Adam Perer, Ph.D.

Research Scientist, IBM Research

I blend data visualization and data mining techniques to create visual interactive systems to help users make sense out of big data. Lately, my research focuses on extracting insights from clinical data to support data-driven medicine. -@adamperer

Bio

Adam Perer is a Research Scientist at IBM's T.J. Watson Research Center in New York, where he is a member of the Healthcare Analytics Research Group. His research in visualization and human-computer interaction focuses on the design of novel visual analytics systems. This work has been published at premier venues in visualization, human-computer interaction, and medical informatics (IEEE InfoVis, IEEE VAST, ACM CHI, ACM CSCW, ACM IUI, AMIA). He holds Ph.D. and masters degrees in Computer Science from the University of Maryland, College Park, and a B.S. in Computer Science from Case Western Reserve University.

Recent Talks

MUCMD Talk

MUCMD

35 MINUTES I recently gave a talk at the Meaningful Use of Complex Medical Data Symposium that gives an overview of some of my recent research in healthcare analytics, including CareFlow, MatrixFlow, and our work on predictive modeling.
MUCMD Talk

Health 2.0

5 MINUTES For a quick introduction to CareFlow, I gave a mainstage demo at the Health 2.0 Conference which shows how CareFlow can help identify the most desirable and most problematic treatments for complex patients.

Recent Projects

Frequence

Frequence

Frequence is a visual analytics system that integrates data mining and visualization for finding frequent patterns from longitudinal event sequences. Frequence features a novel frequent sequence mining algorithm to handle multiple levels-of-detail, temporal context, concurrency, and outcome analysis. Frequence also features a visual interface designed to support insights, and support exploration of patterns of the level-of-detail relevant to users.

PAPER Adam Perer and Fei Wang. Frequence: Interactive Mining and Visualization of Temporal Frequent Event Sequences. ACM Intelligent User Interfaces (IUI) 2014. Haifa, Israel. (2014).
CAVA

CAVA

Cohort analysis is a widely used technique for the investigation of risk factors among clinical populations. However, this process is often constrained by a lack of integrated analytics and visualization. To address this challenge, we have designed CAVA, a platform for Cohort Analysis via Visual Analytics, which is designed to help domain experts work faster and more independently when performing retrospective cohort studies.

PAPER PRE-PRINT Zhiyuan Zhang, David Gotz, and Adam Perer. Interactive Cohort Analysis and Exploration. Journal of Information Visualization (IVS). (2014).
VIDEO Demo of CAVA.
CareFlow

CareFlow

CareFlow is a novel visual analytics tool designed to help clinicians devise care plans for their patient. Using historical outcomes from clinically similar patients, CareFlow allows doctors to analyze which treatments have been effective for patients like theirs.

PAPER Adam Perer and David Gotz. Data-Driven Exploration of Care Plans for Patients. ACM CHI 2013. Paris, France. (2013).
PAPER Adam Perer and David Gotz. Visualizations to Support Patient-Clinician Communication of Care Plans. ACM CHI 2013 Workshop on Patient-Clinician Communication. Paris, France. (2013).
VIDEO Demo of CareFlow at Health 2.0. Santa Clara, California. (2013).
Predictive Modeling

Predictive Modeling

Common chronic diseases such as hypertension are costly and difficult to manage, so we explore using predictive modeling and visualization to predict the risk and timing of deterioration in hypertension control.

PAPER Jimeng Sun, Candace D McNaughton, Ping Zhang, Adam Perer, Aris Gkoulalas-Divanis, Joshua C Denny, Jacqueline Kirby, Thomas Lasko, Alexander Saip, Bradley A Malin Predicting changes in hypertension control using electronic health records from a chronic disease management program. Journal of the American Medical Informatics Association (JAMIA). (2013).
Outcome Analysis

Outcome Analysis

Patients' medical conditions often evolve in complex and seemingly unpredictable ways. Even within well-defined episodes of care, variations between patients' progression and outcome can be dramatic. We present a visual analytic system to support outcome analysis from retrospective clinical patient data. Our approach combines (1) visual queries to specify episodes, (2) pattern mining techniques to discover important events, and (3) interactive visualizations uncover event patterns that impact outcome and change over time.

PAPER COMING SOON David Gotz, Fei Wang, and Adam Perer. A Methodology for Interactive Mining and Visual Analysis of Clinical Event Patterns using Electronic Health Records Data. Journal of Biomedical Informatics (JBI). (2014).
MatrixFlow

MatrixFlow

MatrixFlow is a visual analytic tool designed to help aid clinical researchers by making the subtle trends of disease progression more obvious. By unearthing the hidden patterns of co-occuring symptoms in patient health records, emerging health risks may become more discoverable.

PAPER Adam Perer and Jimeng Sun. MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression. American Medical Informatics Association Annual Symposium (AMIA 2012). Chicago, Illinois. (2012).
SLIDES Presentation from AMIA 2012.

ORION

Orion

Orion is a system for interactive modeling, transformation and visualization of network data. Orion’s interface enables the rapid manipulation of large graphs — including the specification of complex linking relationships — using simple drag-and-drop operations.

PAPER Jeffrey Heer and Adam Perer A System for Modeling, Transformation and Visualization of Multi-dimensional Heterogeneous Networks. Information Visualization Journal (2014).
PAPER Jeffrey Heer and Adam Perer A System for Modeling, Transformation and Visualization of Multi-dimensional Heterogeneous Networks. IEEE Visual Analytics Science and Technology (VAST). Providence, Rhode Island. (2011).
SaNDVis

SaNDVis

SaNDVis is a visual analytics tool that mines, aggregates, and infers a social graph from social media. The visualization tool supports people-centric tasks like expertise location, team building, and team coordination in the enterprise.

PAPER Adam Perer, Ido Guy, Erel Uziel, Inbal Ronen, Michal Jacovi. The Longitudinal Use of SaNDVis: Visual Social Network Analytics in the Enterprise. IEEE Transactions in Visualization and Computer Graphics (IEEE TVCG). (2013).
PAPER BEST PAPER AWARD Adam Perer, Ido Guy, Erel Uziel, Inbal Ronen, Michal Jacovi. Visual Social Network Analytics for Relationship Discovery in the Enterprise. IEEE Visual Analytics Science and Technology (VAST). Providence, Rhode Island, USA. (2011).
VIDEO Demo of SaNDVis.

Older Research

Social Media Analytics

Social Media Analytics

While working in the Social Technologies Group at IBM Research Haifa, my research centered around methods and techniques for making sense of big data in social media.

PAPER Michael Muller, Kate Ehrlich, Tara Matthews, Adam Perer, Inbal Ronen, and Ido Guy. Diversity among Enterprise Online Communities: Collaborating, Teaming, and Innovating through Social Media. ACM Conference on Human Factors in Computing Systems (CHI 2012). Austin, Texas. (2012).
PAPER Michal Jacovi, Ido Guy, Adam Perer, Inbal Ronen, Erel Uziel and Michael Maslenko. Digital Traces of Interest: Deriving Interest Relationships from Social Media Interactions. European Conference on Computer-Supported Cooperative Work (ECSCW). Aarhus, Denmark. (2011).
PAPER Ido Guy, Adam Perer, Tal Daniel, Ohad Greenshpan, Itai Turbahn. Guess Who? Enriching the Social Graph through a Crowdsourcing Game. ACM Conference on Human Factors in Computing Systems (CHI). Vancouver, Canada. (2011).
PAPER Ido Guy, Sigalit Ur, Inbal Ronen, Adam Perer, Michal Jacovi. Do You Want to Know? Recommending Strangers in the Enterprise. ACM Conference of Computer Supported Cooperative Work (CSCW 2011). Hangzhou, China. (2011).
PAPER Ido Guy, Michal Jacovi, Adam Perer, Inbal Ronen and Erel Uziel. Same Places, Same Things, Same People? Mining User Similarity on Social Media. ACM Conference of Computer Supported Cooperative Work (CSCW). Savannah, Georgia, USA. (2010).
PAPER Marc A. Smith, Ben Shneiderman, Natasha Milic-Frayling, Eduarda Rodrigues, Vladimir Barash, Cody Dunne, Tony Capone, Adam Perer and Eric Gleave. Analyzing (Social Media) Networks with NodeXL. International Conference on Communities and Technologies (C&T). University Park, Pennsylvania, USA. (2009).
SocialAction

SocialAction

A large amount of my Ph.D. research at the University of Maryland involved making sense out of social networks. Much of this research was integrated into SocialAction, a novel visual social network analytics tool. A variety of publications were published on this work, including:

BOOK CHAPTER Adam Perer. Finding Beautiful Insights in the Chaos of Social Network Visualizations. Beautiful Visualization. O’Reilly Press. (2010).
PAPER Adam Perer and Ben Shneiderman. Integrating Statistics and Visualization: Case Studies of Gaining Clarity During Exploratory Data Analysis. ACM Conference on Human Factors in Computing Systems (CHI). Florence, Italy. (2008).
PAPER Adam Perer and Ben Shneiderman. Systematic Yet Flexible Discovery: Guiding Domain Experts Through Exploratory Data Analysis. International Conference on Intelligent User Interfaces (IUI). Gran Canaria, Canary Islands, Spain. (2008).
PAPER Adam Perer and Ben Shneiderman. Balancing Systematic and Flexible Exploration of Social Networks. IEEE Transactions on Visualization and Computer Graphics (InfoVis 2006). 12(5): 693-700. Baltimore, United States. (2006).
PAPER Adam Perer and Ben Shneiderman. Integrating Statistics and Visualization for Exploratory Power: From Long-Term Case Studies to Design Guidelines.. IEEE Computer Graphics and Applications (CG&A): Special Issue on Visual Analytics Evaluation. 29(3): 39-51 (2009).
Degree-of-Interest Graphs

Degree-of-Interest Graphs

In response to demand for analyzing massive networks, we developed a new methodology for interacting with networks: "Search, show context, expand-on-demand” where users search for particular datapoints as a focus for analysis and the system then computes and displays an optimal relevant context given the users’ current interests.

PAPER Frank van Ham and Adam Perer. “Search, Show Context, Expand on Demand”: Supporting Large Graph Exploration with Degree-of-Interest. IEEE Conference on Information Visualization (InfoVis). Atlantic City, New Jersey, USA. (2009).
PAPER Adam Perer and Frank van Ham. Integrating Querying and Browsing in Partial Graph Visualizations. IBM Technical Report. (2011).
VIDEO Demonstration of Degree-of-Interest Graphs on a large legal citation network.
Email Visualization

Email Visualization

Email, while often considered personal and private, is also a rich source of social data. Over time, people accumulate extensive email repositories that contain detailed information about their personal communication patterns and relationships. My prior research focused on building visualizations that capture hierarchical, correlational, and temporal patterns present in email repositories.

PAPER Adam Perer and Marc A. Smith. Contrasting portraits of email practices: visual approaches to reflection and analysis. International Conference on Advanced Visual Interfaces (AVI). Venice, Italy. (2006).
PAPER Adam Perer, Ben Shneiderman, Douglas W. Oard. Using rhythms of relationships to understand e-mail archives. Journal of the American Society for Information Science and Technology (JASIST) 57(14): 1936-1948 (2006).