhigen Zhao
National Science Foundation$161,155Bigdata09/01/201508/31/2018Cheng-Yong Tang
National Science Foundation$38,101Exploring Joint Modeling Approaches for Longitudinal data09/01/201508/31/2016Cheng-Yong Tang

Funded Projects

  • 2014-2017, NIGMS “A Statistical Framework for the Spectral Analysis of Electrophysiology,” ($843,465), Dr. Robert Krafty, Principal Investigator.
  • 2013-2017, National Science Foundation, “Collaborative Research: New Directions for Research on Some Large-Scale Multiple Testing Problems”, ($126,575), Dr. Sanat Sarkar, Principal Investigator.
  • 2012-2015, National Science Foundation, “Bayesian Decision Theoretic Methods for Some High-Dimensional Multiple Inference Problems,” ($174,975), Dr. Sanat Sarkar and Dr. Zhigen Zhao Co-Principal Investigator.
  • 2011-2014, National Science Foundation, “New Developments in Sufficient Dimension Reduction,” ($100,000), Dr. Yuexiao Dong, Principal Investigator
  • 2010-2013, National Science Foundation, “Collaborative Research: Constructing New Multiple Testing Methods,” ($167,659), Dr. Sanat Sarkar, Principal Investigator

Center for Web and Social Media Analytics

The Center for Online and Social Data Analytics focuses on opportunities to understand marketing, advertising and journalism problems by drawing upon the Big Data created continuously by social media. Research opportunities focus on real-time automated, targeted and personalized advertising, extraction of useful knowledge from social media, marketing analytics, digital marketing, and the creation of new forms of social media that take advantage of more advanced Internet technologies.
The objective of the Center on Web and Social Media Analytics is to provide thought leadership in the use of web and social media analytics among firms. Specifically, the Center will:
  1. Provide an array of services including fostering cutting-edge research, industry outreach, education, and development of commercial tools.
  2. Bring together researchers from business, computer science, statistics and sociology to develop solutions to challenges faced by firms in online and social media analytics.
  3. Coordinate and seed multidisciplinary research projects which explore how social and web media contribute to market analysis, business decisions and consumer behavior.
  4. Organize industry workshops on online and social analytics.
  5. Offer executive education programs and industry certifications.
  6. Build new curricula related to social media analytics by working with schools and departments across Temple University.

Funded Projects

  • 2012 – 2013, “Designing 21st Century Organizations for Generativity: An Organizational Genetics Approach.” Funded by CIGREF ($92,550).

  • 2012, “Geography and Social Differences in Online Crowdfunded Markets.” Funded by the Temple University Center for International Business, Education and Research ($5,000).

  • Director

    Sunil Wattal, Associate Professor Department of Management Information Systems Fox School of Business, Temple University

    Affiliated Faculty

    • David Schuff, Associate Professor of Management Information Systems
    • Nathan Fong, Assistant Professor of Marketing and Supply Chain Management


    • Emira Zilfa, PhD Student
    • Katrick Ganju, PhD Student
    • Yili( Kevin) Hong, PhD Student

    Areas of Expertise

    • Econometric Analysis
    • Online Consumer Behavior
    • Digital Marketing
    • Social Network Analysis
    • Randomized Control Trials
    • Business Analytics
    • Analytical Methods for Networks

    Current Projects

    • Examining online privacy controls and user engagement: evidence from a randomized experiment in crowdfunding
    • Targeted marketing and customer search
    • How sales taxes affect customer and firm behavior: the role of search on the tnternet
    • Role of social media in political campaigns
    • In-game purchases in online gaming: exploring the impact of user expertise and social norms

    Center for Data Science in Oncology

    Affiliated with the Fox Chase Cancer Center, the Center for Data Science in Oncology unites the power of big data analytics with innovative medical research. At Fox Chase, the Biostatistics and Bioinformatics Facility serves as a shared institutional resource for research related to methodologies such as statistical and bioinformatics consulting. Through the power of Data Science, the facility unites biostatistics with bioinformatics to provide immediate access to quantitative consultation and support. It also grants statistical and bioinformatics expertise to experiment and study design as well as research and manuscript proposals.

    Funded Projects

    • 2011-2014, “Biostatistics and Bioinformatics Facility: Comprehensive Cancer Center Program at Fox Chase” NIH, Dr. Eric Ross, Facility Director; Dr. Richard Fisher, Center Principal Investigator (Facility Budget: $980,000)
    • 2011-2014, “Population Studies Facility: Comprehensive Cancer Center Program at Fox Chase” NIH, Dr. Eric Ross, Facility Director; Dr. Richard Fisher, Center Principal Investigator (Facility Budget: $786,000)
    • 2009-2014, “Biostatistics and Bioinformatics Core: FCCC-PENN SPORE in Ovarian Cancer” NIH, Dr. Eric Ross, Core Director; Dr. Jeffrey Boyd, SPORE Principal Investigator (Core Budget: $530,000)
    • 2011-2014, “Identifying Subgroups with Localized Kidney Cancer Who Can Defer Surgery” NIH, Dr. Brian Egleston, Principal Investigator, (Budget: $173,000)
    • 2013-2015, “Clinical Trials with Exclusions Based on Race, Ethnicity, and English Fluency” NIH, Dr. Brian Egleston, Principal Investigator, (Budget: $173,000)
    • 2003-2011, “Bioinformatics Supplement to the Center for the Environment and Mammary Gland Development“ NIH, Dr. J. Robert Beck, Principal Investigator; Dr. Eric Ross, Co-Investigator (Budget: $500,000)
    • 2009-2012, “Adoption of New Technologies for Remote Data Capture and Protocol Authoring – CCSG Supplement” NIH, Dr. Eric Ross, Project Co-Investigator; Dr. Michael Seiden, Center Principal Investigator (Budget: $150,000)
    • 2006-2007, “PRESAGE caDSR Enablement Study, caBIG™ Population Sciences Special Interest Group” NIH, Dr. Eric Ross, Principal Investigator (Budget: $70,197)
    • 2004-2007, “IAIMS: Integrating Clinical and Molecular Data NIH, Dr. J. Robert Beck, Principal Investigator; Dr. Eric Ross, Co-Investigator (Budget: $330,000)
    • 2005-2006, “Early Detection Research Network (EDRN) - Resource Network Exchange (ERNE)” NIH, Dr. Eric Ross, Principal Investigator (Budget: $99,949)
    • 1995-2012, “Philadelphia Breast Cancer Family Registry” NIH, Dr. Mary Daly, Principal Investigator; Dr. Eric Ross, Co-Investigator (Budget: $474,430/year)

    Center Director

    Eric A. Ross, Ph.D. Assistant Vice President Director, Biostatistics/Bioinformatics and Population Studies Facilities 
    Fox Chase Cancer Center

    Center for Data Science in Health Sciences

    While faculty at the Fox Chase Cancer Center and the Temple University have a long history of conducting research on large data sets, the two institutions are now working together to form the Center on Data Science in the Health Sciences.   This Center will focus on health informatics and other applications of data science by integrating the field of medicine with computer science, statistics, neuroscience, and computational sciences. Projects will include analyses of large-scale data on the human genome, integrated with patient data from electronic medical records and public health records, patient adherence data on medications, and other information that can be used to optimize treatment decisions, assess prognosis, improve patient safety and enhance efficiency in health care institutions.

    AgencyAward AmountGrant TitleProject Period StartProject Period End
    University of Pennsylvania$89,165Smarter Big Data for A Healthy Pennsylvania05/01/201605/31/2019

    Center Director

    Mark Weiner, MD Professor, Department of Clinical Sciences Assistant Dean for Informatics, Temple University School of Medicine Chief Medical Information Officer, Temple University Health System
    Several existing Institutes and Centers at Temple University create an enormous amount of data and deal with big data problems and challenges. We envision these institutes and centers to play a collaborative role with the core centers under the Data Science Institute umbrella. These include:

    The Center for Neural Decision Making (CNDM) examines how an understanding of the brain’s underlying functionality can inform human decision making, behavior and preference formation. The Center uses functional magnetic resonance imaging (fMRI), eye tracking and other biometric (skin conductance, heart rate, breathing) data in combination with existing research methods, to develop models of decision making that correspond to the body’s functionality. Neuroimaging data are large-scale data that require sophisticated data collection, processing, and analysis approaches, and CNDM is at the forefront of experimentation and development of such approaches.
    AgencyAward AmountGrant TitleProject Period StartProject Period EndPI
    Advertising Research Foundation$286,400Neuro 2.0 How Advertising Works Today. Predeictive Metrics for Effective Advertising09/15/201206/30/2015Angelika Dimoka
    Duke University$11,122Impact of In-store Promotions on Consumer Decision Making09/01/201208/31/2015Vinod Venkatraman
    USPS Office of Inspector General$130,042Understanding the Effectiveness of Physical Communications Through Neuroscience12/05/201412/04/2015Angelika Dimoka
    United States Postal Service Office$140,618Understanding How to Optimize Mixed-Media Sequencing09/01/201511/30/2016Angelika Dimoka
    Environmental Defense Fund$107,807Understanding Consumer Decisions Regarding Environmentally-Friendly Energy Options02/01/201609/30/2017Crystal Reeck

    Cancer Genome Institute

    The Cancer Genome Institute (CGI) at Fox Chase Cancer Center (FCCC) has a multidisciplinary team of scientists, clinicians, bioinformaticians, and biostatisticians to guide cancer patients’ treatment or steer the patient into appropriate new drug trials. Additionally, the CGI research division perfoms next-generation sequencing (e.g., whole genome, whole exome, whole transcriptome to support basic research throughout the Temple University/FCCC system.  The mission of CGI is to promote “precision medicine”, by applying advanced DNA sequencing technology to patient care, prevention, and research. CGI generates massive quantities of data, and applying genome sequencing to cancer care will require secure storage of terabytes of data linked to medical records.