Research Areas

Public Safety Coalition Projects

Enterprise Resiliency Experiments

Sports Evacuation Planning

Visual Analytics for Security Applications

Information and Gathering Distillation

Information Networks and Analysis

Information-Driven Modeling and Simulation

Information-Driven Decision Making

Education Initiatives

International Collaborations

About Us

Rutgers, the State University of New Jersey
4th Floor, CoRE Bldg.
96 Frelinghuysen Road
Piscataway, NJ 08854-8018
Tel:  848-445-5928

Purdue University
500 Central Drive, 226
West Lafayette, IN  47907
Tel:   765-496-3747

CVADA Factsheet


Dr. David S. Ebert, Director, VACCINE

Dr. Fred S. Roberts, Director, CCICADA

Project Search

Center for Visualization and Data Analytics

At the Center for Visualization and Data Analytics (CVADA) researchers and educators develop faster ways for data to be collected, distilled, managed, visualized, understood, and shared before, during, and after a crisis. CVADA is creating a foundation in visual and data analytics to enable swiftly sifting through a tsunami of information, in diverse forms, to get early warning of potential threats.

Center Activity

Tweets by ccivisanalytics

Project Spotlights


To Thwart Hoax Distress Calls, the Coast Guard R&D Center Has Partnered with Carnegie Mellon Researchers

Law Enforcement Agencies Using Powerful New Software Tool to Catch Child Sex Traffickers

CCICADA Researchers Probe How to Protect Privacy Rights While Mining and Analyzing Big Data

Rutgers Team Is Applying their ‘Differentially Private Anomaly Detection’ Research to Three Data-Analysis Techniques

Missed a Flight Due to Long Security Lines? TSA Official Unveils ‘Intelligent’ Solution at CCICADA Research Retreat

Howard University Students Gain Valuable Research Experience, PhD Help and Post-Doctoral Fellowship with Help from CCICADA

more news . . .

CCICADA Initiates the DMARA Project to aid the Coast Guard’s Resource Allocation Challenges in the Arctic

The Arctic is a major area of emphasis for the U. S. Coast Guard (USCG) because of the rapidly changing climate and resulting impact on ice conditions and the stress on USCG areas of responsibility. Following on two visits to USCG District 17 (D17) offices in Juneau, Alaska, by CCICADA partners from Rensselaer Polytechnic Institute (RPI), CCICADA has reached agreement with D17 to undertake a project to formulate models designed to analyze and support decisions concerning current, anticipated, and proposed operations of the Coast Guard in the Arctic, specifically in the Bering Strait region. The Dynamic Modeling for Arctic Resource Allocation (DMARA) project has been developed in conjunction with USCG D17 leadership, and USCG D17 Operations and Logistics staff, consistent with D17 Arctic Shield 2013 priorities.

Three specific modeling questions related to USCG resource allocation were identified for further investigation under the DMARA Project: (1) deployment and resource allocation of communications technology for vessel tracking and monitoring in the Bering Straits; (2) dynamic models of the USCG supply chain in D17; and (3) logistics planning for oil spill response resources in the Arctic. Phase 1 of the project will focus primarily on question 3 - resource allocation modeling for oil spill response.

Further Details:

Resource allocation in the Arctic is a persistent and complex challenge that is at the center of many USCG missions, including navigational safety, oil spill response, search and rescue, and traffic management. The Arctic is an immense, seasonally-variable waterway with very little development along its shores. Access to the Chukchi and Beaufort Seas in the western Arctic Ocean occurs through the Bering Strait, a focus of growing interest as marine traffic increases in warmer and longer ice-free Arctic seasons. The Arctic is an environmentally harsh and sensitive area with little commercial, maritime or safety infrastructure, and great distances to access resources in the case of a maritime, personnel casualty or oil spill event.

In the Arctic, as elsewhere, logistics--the procurement, maintenance and transportation of materials, facilities and personnel—is dependent upon existing infrastructure. Lack of infrastructure makes logistics challenging and heightens the need for comprehensive and thoughtful resource allocation models. In the absence of shore-based infrastructure, long-range planning for refueling and replenishment are required. Distances between ports, coupled with the unpredictability of weather, sea states and environmental conditions, complicate access, deployment and supply of critical resources, as well as removal of waste and, in the case of oil spills, recovered product and waste. Public expectations for four-season response capability in the event of an incident also increase the need for thoughtful and flexible planning and robust resource allocation models.

Currently, USCG policy favors seasonal surges of technology, personnel and equipment, supported by industrial contracts for deployable resources, rather than shore-based, pre-positioned assets. Initially, the DMARA project will assess the tradeoffs and net benefits associated with different asset allocation strategies in the Arctic/Bering Strait for oil spill response, one of the USCG key Arctic missions. Other missions—search and rescue, navigational safety or traffic management, etc.—can be explored in follow-on efforts.

The DMARA project will provide the USCG with robust models that will permit examination of persistent resource allocation challenges, as well as examine strengths and vulnerabilities of existing and potential bilateral agreements for oil spill response. Included in this assessment will be an examination of the net benefits of development of deepwater port resources in various settings (Port Clarence, Kotzebue, Kodiak, etc.), and an examination of the importance of rail and/or road transportation infrastructure linking Nome, Kotzebue and Point Clarence, between 65N and 66N on the Seward Peninsula. The models will also consider tradeoffs and options associated with forward deployment, surge deployment and permanent deployment of needed resources for USCG Arctic oil spill response. Other USCG missions, such as navigational safety, search and rescue, or traffic management, can be investigated in subsequent projects.

The first phase of the DMARA project will develop a model that allows decision-makers to assess the tradeoffs between pre-event resource expenditures and post-event response results, including time to an appropriate response and impacts of an incident. The portion of the model considering post-event response will incorporate constraints on the transport of resources from their initial locations to a spill site or appropriate staging area. The modeling approach will be flexible enough to consider response capabilities for multiple distinct geographical regions (e.g., Bering Strait, North Slope Borough, Northwest Borough, Chukchi Sea, Beaufort Sea, etc.) and can incorporate regional priorities. The model can examine resource allocation and budget expenditures over a long planning horizon (5-10 years) and thus can assess various levels of investment into long-term infrastructure capabilities, permanent pre-positioned resources, and seasonal resource surges.

Following development of the initial project, the goal of a follow-on long-term study is to develop models that provide the USCG with robust plans for other missions in the face of dynamic uncertainties. The proposed models can focus both on near-term (e.g., as drilling in the Arctic scales up) and long-term (e.g., the ‘steady-state’ of Arctic drilling operations) response capabilities of the USCG. The models can consider not only where to locate response equipment, resources, and bases but when to locate these response resources. The timing of this location becomes important in both planning robustly for the uncertainties in the environment and in how Arctic operations will scale up over the near-term.



U.S. Coast Guard Accredits Analytical System Developed by VACCINE

In efforts to prioritize and efficiently manage the repair of boats and stations damaged by Superstorm Sandy, the U.S. Coast Guard has accredited a system called Coast Guard Search and Rescue Visual Analytics (cgSARVA) developed in collaboration with Purdue University.

The Coast Guard accredited the system on April 22, 2013, at its headquarters in Washington, D.C.

The cgSARVA tool was created by researchers at the Purdue-led center Visual Analytics for Command, Control and Interoperability Environments, or VACCINE, a U.S. Department of Homeland Security Center of Excellence.

"The accreditation is the first time anything produced by a DHS Center of Excellence has been verified and validated for use by the Coast Guard," says David Ebert, VACCINE director and Silicon Valley Professor of Electrical and Computer Engineering. "The cgSARVA tool can help DHS agencies and law enforcement agencies across the country."

The tool has enabled an interactive visualization, analysis and assessment of search-and-rescue missions completed by each Coast Guard station in hurricane stricken parts of New York and New Jersey.

"The cgSARVA tool is especially helpful in guiding operations and resource decisions by carefully analyzing data in a way that ensures the best return on investment," says Vice Adm. Rob Parker, Coast Guard Atlantic Area commander. "This project serves as a great example of positive partnerships that are being forged between the Coast Guard, the DHS Center of Excellence, and academia."

Purdue initially designed the computer-based visualization to help Coast Guard analysts assess adjustments to boat stations and capabilities on the Great Lakes. It was later used in the Mid-Atlantic region to reallocate resources for Hurricane Irene in 2011 and last year in the aftermath of Superstorm Sandy, which severely damaged 14 Coast Guard stations in the region. The Coast Guard is using the tool to prioritize rebuilding of damaged stations and to help determine which stations should and shouldn't be rebuilt.

"The system can look at what happens if you were not able to immediately rebuild a given station with a certain search-and-rescue caseload," Ebert says. "How long would it take other stations to respond if this station were not here? And if this station were not here, how many cases would have to be handled simultaneously by nearby stations? So it doesn't take all the input and give a final answer, but it provides criteria of the workload and the benefit and what happens if a station closes."

Following Superstorm Sandy, Coast Guard analysts were charged with prioritizing the rebuilding of damaged small-boat stations to determine the order in which stations were to be repaired.

"The cgSARVA model formulation proved to be tremendously insightful for the Coast Guard as it began to prioritize the repair of its stations," says Commander Kevin Hanson, analysis team leader. "Even upon receiving full funding for all damages, the Coast Guard is unable to execute all repairs at the same time and the outputs from cgSARVA have been instrumental in assisting senior leadership in prioritizing work."

Using cgSARVA, the Coast Guard was able to quickly and easily determine how resources might be reallocated in New Jersey, allowing the Coast Guard to operate with increased efficiency.

"A remarkable amount of intellectual rigor has brought us to this point," says Rear Admiral Dean Lee, Deputy for Operations Policy and Capability. "Our analysis team here at headquarters saw tremendous potential in the initial version of cgSARVA and had the organizational vision to expand its capabilities for inclusion in their strategic modeling efforts. Our partnering with Purdue University and the Research and Development Center has yielded insight into our coastal operations that we have never achieved before."

Three Purdue graduate students have been involved in the cgSARVA project, which is ongoing, with researchers continuing to add new capabilities.

The computer-based modeling tool runs on an ordinary computer or laptop.

VACCINE In the News

Purdue Rolls Out Visual Analytics Law Enforcement Toolkit

U.S. University Develops New Sofware Toolkit for Law Enforcement Agencies

New Tool from Purdue Keeps Police Officers Ahead of the Curve


Current Projects

CCICADA Projects

Economics of Security and Randomization (Office of SAFETY Act Implementation)

CCICADA’s research team will concentrate their research in the following three focus areas; (A) economic benefits and costs of security at stadiums, (B) randomization designs, and (C) practical implementation of simple randomization for patron screening. This research will explore the anticipated economic benefits and costs of implementing best practices security strategies at stadium venues that host primarily professional sports and at times entertainment events (such as concerts, monster truck and/or appearances of major world figures), and will assess the anticipated effectiveness of randomizing certain aspects of security such as screening processes (for patrons and vehicles), location and schedule for deployment of security guards and law enforcement, security camera use, and updates of employee background checks.

Large Venue Security

This work builds on the modeling tools we have developed for stadium security in earlier years.  CCICADA will continue its experiments with performance of walk through metal detectors in  real stadium situations. 

CCICADA is also enhancing its crowd management tools, including 2-D and 3-D simulations, in collaboration with various partners.  These partners provide a test-bed for trying out our models. Our models consider both pedestrian movements and vehicle movements and provide guidance as to people and vehicle movement during emergency situations as well as during “normal” operations or operations that depart from the normal due to construction or repairs or emergency situations. 

We will explore the relevance of our crowd management tools to other venues, including malls, convention centers, theaters, casinos, etc. 

Cyber Security: Information Sharing and Metrics

Computer networks control some of the most important critical infrastructure in the world. This includes power systems, water supply systems, air traffic control, building control systems, and transportation systems. This infrastructure is vulnerable to failures of computer systems, accidental disruptions, or deliberate cyber attacks, and the concern about such attacks has been widely discussed. CCICADA plans to continue its work on cyber security in two directions: information sharing to enhance cyber security and metrics to measure cyber readiness. We take our motivation from work we have done on maritime cyber security and expect to continue that work with an emphasis on topics that generalize to other sectors such as the energy and financial sector.

Arctic Resource Allocation and Data Problems

Understanding the rapidly changing conditions in the Arctic presents major challenges in homeland security, dealing with preparation for disasters such as oil spills to finding the best way to monitor changing ice conditions.  The project on Dynamic Modeling of Arctic Resource Allocation (DMARA) has concentrated on oil spill response.

Resource allocation in the Arctic is a persistent and complex challenge that is at the center of many USCG missions, including navigational safety, oil spill response, search and rescue, and traffic management. The Alaskan Arctic, comprised of the Chukchi and Beaufort seas, is an immense, seasonally variable, environmentally sensitive area with very little development along its shores. In the Arctic, there is currently very little commercial, maritime, or safety infrastructure and great distances to access resources in the event of a maritime, personnel casualty, or oil spill incident. This lack of infrastructure makes logistics – the procurement, maintenance, and transportation of materials, facilities, and personnel – challenging and heightens the need for comprehensive and thoughtful resource allocation models. In the absence of shore-based infrastructure, long-range planning for refueling and replenishment are required. Distances between ports coupled with the unpredictability of weather, sea states and environmental conditions complicate access, deployment and supply of critical resources as well as removal of waste and, in the case of oil spills, recovered product and waste. Public expectations for four-season response capability in the event of an incident increase the need for thoughtful planning and robust resource allocation models.

Dynamic network modeling approaches are needed for the Alaskan Arctic because of dynamic budgets, varying personnel and logistical deployments, large-scale supply chains, unique transportation constraints, and highly variable environmental conditions. The models we have been developing should help assess (a) tradeoffs between competing constraints, (b) alternatives and priorities for infrastructure investment, build up, deployment and retirement, (c) costs and impacts of environmental constraints on supply chain, investment and planning alternatives, and (d) needs and requirements for communications, information technology, vessel tracking and monitoring, and integrated technology suites in support of Arctic activities.  

Social Media for Decision Support

In the past seven years researchers at CCICADA have pioneered research related to extracting and tracking events from unstructured texts from a wide range of genres and domains. Our work on social media has evolved into a 4-way collaboration among a group of CCICADA partners. The pieces include RPI’s information extraction system, UIUC’s tools to analyze trustworthiness of social media reports, CMU’s event detection and evolution characterizations, and Rutgers’ use of machine learning and combinatorial optimization tools to characterize requests for emergency assistance and allocate resources based on those requests. We have worked to understand how the social media tools developed by the various partners can be applied to risky situations and are concluding a project aimed at  tying together all of our social media work and completing the development of approaches into one common scenario we have been employing in our research, namely the events surrounding Hurricane Sandy from approach to recovery. Using data from the Twitter firehose, we will be illustrating how our various tools apply to things like: The immediate informational needs for first responders; event annotations with event triggers (dates/times/locations); evolution of responses such as fear and panic and planning with categories of people and location emphasized; requests for aid at different times clustered by type of aid and location.

Research Experiences for Undergraduates

The CCICADA REU program offers a one-on-one research project experience to undergraduates under the mentorship and guidance of CCICADA researchers. The program leverages a large REU program with almost 50 participants that has been run by the DIMACS Center at Rutgers since the early 1990s. The REU program crosses two CCICADA fiscal years, starting in June and ending in August. CCICADA will team with our MSI partner institutions to include their students in the overall REU program, support a competitively-chosen student from other institutions, and introduce the other REU student participants to homeland security applications. Mentors come from CCICADA and all are volunteers and are not compensated for their work. Student research topics are aligned with CCICADA’s research themes. Students present their work at national meetings during the year, and many have won awards for their work. Through a homeland security emphasis, CCICADA introduces all of the almost-50 program participants to the mathematics and computer science aspects of homeland security, thus greatly leveraging a very modest investment. Each year, our REU students also contribute to the research effort at CCICADA. 


VACCINE Projects

A Data Integration Framework for Enhancing Emergency Response Situation Reports with Multi-Agency, Multi-Partner Multimedia Data; Public Safety Coalition Projects

Analytical Visualization of the Port Arthur, TX Economic Impact Study

Analyzing High-dimensional Multivariate Network Links with Integrated Anomaly Detection, Highlighting and Exploration

Bristle Maps - A Multivariate Abstraction Technique for Geovisualization

cgSARVA - Coast Guard Search and Rescue Visual Analytics

Chicago LTE Project

COAST - Coastal Operations and Analysis Suite of Tools

Coast Guard PROTECT Visualization

Collegiate Cybersecurity Defense Competitive System (CCDC)

 (Crowdsourcing) Combining Crowdsourcing technology with machine learning to do visual analytics on big qualitative data (video datasets).

Cyber Education

Cybersecurity Visual Analytics

Design & Development of the Artifact Genome Project (AGP)

Disposable Cell Phone Analysis

Distributed Rendering for Web-Enabling the Stadium Evacuation Planning Tool

vBOLO Project

Explore Impact of Visualization on Predictive Analysis

Financial Fraud Visual Analytics

Foreign Animal and Zoonotic Disease Visual Analytics

GARI - Gang Graffiti Image Recognition and Interpretation

GeoJunction: Collaborative Visual-Computational Information Foraging and Contextualization to Support Situation Awareness

Geovisual Analytics

(GeoViz & CrimeViz) Situational Surveillance & In-field Criminal Investigative Analytics - A Web Service to Geo-Locate Places in Microblog Posts and Other Textual Information Sources

Hazmat Placards

HS-STEM Career Development Program

iLEAPS - iLaw Enforcement Apps Assistance Program for Students

Impacts on Visualization Literacy on Performance of Visual Analytics

Introducing Sustainable Visual Analytics into Command Center Environments

Jigsaw - Visual Analytics for Investigative Analysis on Document Collections

Justice Institute of British Columbia and VACCINE Collaborative Workshops

MADIS - A Data Integration Framework for Enhancing Emergency Response Situation Reports with Multi-Agency, Multi-Partner Multimedia Data

Measuring & Visualizing Information Trustworthiness Using Visual Analytics

MERGE - Mobile Emergency Response Guide

Mobile Application Communication

Mobile 3D Routing, Emergency Evacuation, and In-Field Criminal Investigative Analytics

MSI Collaboration

Multimedia, Social Media, Text, and Emergency Response Analytics

Multimedia Visual Analytics for Investigative Analysis

Officer Performance Visualization System

ORAM - Operational Risk Assessment Module Visualization

Physical Extraction & Reconstruction of Evidence from Mobile Phones Using JTAG Test Ports


Remote Airborne Sensing Technology for Emergency Responders (RASTER)

(Rosetta Phone) Mobile Imaging, Rosetta Phone, and Light-Weight Visual Analytics for In-Field Analytics

Safety in View: A Public Safety Visual Analytics Tool Based on CCTV Camera Angles of View


SensePlace 2 - Collaborative Visual-Computational Information Foraging and Contextualization to Support Situation Awareness

SMART- Social Media Analytics and Reporting Toolkit - Real time Twitter Analysis

Social Media and Healthcare Analytics for Identification of Emerging Health Threats

Symbol Store - Supporting Map Symbol Interoperability

Tech Contract 7 Support for the Cybersecurity Research & Development Program

The Uncertainty of Identity

TRIP - Travel Response Investigative Profiler

User Adoption Learning Tool (Ulearning)

VALET - Visual Analytics Law Enforcement Toolkit

VASA - Visual Analytics for Security Applications

Video Surveillance Visual Analytics

Virtual USA

Visual Analytics Decision Support Environment for Epidemic Modeling and Response Evaluation

Visual Analytics Environment for Public Health Surveillance

Visual Analytics for the DHS Centers of Excellence

Visual Analytics of Microblog Data for Public Response Behavioral Analysis in Disaster Events


Recent Publications


B. Li, J. P. M?noz, X. Rong, J. Xiao, Y. Tian, and A. Arditi, ISANA: Wearable Context-Aware Indoor Assistive Navigation with Obstacle Avoidance for the Blind, Fourth International Workshop on Assistive Computer Vision and Robotics (ACVR) in conjunction with ECCV 2016.

C. Zhang and Y. Tian, Automatic Video Captioning via Multi-channel Sequential Encoding, Fourth International Workshop on Assistive Computer Vision and Robotics (ACVR) in conjunction with ECCV 2016. Dataset of this paper

C. Zhang and Y. Tian, Automatic Video Description Generation via LSTM with Joint Two-stream Encoding, the 23rd International Conference on Pattern Recognition (ICPR), 2016.

C. Zhang and Y. Tian, BCA: Bi-symmetric Component Analysis for Temporal Symmetry in Human Actions, IEEE International Conference on Multimedia and Expo (ICME), 2016.

C. Zhang, Y. Tian, and M. Huenerfauth, Multi-Modality American Sign Language Recognition, IEEE International Conference on Image Processing (ICIP), 2016.

J. P. Munoz, B. Li, X. Rong, J. Xiao, Y. Tian, and A. Arditi, Demo: Assisting Visually Impaired People Navigate Indoors, the 25th International Joint Conference on Artificial Intelligence (IJCAI), 2016.

L. Du, H. Lang, Y. Tian, C. Tan, J. Wu, and H Ling, Covert Video Classification by Codebook Growing Pattern, IEEE International Workshop on Moving Cameras Meet Video Surveillance: from Body Cameras to Drones (MCMVS) in conjunction with CVPR 2016.

Liu, Z., Zhang C. and Tian, Y (accepted 2016). 3D-based Deep Convolutional Neural Network for Action Recognition with Depth Sequences, Image and Vision Computing.

R. Munoz, X. Rong, and Y. Tian, Depth-aware Indoor Staircase Detection and Recognition for the Visually Impaired, The 3rd IEEE International Workshop on Mobile Multimedia Computing (MMC 2016) in conjunction with ICME 2016.

Tang, H. (July 2016). The 1ST Quarterly Report of SRT Follow-On Project, Scientific Assessment and Workforce Development of ORAU.

X. Rong and Y. Tian, Adaptive Shrinkage Cascades for Blind Image Deconvolution, IEEE International Conference on Digital Signal Processing (DSP), 2016.

X. Rong, B. Li, J. P. M?no, J. Xiao, A. Arditi and Y. Tian, Guided Text Spotting for Assistive Blind Navigation in Unfamiliar Indoor Environments, 12th International Symposium on Visual Computing (ISVC), 2016.

X. Rong, C. Yi, and Y. Tian, Recognizing Text-based Traffic Guide Panels with Cascaded Localization Network, ECCV2016 workshop on Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD) in conjunction with ECCV 2016.

Xian, Y., Rong, X, Yang, X. and Tian, Y. (accepted 2016). Evaluation of Low-Level Features for Real-World Surveillance Event Detection, IEEE Transactions on Circuits and Systems for Video Technology.

Y. Xian and Y. Tian, Single Image Super-Resolution via Internal Gradient Similarity, Journal of Visual Communication and Image Representation, pp. 91-102, Vol. 35, Feb. 2016.

Y. Xian and Y. Tian. Resolution Enhancement in Single Depth Map and Aligned Image. IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.

Y. Xian, X. Rong, X. Yang, and Y. Tian, Evaluation of Low-Level Features for Real-World Surveillance Event Detection, IEEE Transactions on Circuits and Systems for Video Technology, Accepted, 2016.

Y. Ye and Y. Tian, Embedding Sequential Information into Spatiotemporal Features for Action Recognition, IEEE CVPR2016 workshop RoF: Robust Features for Computer Vision 2016.

Y. Ye, X. Rong, X. Yang, and Y. Tian, Region Trajectories for Video Semantic Concept Detection, ACM International Conference on Multimedia Retrieval (ICMR), 2016.

Yang and Y. Tian, Super Normal Vector for Human Activity Recognition with Depth Cameras, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted, 2016.

Yang, X and Tian, Y. (accepted 2016). Super Normal Vector for Human Activity Recognition with Depth Cameras, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Ye Y. and Tian, Y. (2016). Embedding Sequential Information into Spatiotemporal Features for Action Recognition, IEEE CVPR2016 workshop RoF: Robust Features for Computer Vision 2016.

Z. Liu, C. Zhang and Y. Tian, 3D-based Deep Convolutional Neural Network for Action Recognition with Depth Sequences, Image and Vision Computing, Vol. 55, Part 2, pp. 93-100, November 2016. doi:10.1016/j.imavis.2016.04.004

Z. Zhu, W. L. Khoo, C. Santistevan, Y. Gosser, E. Molina, H. Tang, T. Ro and Y. Tian. EFRI-REM at CCNY: Research Experience and Mentoring in Multimodal and Alternative Perception for Visually Impaired People. In IEEE 6th Integrated STEM Education Conference (ISEC '16), 2016.

Lehman, F.J. and Singh, R. (2016). Estimation of Children’s Physical Characteristics from their Voices. Interspeech

Singh, R. (2016). Mereological Algebras as Mechanisms for Reasoning about Sound. IEEE International Conference on Machine Learning for Signal Processing,

Singh, R., Baker, J., Pennant, L. and Morency, L. (2016). Voice-based Grading of Psychiatric Disorders. (Under Revision)

Singh, R., Gencaga, D, and Raj, B. (March 2016). Formant Manipulations in Voice Disguise by Mimicry (best paper award). 4th International Workshop on Biometrics and Forensics (IWBF), Cyprus.

Singh, R., Gencaga, D, and Raj, B. (May 2016). Forensic Anthropometry from Voice: An Articulatory-Phonetic Approach. 39th International Convention on Information and Communication Technology, Electronics and Microelectronics: Special Session on Biometrics, Forensics and De-Identification, Opatija, Croatia.

Singh, R., Keshet, J. and Hovy, E. (May 2016). Profiling Hoax Callers. IEEE International Symposium on Technologies for Homeland Security, Boston.

Singh, R., Keshet, J., Gencaga, D. and Raj, B. (March 2016). The Relationship of Voice Onset Time and Voice Offset Time to Physical Age. IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Shanghai.

Singh, R., Raj, B. and Baker, J. (March 2016). Short-Term Analysis for Estimating Physical Parameters of Speakers. 4th International Workshop on Biometrics and Forensics (IWBF), Cyprus.

Dembele, B. & Yakubu, A. A. (2016). Controlling Imported Malaria Cases in Mathematical Biosciences and Engineering, USA.

Doumbia, M. & Yakubu, A. A. (2016 accepted). Malaria Incidence and Anopheles Mosquito Density in Irrigated and Adjacent Non-Irrigated Villages of Niono in Mali , DCDS-B, 2016

Farkas, J., Courley, S., Liu, R. & Yakubu, A. A. (2016 - resubmitted). Modelling Wolbachia Infection in a Sex Structured Mosquito Population Carrying West Nile Virus, Journal of Mathematical Biology.

Saad-Roy, C. M., van den Driessche, P. & Yakubu, A. A. (Submitted, 2016). A Mathematical Model of Anthrax Transmission in Animal Populations. Bulletin of Mathematical Biology.

Siewe, N., Yakubu, A. A., Satoskar, A. & Friedman, A. (2016 - resubmitted). Granuloma Formation in Leishmaniasis: A Mathematical Model. Re-Submitted, Journal of Theoretical Biology.

Siewe, N., Yakubu, A. A., Satoskar, A. & Friedman, A. (2016). Immune Response to Infection by Leishmania: A Mathematical Model. Mathematical Biosciences, 276, 28-28-43.

Ziyadi, N. & Yakubu, A. A. (2016). Local and Global Sensitivity Analysis in a Discrete-Time SEIS Epidemic Model. Advances in Dynamical Systems and Applications, 11(1), 15-15-33.

Damoah, R. (31 May-1 June, 2016). Aerosol Robotic Network: Preliminary Results from All Nations University Station Applications. 5th Annual International Conference on Space Science and Satellite Technology Koforidua, Ghana.

Damoah, R. (May 31 -1 June, 2016). Satellite and Model Analysis: Applications to Air Quality, Food Safety and Human Health, 5th Annual International Conference on Space Science and Satellite Technology Applications, Koforidua, Ghana.

Nkwanta, A. & Barber, J. (2016). Book Chapter in Social Media and Networking: Concepts, Methodologies, Tools, and Applications (4 volumes) Information Resources Management Association (USA).

Prah, B. (August 2016 - abstract accepted). Analysis of Climate Conditions and Outbreak of Mosquitoes in Kenya from 2014-2015. NASA Internships Poster Section 2016, NASA Goddard, Greenbelt

Garrett, R. A. (June 2016). Dynamic Modeling of Arctic Resource Allocation for Oil Spill Response, Masters, Rensselaer Polytechnic Institute.

Garrett, R. A., Sharkey, T. C., Grabowski, M. and Wallace, W. A. (2016). Dynamic Resource Allocation to Support Oil Spill Response Planning for Energy Exploration in the Arctic, European Journal of Operational Research, to appear.

Korolov, R., Peabody, J., Lavoie, A., Das, S., Magdon-Ismail, M. and Wallace, W. (2016). Predicting Charitable Donations Using Social Media, Social Network Analysis and Mining, 6(1), 1-10.

Nguyen, H. & Sharkey, T. C. (2016). A Computational Approach to Determine Damage in Infrastructure Networks from Outage Reports. Optimization Letters, 1-18.

Baron, J.D. and Kahn, J. (2016). Tuza’s Conjecture is Asymptotically Tight for Dense Graphs, Combinatorics, Probability & Computing 25(5): 645-667.

DiRenzo II, J., Drumhiller, N. & Roberts, F.S. (in preparation). Maritime Cyber Security. PSO/Westphalia Press.

Dobson A. and Bekris, K. E. (2015). Planning Representations and Algorithms for Prehensile Multi-Arm Manipulation, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, pp. 6381-6386.

Egan, D., Hering, D., Kantor, P., Nelson, C & Roberts, F.S. (2016) Information Sharing for Maritime Cyber Risk Management, in Direnzo, J. II, Drumhiller, N., Roberts, F.S. (eds.) Maritime Cyber Security, PSO/Westphalia Press, to appear.

Garcia-Quismondo, M. & Fefferman, N., A Machine Learning Framework for Integrative Clustering (in review).

Garcia-Quismondo, M., Lofgren, E. & Fefferman, N., How Will Global Climate Change Impact Seasonality in Childhood Gastrointestinal Infections in the Developed World? (in preparation).

Garcia-Quismondo, M., Mayberry, A., Lofgren, E. & Fefferman, N., Modeling the Effect of Climate Change in the Epidemiological Dynamics of Rotavirus in Children (in review).

Ghassemi, M., Sarwate, A.D. and Wright, R.N. (2016 – to appear). Differentially Private Online Active Learning with Applications to Anomaly Detection, in Proceedings of the 9th ACM Workshop on Artificial Intelligence and Security.

Krontiris, A., Bekris, K. & Kapadia, M. (2016). Acumen: Activity-Centric Crowd Authoring Using Influence Maps. In 29th International Conference on Computer Animation and Social Agents (CASA) Geneva, Switzerland.

Biswal, B., Shetty, S. & Rogers, T. (accepted). Enhanced Learning Classifier to Locate Data in Cloud Datacenters. International Journal of Metaheuristics.

Mcneil, P., Shetty, S., Guntu, D. & Barve, G. (May 2016). SCREDENT: Scalable Real-Time Anomalies Detection and Notification of Targeted Malware in Mobile Devices, 7th International Conference on Ambient Systems, Networks and Technologies (ANT-2016)- International Workshop on Mobile Cloud Computing Systems, Management, and Security.

Mukkavilli, S. K., Shetty, S. & Hong, L. (2016). Generation of Labelled Datasets to Quantify the Impact of Security Threats to Cloud Data Centers, Journal of Information Security.

Oyedare, T., Sharah, A. A. & Shetty, S. (May 2016). Reputation-Based Coalition Game to Prevent Smart Insider Jamming Attacks, in MANETs 14th International Conference on Wired & Wireless Internet Communications (WWIC 2016).

Tweneboah, O. (2016). Challenges in Identifying Integer Sequences (Browsing the OEIS), CCICADA REU Report.

Ward, A. (2016). Climatology and Cluster Analysis: Self Organizing Maps(SOMs), CCICADA REU Report.

Cetin, O., Jhaveri, M., Ganan, C., van Eeten, M. & and Moore, T. (to appear). Understanding the Role of Sender Reputation in Abuse Reporting and Cleanup. Journal of Cybersecurity

Jhaveri, M., Cetin, O., Ganan, C., Moore, T. & van Eeten, M. (in submission). Abuse Reporting and the Fight Against Cybercrime.

Bhuyan, J., Mohapatra, S. & Narang, H. (under review). Deployment of a Secured Cluster-Based Information Retrieval System in the Cloud, International Journal of Network Security & Its Applications.

Iqbal, A. & Wu, F. (Feb 2016). Energy-Accuracy Trade-Off in Wireless Sensor Network Localization. International Journal of Handheld Computing Research (IJHCR), 6(4), 1.

Narang, H., Wu, F. & Ogunniyan, A. (June 2016). Numerical Solutions of Heat and Mass Transfer with the First Kind Boundary and Initial Conditions in Hollow Capillary Porous Cylinder Using Programmable Graphics Hardware.  2016 International Journal of Advanced Computer Science and Applications (IJACSA), 7(6).

Wu, F., Clarke, D., Jiang, J., Baba, A. & Buford, S. (May 2016). The Digital Age of Campus Maps on Mobile Devices, Journal of Computer and Communications, 4(7).

Wu, F., Clarke, D., Jiang, J., Turner, A., Baba A. & Buford, S. (May 2016). Efficiency in Motion: The New Era of E-Tickets. International Journal of Advanced Computer Science and Applications (IJACSA), Volume 7(6). pp. pp. 124-128

Peng, H., Song, Y. & Roth, D. (2016). Event Detection and Co-Reference with Minimal Supervision, EMNLP.

Upadhyay, S., Christodoulopoulos, C. & Roth, D. (2016). Making The News - Identifying Noteworthy Events in News Articles, NAACL Workshop on Events.

Upadhyay, S., Gupta, N., Christodoulopoulos, C. & Roth, D. (2016). Revisiting Evaluation for Cross Document Event Co-Reference, COLING.

Maasberg, M., Ko, M. & Beebe, N. L. (January 5-8, 2016). Exploring a Systematic Approach to Malware Threat Assessment, The 49th Annual Hawaii International Conference on System Sciences (HICSS, Kauai, Hawaii.

Williams, T., Betak, J. & Findley, B. (2016). Text Mining Analysis of Railroad Accident Reports. 2016 Joint Rail Conference

Roberts, F.S. (2016), Meaningful and meaningless statements using metrics for the border condition, Proceedings of 2016 IEEE International Symposium on Technologies for Homeland Security, IEEE, in press (winner of Best Paper award).

Ghemri, L.,  Yuan, S. (2016) .Teaching Security in Mobile Environments. Proceedings of 2016 International Conference in Frontiers in Education: Computer Science and Computer Engineering. Las Vegas, NV, July 25-28, 2016.

Ghemri, L., Wright, R.N. (2016). Accountability in Social Networks. In Data Analytics for Security—Novel Research for Issues in Homeland Security. Ed Hovy (editor). In Advanced Science and Technologies for Security Applications. Springer (to appear 2017)


Year 7 Publication List

Arizona State

  1. Visualizing the Impact of Geographical Variations on Multivariate Clustering, Y Zhang, W Luo, EA Mack, R Maciejewski, Computer Graphics Forum 35 (3), 101-110
  2. Michael Steptoe, Robert Krueger, Yifan Zhang, Xing Liang, Wei Luo, Rolando Garcia, Sagarika Kadambi, Thomas Ertl, Ross Maciejewski. VADER/VIS VAST 2015 Grand Challenge Entry. Proceedings of the IEEE Visual Analytics Science and Technology Challenge Workshop, 2015.


Georgia Tech


  1. Alex Godwin and John Stasko, “HotSketch: Drawing Police Patrol Routes among Spatiotemporal Crime Hotspots”, submitted to the Hawaii International Conference on System Sciences, January 2017.


  1. Alex Godwin and John Stasko, "Drawing Data on Maps: Sketch-Based Spatiotemporal Visualization", (Poster), IEEE Information Visualization Conference, Chicago, IL, Oct. 2015.
  2. Kwon, Bum Chul, Kim, Hannah, Choo, Jaegul, Park, Haesun, and Endert, Alex, "AxiSketcher: Interactive Nonlinear Axis Mapping through User’s Drawing on Visualization” IEEE TVCG 2017 (to appear at IEEE VAST 2016).


Florida International University

  1. Wei Xue, Tao Li, Naphtali Rishe. Aspect Identification and ratings inference for hotel reviews. World Wide Web Journal, 2016, in press.
  2. Liang Tang, Yexi Jiang, Lei Li, Chunqiu Zeng, and Tao Li. 2015. Personalized Recommendation via Parameter-Free Contextual Bandits. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '15).
  3. Wei Xue, Tao Li, Naphtali Rishe: Aspect and Ratings Inference with Aspect Ratings: Supervised Generative Models for Mining Hotel Reviews. WISE (2) 2015: 17-31
  4. Mingjin ZhangHuibo WangYun Lu, Tao Li, Yudong GuangChang LiuErik EdrosaHongtai LiNaphtali Rishe: TerraFly GeoCloud: An Online Spatial Data Analysis and Visualization System. ACM TIST 6(3): 34 (2015)
  5. Yilin Yan, Min Chen, Mei-Ling Shyu, and Shu-Ching Chen, "Deep Learning for Imbalanced Multimedia Data Classification," IEEE International Conference on Multimedia (ISM 2015), Miami, FL, pp. 483-488, December 14-16, 2015.
  6. Hsin-Yu Ha, Yimin Yang, Samira Pouyanfar, Haiman Tian, and Shu-Ching Chen, "Correlation-based Deep Learning for Multimedia Semantic Concept Detection," The 16th International Conference on Web Information System Engineering (WISE 2015), Miami, FL, pp. 473-487, November 1-3, 2015.
  7. Hsin-Yu Ha, Shu-Ching Chen, and Mei-Ling Shyu, "Negative-based Sampling for Multimedia Retrieval," The 16th IEEE International Conference on Information Reuse and Integration (IRI 2015), San Francisco, USA, pp. 64-71, August 13-15, 2015. 3.
  8. Yimin Yang and Shu-Ching Chen, "Ensemble Learning from Imbalanced Data Set for Video Event Detection," The 16th IEEE International Conference on Information Reuse and Integration (IRI 2015), San Francisco, USA, pp. 82-89, August 13-15, 2015


Oxford University

  1. K. L. Tam, V. Kothari, and M. Chen. An analysis of machine- and human-analytics in classification. To appear in IEEE Transactions on Visualization and Computer Graphics, 23(1), 2017. (To be presented in IEEE VIS 2016.)


Penn State


  1. GeoCorpora: Building a Corpus to Test and Train Microblog Geoparsers (submitted) International Journal of Geographical Information Science


Prairie View

  1. Examining the Role of Social Media in Disaster Management from an Attribution Theory Perspective


Purdue University – Ed Delp

  1. Kim, L. Huffman, H. Li, J. Yue, J. Ribera, E. Delp, " Automatic and Manual Tattoo Localization,” Proceedings of the IEEE International Symposium on Technologies for Homeland Security, Waltham, MA, May 2016.
  2. Kim, H. Li, J, Yue, E, Delp, "Tattoo Image Retrieval for Region of Interest,"Proceedings of the IEEE International Symposium on Technologies for Homeland Security, Waltham, MA, May 2016.
  3. Delgado, K. Tahboub and E. J. Delp, "Superpixels shape analysis for carried object detection,” Proceedings of the IEEE Winter Applications of Computer Vision Workshops, Lake Placid, NY, 2016, pp. 1-6.


Purdue University – Ebert

  1. Xia, J., Hou, Y., Chen, V., Qian, C., Ebert, D., Chen, W., “Visualizing Rank Time Series of Wikipedia Top Viewed Pages,” IEEE Computer Graphics and Applications, to appear 2016.
  2. Chae, J., Zhang, J., Ko, S., Malik, A., Connell, H., Ebert, D., “Visual Analytics for Investigative Analysis of Hoax Distress Calls using Social Media”, IEEE International Conference on Technologies for Homeland Security, 2016
  3. Zhang, J., Ahlbrand, B., Malik, A., Chae, J., Min, Z., Ko, S. and Ebert, D., “A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation”, Computer Graphics Forum, 35: 441–450, 2016.
  4. Xia, J., Chen, W., Hou, Y., Hu, W., Huang, X., H., Ebert, D., “DimScanner: A Relation-based Visual Exploration Approach Towards Data Dimension Inspection,” IEEE Visual Analytics Science and Technology (VAST) Conference, 2016.
  5. Ko, S., Cho, I., Afzal, S., Yau, C., Chae, J., Malik, A., ... & Ebert, D. S. (2016, June). A Survey on Visual Analysis Approaches for Financial Data. InComputer Graphics Forum(Vol. 35, No. 3, pp. 599-617).
  6. Koh, Y., Mohan, A., Wang, G., Xu, H., Malik, A., Lu, Y., Ebert, D., “Improve Safety Using Public Network Cameras,” IEEE Homeland Security Technology 2016, 2016.
  7. Zhao, J., Wang, G., Chae, J., Xu, H., Chen, S., Hatton, W., ... & Malik, A. (2015, October). ParkAnalyzer: Characterizing the movement patterns of visitors VAST 2015 Mini-Challenge 1. InVisual Analytics Science and Technology (VAST), 2015 IEEE Conference on (pp. 179-180). IEEE.
  8. Ebert, D., Fisher, B., Gaither, K., “Introduction to the Minitrack on Interactive Visual Decision Analytics,” 2016 49th IEEE Hawaii International Conference on System Sciences (HICSS), 2016.
  9. Chae, J., Wang, G., Ahlbrand, B., Gorantla, M. B., Zhang, J., Chen, S., ... & Ko, S. (2015, October). Visual analytics of heterogeneous data for criminal event analysis VAST challenge 2015: Grand challenge. InVisual Analytics Science and Technology (VAST), 2015 IEEE Conference on (pp. 149-150). IEEE.
  10. Hatton, W., Zhao, J., Gorantla, M. B., Chae, J., Ahlbrand, B., Xu, H., ... & Ko, S. (2015, October). Visual analytics for detecting communication patterns. InVisual Analytics Science and Technology (VAST), 2015 IEEE Conference on (pp. 137-138). IEEE.
  11. Badam, S. K., Zhao, J., Sen, S., Elmqvist, N., & Ebert, D. (2016, May). TimeFork: Interactive Prediction of Time Series. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5409-5420). ACM.
  12. Archambault, D., Bunte, K., Carreira-Perpiñán, M. Á., Ebert, D., Ertl, T., & Zupan, B. (2015). 4.2 Machine Learning Meets Visualization: A Roadmap for Scalable Data Analytics.Bridging Information Visualization with Machine Learning, 7.
  13. Improve Safety using Public Network Cameras
  14. Visual Analytics for Investigative Analysis of Hoax Distress Calls using Social Media


  1. Chae, J., Zhang, J., Jeong, S., Jang, Y., Malik, A., Ebert, D., “Forecasting the Flow of Human Crowds”, IEEE Visual Analytics Science and Technology (VAST) Conference, 2016
  2. Wang, A. Malik, S. Chen, S. Afzal, D. S. Ebert. A Client-based Visual Analytics Framework for Large Spatiotemporal Data under Architectural Constraints. IEEE Symposium on Large Data Analysis and Visualization.


University of North Carolina at Charlotte


  1. Todd Eaglin, William Ribarsky, and Isaac Cho. Space-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics. Submitted to Hawaii International Conference on Systems Science (HICSS 2017).
  2. Isaac Cho, Wenwen Dou, and William Ribarsky. CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data. Submitted to IEEE Trans. on Visualization and Computer Graphics.
  3. Alex Endert, William Ribarsky, Cagatay Turkay, Ignacio Blanco, and Fabrice Rossi. The State of the Art in Coupling Machine Learning with Visual Analytics. Submitted to Computer Graphics Forum.


Year 6 Publication List

Arizona State

  1. Abish Malik, Ross Maciejewski, Sean McCullough, Sherry Towers, David S. Ebert. Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement. IEEE Transactions on Visualization and Computer Graphics, 20(12): 1863-1872, 2014
  2. Yafeng Lu, Feng Wang, Ross Maciejewski. Business Intelligence from Social Media: A Study from the VAST Box Office Challenge. IEEE Computer Graphics and Applications, 34(5): 58-70, 2014
  3. Yafeng Lu, Robert Kruger, Dennis Thom, Feng Wang, Steffen Koch, Thomas Ertl, Ross Maciejewski. Integrating Predictive Analytics and Social Media. Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 2014


Florida International University

  1. Hsin-Yu Ha, Shu-Ching Chen and Min Chen, “FC-MST: Feature Correlation Maximum Spanning Tree for Multimedia Concept Classification,” Ninth IEEE International Conference on Semantic Computing (IEEE ICSC2015), Anaheim, California, USA, pp. 276-283, February 7-9, 2015
  2. Yexi Jiang, Chunqiu Zeng, Jian Xu, Tao Li. “Real time contextual collective anomaly detection over multiple data streams,” ACM SIGKDD Workshop Outlier Detection & Description under Data Diversity (SIGKDD Workshop ODD^2), 2014.
  3. Li Zheng, Tao Li and Chris Ding, “A Framework for Hierarchical Ensemble Clustering,” ACM Transactions on Knowledge Discovery from Data (ACM TKDD), 9(2): 9, 2014.
  4. Liang Tang, Yexi Jiang, Lei Li, Tao Li, “Ensemble Contextual Bandits for Personalized Recommendation,” ACM Conference on Recommender Systems (RecSys), 2014.
  5. Lei Li and Tao Li, “An Empirical Study of Ontology-based Multi-Document Summarization in Disaster Management,” IEEE Transaction on Systems, Man, and Cybernetics: Systems, 44(2), pages 162-171, 2013.
  6. Chunqiu Zeng, Hongtai Li, Huibo Wang, Yudong Guang, Chang Liu, Tao Li, Mingjin Zhang, Shu-Ching Chen, Naphtali Rishe, “Optimizing Online Spatial Data Analysis with Sequential Query Patterns,” The 15th IEEE international Conference on Information Integration and Reuse(IRI 2014)
  7. Mingjin Zhang, Huibo Wang, Yun Lu, Tao Li, Yudong Guang, Chang Liu, Erik Edrosa, Hongtai Li, Naphtali Rishe, “TerraFly GeoCloud: An Online Spatial Data Analysis and Visualization System.”
  8. Li Zheng, Chao Shen, Liang Tang, Chunqiu Zeng, Tao Li, Steve Luis, and Shu-Ching Chen, “Data Mining Meets the Needs of Disaster Information Management,” In IEEE Transactions on Human-Machine Systems, 43(5): 451-464, 2013.


Georgia Tech

  1. Carsten Görg, Zhicheng Liu, and John Stasko, "Reflections on the Evolution of the Jigsaw Visual Analytics System", Information Visualization, Vol. 13, No. 4, Oct. 2014, pp. 336-345.
  2. Jaegul Choo, Yi Han, Mengdie Hu, Hannah Kim, James Nugent, Francesco Poggi, Haesun Park, John Stasko, "Exploring Anomalies in GAStech", Proceedings of IEEE VAST '14 (VAST Challenge paper), Paris, France, Nov. 2014, pp. 347-348.
  3. Alex Godwin, Anand Sainath, Sanjay Obla Jayakumar, Vivek Nabhi, Sagar Raut, John Stasko, "Exploring Spatio-Temporal Data as Personal Routes" (Poster), IEEE Information Visualization Conference, Paris, France, Nov. 2014.
  4. John Stasko, "Value-Driven Evaluation of Visualizations", Proceedings of BELIV 2014, Paris, France, November 2014, pp. 46-53.


Penn State

  1. Wallgrün, J.O., Karimzadeh, M., MacEachren, A.M., Hardisty, F., Pezanowski, S. and Ju, Y. 2014: Construction and First Analysis of a Corpus for the Evaluation and Training of Microblog/Twitter Geoparsers. In Purves, R. and Jones, C., editors, GIR'14: 8th ACM SIGSPATIAL Workshop on Geographic Information Retrieval, Dallas, TX: ACM.

Not funded by, but derived from GeoTxt research: MacEachren, A.M. 2014: Place Reference in Text as a Radial Category: A Challenge to Spatial Search, Retrieval, and Geographical. Position paper for the 2014 Specialist Meeting — Spatial Search, Santa Barbara, CA: UCSB Center for Spatial Studies. Page 48-51

Purdue - Delp

  1. Albert Parra Pozo, August 2014, “Integrated Mobile Systems Using Image Analysis With Applications In Public Safety”
  2. Bin Zhao, December 2014, "Image Analysis Using Visual Saliency with Applications in Hazmat Sign Detection and Recognition."
  3. Ribera, K. Tahboub and E. J. Delp, “Automated crowd flow estimation enhanced by crowdsourcing,” Proceedings of the IEEE National Aerospace and Electronics Conference (NAECON), June 2014, Dayton, OH.
  4. Delgado, K. Tahboub and E. J. Delp, “Automatic detection of abnormal human events of train platforms,” Proceedings of the IEEE National Aerospace and Electronics Conference (NAECON), June 2014, Dayton, OH.
  5. Zhao and E. J. Delp, “Visual Saliency Models Based on Spectrum Processing,” Proceedings of the IEEE Winter Conference on Applications of Computer Vision, January 2015, Hawaii, pp. 976-981.
  6. Tahboub, N. Gadgil, J. Ribera, B. Delgado, and E. J. Delp, "An Intelligent Crowdsourcing System for Forensic Analysis of Surveillance Video," Proceedings of the IS&T/SPIE Conference on Video Surveillance and Transportation Imaging Applications, vol. 9407, San Francisco, February 2015.
  7. Kim, A. Parra, H. Li, E. J. Delp, "Efficient Graph-Cut Tattoo Segmentation," Proceedings of the IS&T/SPIE Conference on Visual Information Processing and Communication, vol. 9410, San Francisco, February 2015.
  8. Ribera, K. Tahboub, and E. J. Delp, "Characterizing The Uncertainty of Classification Methods and Its Impact on the Performance of Crowdsourcing," Proceedings of the IS&T/SPIE Conference on Imaging and Multimedia Analytics in a Web and Mobile World, vol. 9408, San Francisco, February 2015.



VACCINE Students

Guizhen Wang (Graduate Student)

Calvin Yau (Graduate Student)

Jiawei Zhang (Graduate Student)

Jieqiong Zhao (Graduate Student)


Jeff Avery (HS-STEM Student)

Scott Carr (HS-STEM Student)

Kelly Cole (HS-STEM Student)

Whitney Haung (HS-STEM Student)

Oyindamola Olutwatimi (HS-STEM Student)

Rachel Sitarz (HS-STEM Student)



CCICADA Students

Rutgers University
Charlie File (DHS Fellow)
Manuel Garcia-Quismondo
Alex Mayberry
Vanessa Kitzie
Athanasis Kronitiris
Chintan Dalal  (DHS Fellow)
Jacob Baron  (DHS Fellow)
Andrew Dobson  (DHS Fellow)
Liyang Xie
Alisa Matlin
Aditya Chukka
Georgiana Haldeman
Xiaowen Jiang
Kyle J Leonard (University of Nebraska)
Yi Yu
Victor Baqeuro
Prarthana Raja
William Yau
Arvind Kann
Ashley DeNegre
Michael Lan
Katie L. McKeon  (DHS Fellow)
Han Meng
Kevin McInerney
Cheng Yin
Ulu Sevincgul
University of Illinois at Urban-Champaign
Nitish Gupta
Pavankumar Muddireddy
Haoruo Peng
Jiarui Xu
Carnegie Mellon University
Dongyeop Kang
Shuxin Yao
Rensselaer Polytechnic Institute
Richard Garrett (DHS Fellow)
Aaron Rowen (DHS Fellow)
Huy Nguyen
Rostyslav Korolov
John Peabody
Allen Lavoie
Hao Li
Orkun Baycik
Joyce Liu
Howard University
Moussa Doumbia
Nourridine Siewe
Pratik Koirala
Subhay Manadhar
Shannel Thomas
Morgan State University
Bashan Prah
Abena Aduesi
Dexter Harris
Texas Southern University
Omoikhefe Eboreime
Emma Hamilton
Andre Parrot
Sabrina Shahnaj
Otis K Tweneboah
Ayzha Ward 
City University of New York (MSI Summer Program)
Tamar Lichter
Gautam Ramasubramanian
Chenyang Zhang
Xuejian Rong
Yang Xian
Yuancheng Ye
Rai Munoz
Greg Olmschenk
Norbu Tsering
David Zeng
Vishnu Nair
Tuskegee University
Dwayne Clarke
Kushal Prayakarao
Abisoye Ogunniyan
Destanni Golatt
Tennessee State (MSI Summer Program)
Justin Blake Bowers
Ebholo Ijieh
Karzan Mohamad-Ali
Tarence Rice
Anthony Wadsworth
Ayana Wild
Colorado State University
Anant Shah
Dan Rammer
University of Tulsa
Matthew Weeden
Orcun Cetin
Marie Vasek
Georgetown University
Beom Jun Kim


Software Technologies

Smart Transportation Hub
Beacon based localization app
Stadium Security Simulation
BAM II boat sharing
MINES-SAR (MISLE INconsistency & Error Screening for SAR)
PABT Simulation
PABT Unity Visualization
BGPmon (Watchdog)
WAT (web archival tool)


Research Partners


Rutgers, the State University of New Jersey (CCICADA Lead)
University of Illinois at Urbana-Champaign
University of Southern California - Information Sciences Institute
Carnegie Mellon University
Rensselaer Polytechnic Institute
University of Massachusetts-Lowell
Princeton University
City College of New York
Morgan State University
Howard University
Texas Southern University
Tuskegee University
Applied Communication Sciences
Regal Decision Systems
Alcatel-Lucent Bell Labs
AT & T Labs- Research


University Partners
  • American Military University
  • Arizona State University
  • Florida International University
  • Georgia Institute of Technology
  • Morgan State University
  • University of North Carolina at Charlotte
  • Oak Ridge National Lab
  • Oxford University
  • Pennsylvania State University
  • Prairie View A&M University
  • Purdue University
Government Partners
  • Indiana Intelligence Fusion Center
  • Charlotte Mecklenburg Police Department
  • Lafayette Police Department
  • West Lafayette Police Department
  • Purdue Police Departments
  • Tippecanoe County EMA
  • Sherriff's Department
  • Evansville Police Department


Past Partners
University Partners
  • Bethune-Cookman University
  • University of British Columbia
  • University of Calgary
  • Carleton University
  • Dalhousie University
  • University of Houston, Downtown
  • Indiana University
  • Justice Institute of British Columbia
  • University of Manitoba
  • University of Maryland
  • Ontario Institute of Technology
  • Simon Fraser University
  • Stanford University
  • University of Stuttgart
  • University of Swansea
  • University of Texas at Austin
  • University of Victoria
  • Virginia Tech
  • University of Washington
Corporate Partners
  • Motorola Solutions, Inc (inaugural VACCINE LLC Member)
  • Harris Corporation
  • Charles F. Day & Associates
  • Boeing
  • Next Wave Systems, LLC
  • VIN
  • Banfield, The Pet Hospital
  • Raytheon
  • MacDopnald, Dettwiler & Associates
  • Oculus Info Inc.
  • Kx Systems
  • Bank of America
  • Duke Energy
  • World Vision International
  • Gates Foundation
  • Kimberly Clark
  • Hallmark


Government Partners
  • Indiana Intelligence Fusion Center
  • INGang Network
  • Indiana Department of Homeland Security
  • Indiana State Department of Health
  • Indiana Board of Animal Health
  • Georgia Department of Health
  • Coast Guard Seattle Sector
  • Joint harbor Operations Command Center
  • Port of Seattle
  • Automated Regional Justice Information System
  • Port Authority of New York and New Jersey
  • National Maritime Intelligence Center
  • National Geospatial Intelligence Agency
  • National Science Foundation
  • Army Research Office
  • Department of Defense
  • Department of Health and Human Services
  • U.S. Dept. of State
  • Foreign Broadcast Information Service
  • National Institute of Justice
  • Defence Research & Development Canada
  • U.S. Coast Guard
  • Customs and Boarder Patrol, DHS
  • Oak Riddge National Laboratories
  • US Army Corps of Engineers-Engineering Research and Development Center
  • Paciffic Northwest National Laboratory
  • US Attorney’s Office
  • Navajo Nation

CVADA Resources

Gari Training Video

VACCINE Resources
  • Command, Control, and Interoperability Center for Advanced Data Analysis
  • The Center for Education and Reasearch in Information Assurance and Security
  • Department of Homeland Security
  • DHS Centers of Excellence
  • DHS University Programs
  • Department of Electrical and Computer Engineering, Purdue University
  • Foundations of Data and Visual Analytics
  • Indiana Department of Homeland Security
  • Oncological Sciences Center
  • Purdue University Visualization and Analytics Center



Volume 26: February 2017

Volume 25: December 2016

Volume 24: August 2016

Volume 23: January 2015

Volume 22: December 2014

Volume 21: October 2014

Volume 20: September 2014

Volume 19: August 2014

Volume 18: July 2014

Volume 17: June 2014

Volume 16: May 2014

Volume 15: April 2014

Volume 14: March 2014

Volume 13: February 2014

Volume 12: January 2014

Volume 11: December 2013

Volume 10: November 2013

Volume 9: October 2013

Volume 8: September 2013

Volume 7: August 2013

Volume 6: July 2013

Volume 5: June 2013

Volume 4: May 2013

Volume 3: April 2013

Volume 2: March 2013

Volume 1: February 2013