ASU Center for Assured and Scalable Data Engineering (CASCADE)

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CASCADE Researchers

Research Partners

JCI JCI is a partner in NSF Grant #1339835
EarlyWarning EarlyWarning is a partner in NSF Grant #1430144
Centurylink CenturyLink provides computation power for NSF Grants #1318788 and #1518939
Siemens Siemens is a partner in NSF Grant #1239093
Mayo and CASCADE partners in an NVIDIA GPU Research Center
RAI RAI is a partner of a UNITO/RAI-CRIT research agreement


Support for CASCADE comes from ASU OKED, Fulton Schools of Engineering, and CIDSE. CASCADE is also supported partially by NSF-IIP Grant #1464579.



Gail-Joon Ahn, 


Chitta Baral, 

Artificial intelligence, Knowledge representation, Bioinformatics, Natural language understanding

Prof. Baral’s main research interests are threefold: (i) developing language constructs and surrounding building block results for representing knowledge and reasoning with it, (ii) developing a theory of actions and their impact on an environment and using it in autonomous agent design, planning and diagnosis, (iii) Using (i) and (ii) in modeling cell behavior and reasoning with it to explain observations and develop plans of action so as to alter pathways that could suggest therapeutic procedures.


Dragan Boscovic,

Sensor technologies, Smart Cities, Smart Energy/Grid, and Smart Health

Dr. Boscovic has 25 years of high tech experience acquired in an international set up (i.e. UK, France, China, USA) and is uniquely positioned to help data-driven technical advances within today’s global data intensive technology arena. He is a lateral thinker with broad exposure to a wide range of scientific methods and business practices and has a proven track record in conceiving strategies and managing development, investment and innovation efforts as related to advanced data analysis services,  user experience, and mobile and IoT solutions and platforms. Before joining CASCADE, he has lead a consortium of companies that brings together Data Analytics and Interactive Video Solutions, Security, and wireless SDN competencies and technology enablers to jointly address new business opportunities as related to Smart Cities, Smart Energy/Grid and Smart Health applications and services.


K. Selcuk Candan, 

Scalable data management, Data analysis, Multimedia, Data streams, Real-time data processing, Energy, Healthcare, Data clouds, Information retrieval

Dr. Candan is a Professor of Computer Science and Engineering at ASU. He is also a founding member of ASU’s Information Assurance Center and a senior sustainability scientist at ASU’s Global Institute of Sustainability (GIOS), one of whose missions is to connect researchers with businesses and industry. Candan’s primary research interests are in the area of efficient and scalable management of heterogeneous (such as sensed and streaming media, web and social media, scientific, and enterprise) data and data clouds. He has published over 170 journal and peer-reviewed conference articles, one book on multimedia retrieval, and 16 book chapters. He has 9 patents. At ASU, he  led an effort to establish a “Big Data Systems” concentration for the CS MS Program. He has successfully served as the PI or co-PI of numerous grants, including from NSF (5 ongoing), AFOSR, Army Research Office, Mellon Foundation, HP Labs, and JCI. He also served as a Visiting Research Scientist at NEC Laboratories America for over 10 years.  He is a member of the Executive Committee of ACM Special Interest Group on Management of Data (SIGMOD) and an ACM Distinguished Scientist. 


George Colliat,

Scalable data management, Enterprise applications, Analytics technologies

Prof Colliat recently retired from Oracle where he was recently Senior Vice President of CRM products and joined ASU as a Professor of Practice. He joined Oracle in January 2006 through its acquisition of Siebel, where he managed the development of CRM and Analytics products. In addition to his industry experience, he brings to the team his expertise in scalable data management applied to enterprise applications as well as his management experience in product development and the IT marketplace.


Hasan Davulcu, 

Data analysis, Scalable data management, Social media

Prof. Davulcu's research in data mining and information assurance. His previous works in data and services integration were published at prestigious ACM and IEEE conferences. Prior to joining ASU, US DOD's Defense Logistics Agency (DLA) funded his work in industry. The intelligent agent product that he developed, while at a software startup, was the recipient of a Long Island Software Achievement Award. Dr. Davulcu's research to improve situation-awareness, security and adaptability of service-oriented architectures has been funded by National Science Foundation (NSF) prestigious early CAREER award and the Office of Naval Research (ONR) MURI award. He is currently the PI for an NSF Partnership for Innovation : Building Innovation Capacity (PFI:BIC) grant “Fraud Detection via Visual Analytics: An Infrastructure to Support Complex Financial Patterns (CFP)-based Real-Time Services Delivery” in which he collaborates with PI, Candan, and Early Warning services LLC, to develop a platform to enable integration and enrichment of limited private financial data with larger publicly available data sets to detect fraud and reduce losses due to fraudulent transactions. His recent research in socio-cultural modeling and analysis is funded by a US Department of Defense (DOD) Minerva Award.


Adam Doupé

Cyber security, Computer security, Mobile applications, Enterprise networks, Automated vulnerability analysis, Web security

Prof. Doupé is an Assistant Professor of Computer Science and Engineering at ASU and the co-Director of the Laboratory of Security Engineering for Future Computing (SEFCOM). His research focuses on all aspects of keeping Internet users safe, by securing all levels of the computing stack, including web applications, mobile devices, and enterprise networks. 


Jinguri He

Machine learning, Social media, Healthcare, Rare category analysis

Prof.  He's research interests include rare category analysis and heterogeneous machine learning with applications in social media analysis, healthcare, semiconductor manufacturing, traffic prediction. She is the recipient of the 2014 IBM Faculty Award and has published more than 40 referred articles. She is the author of the book on Analysis of Rare categories (Springer-Verlag, 2011) and has served on the organizing/senior program committees of many top-tier conferences, including ICML, KDD, IJCAI, ICDM, and SDM.


Dijiang Huang

Cloud computing, Computer security, Ad hoc networks


Petar Jevtic,

Risk Modeling, Actuarial Science, Mathemathical Finance

Petar Jevtic is an Assistant Professor at Arizona State University, School of Mathematical and Statistical Sciences. From methodological point of view his research interests are in Predictive Analytics, Stochastic processes and geometry, Lévy Processes, Marked Point Processes, Random Graph Theory and Spatial Point Processes. While, from domain point of view, he works on topics in Actuarial Science: Cyber risk, Smart contract risk, Autonomous Systems, Longevity Risk, Property and Casualty; and topics in Mathematical Finance. He published in top theoretical mathematics (Mathematics of Operations Research), actuarial (Insurance: Mathematics and Economics) and statistics and probability journals (Statistics and Probability Letters).



Subbarao Kambhampati

Artificial intelligence, Automated planning, Information integration, Data cleaning, Information retrieval

Prof. Kambhampati's research interests are in AI, automated planning, information integration, and data cleaning. His recent industrial collaborations include IBM and Google. He is a fellow and "president-elect" of the Association for the Advancement of Artificial Intelligence (AAAI).


Baoxin Li

Multimedia, Machine learning, Image understanding, Health informatics

Prof. Li's research focuses on image and video processing, computer vision, statistical inference and multimedia content indexing and analysis, with applications to visual computing for people with visual impairment, medical image analysis, and vision-based robotics. Prior to joining ASU, he was a Senior Researcher with SHARP Laboratories of America.


Huan Liu

Data mining, Social network analysis, Feature selection

Prof. Liu’s research focuses on social network analysis, machine learning (ensemble methods, active learning, rule extraction, feature selection and discretization, subspace clustering), data mining (data quality and integration, stream data reduction, bioinformatics, algorithm scaling-up) and real world applications (CRM, Egeria detection in imagery, intelligent driving data analysis, recommender systems). He Liu joined ASU after conducting research in Telecom (Telstra) Australia Research labs. 


Ross Maciejewski, 

Data visualization, Data analysis, GIS 

Prof. Maciejewski's primary research interests are in developing novel tools and methods for exploring, analyzing and visualizing data across a variety of domains including climate, public health, social media, criminal incident reports and dietary analysis. How work explores the extraction and linking of disparate data sources exploring combinations of structured geographic data to unstructured social media data to enhance situational awareness. 


Paolo Papotti

Data cleaning, Data integration, Data management

Prof. Papotti's research is focused on systems that assist users in complex, necessary tasks and that scale to large datasets with efficient algorithms and distributed platforms. He is the recipient of a Google Faculty Award, has published more than referred 60 articles, and is work has been recognized with the best demo award at SIGMOD 2015. He has served on the organizing/senior program committees of many top-tier conferences, including SIGMOD, VLDB, and ICDE, and is an associate editor of the ACM Journal of Data and Information Quality (JDIQ).


Maria Luisa Sapino, 

Data management, Data analytics, Data integration, Multimedia, Information retrieval

Prof. Sapino is an Adjunct Professor of Computer Science and Engineering at the Arizona State University and a Professor of Computer Science at University of Torino, Italy.  Her initial contributions to computer science were in the areas of logic programming and artificial intelligence, specifically semantics of negation in logic programming and abductive extensions of logic programs, with applications to the challenges associated with database access control and with heterogeneous data management. Her current research interests are in the areas of heterogeneous (including multimedia) data management, with strong emphasis on tackling the so-called "Big Data challenges", including aspects related to the development of efficient techniques for tensor-based big data analysis, and on various aspects related to indexing, classification, and querying of a large spectrum of data types. She is currently active in multiple interdisciplinary projects, including building energy data management, epidemic data management, smart cities, and sustainable environments.


Mohamed Sarwat

Scalable data management, Recommender systems, Mobile applications, Cloud computing


Paulo Shakarian

Data mining, Social network analysis, Cyber security

Prof. Shakarian specializes in data mining, social network analysis, and cyber security.  His group, the Cyber-Socio Intelligent Systems Laboratory (CySIS) is focused on the development new of novel techniques to tackle data mining problems relating to socio-cultural systems as well as cyber-physical systems.  Specific focus application areas include social media analytics, law enforcement, malware analysis, deep web mining, and infrastructure protection.


Yasin Silva

Scalable data management, Data analysis, Social network analysis

Prof. Silva’s research focuses on scalable and efficient database systems. The focus of his research is two-fold: advancing database systems to respond to requirements of multiple emerging applications (e.g., exploratory data analysis, decision support, and social networking), and extending fundamental concepts of database systems to new data management platforms (e.g., big data systems). His specific areas of interest include similarity-aware data analysis (e.g., similarity search and similarity-based clustering), database query processing and optimization, big data analytics, privacy assurance in databases, and scientific database systems.


Hanghang Tong

Data mining, Multimedia, Social network analysis, Healthcare

Prof. Tong's research interest is in large-scale data mining for graphs and multimedia, with applications to social networks analysis, healthcare, cyber security, and e-commerce. He has published over 80 referred articles and more than 20 patents. He is the associated editor of ACM SIGKDD Explorations and has served as a program committee member in top data mining, databases, and artificial intelligence venues. Before joining ASU, he was a research staff member at IBM T.J Watson Research Center.



Teresa Wu

Health imaging informatics, Building energy management, Swarm intelligence

Prof. Wu's  research interests include health informatics, swarm intelligent algorithms for distributed decisions. Her research has been sponsored by NSF, DoD, NIH, DoED. She works closely with industry and has been involved in several industrial projects for Intel, IBM and Mayo Clinic. Her research collaborators in health informatics are from Mayo Clinic, Banner Alzheimer Institute, Duke CIVM Center, University of Nebraska Medical Center. In 2011, she is appointed as an associate professor of Radiology at Mayo Clinic College of Medicine.


Yezhou Yang

Vision, Cognitive robotics, Deep learning

Prof. Yang is an Assistant Professor at School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. His primary interests lie in Cognitive Robotics, Computer Vision, and Robot Vision, especially exploring visual primitives in human action understanding from visual input, grounding them by natural language as well as high-level reasoning over the primitives for intelligent robots. His research mainly focused on solutions to visual learning, which significantly reduces the time to program intelligent agents. These solutions involve Computer Vision, Deep Learning, and AI algorithms to interpret peoples’ actions and the scene’s geometry. He has published over 30 papers in the fields of Computer Vision, Robotics, AI, NLP, and Multimedia, including first author papers at the top-tier conferences in these areas (CVPR, ECCV, ICRA, AAAI, ACL, and ACMMM) and journals in the related fields. He was a recipient of the KEEN Professorship award on Entrepreneurial Mindset education from ASU 2016 and 2017, a recipient of the Qualcomm Innovation Fellowship 2011, the UMD CS Department Dean's Fellowship award and the Microsoft Research Asia Young Researcher Scholarship 2009. He received a B.A. in Computer Science from Zhejiang University in 2010, and a Ph.D. in Computer Science from the University of Maryland, College Park in 2015. Before joining ASU, Dr. Yang was a Postdoctoral Research Associate at the Computer Vision Lab and the Automation, Robotics, and Cognition (ARC) Lab, with the University of Maryland Institute for Advanced Computer Studies


Ming Zhao

Cloud computing, Autonomic computing, High-performance computing, Virtualization

Prof. Ming Zhao directs the research laboratory for Virtualized Infrastructures, Systems, and Applications (VISA). His research is in the areas of experimental computer systems, including
distributed/cloud, big data, and high-performance computing systems. He is also interested in the interdisciplinary studies that bridge computer systems research with other domains. His research has generated significant real-world impacts and been used by various production systems. His work on a cloud storage caching is now deployed at a leading European cloud company, CloudVPS, and adopted by many others (e.g., Facebook) to provide scalable cloud storage and benefit both cloud providers and users. He has collaborated with IBM on the performance virtualization of high-performance parallel storage systems. He has also received a VMware faculty award to work on the QoS issues of data-intensive computing systems.


Support for CASCADE comes from ASU OKED, Fulton Schools of Engineering, and CIDSE. CASCADE is also supported partially by NSF-IIP Grant #1464579.