Labs

BlockChain Lab
Prof. Dragan Boscovic, Head

Blockchain Research Lab's mission is to advance the research and development of blockchain-based technologies for use in Business, Finance, Economics, Mathematics, Computer Science, and all other areas of potential impact. The objective of the Blockchain Research Lab is to create an opening and welcoming environment at Arizona State University where academics, scholars, students, faculty, scientists, and entrepreneurs can come together to see this technology positively impact the world and further our knowledge of how to apply it.

CACTUS Lab
Prof. Jia Zou, Head

ASU's CACTUS data-intensive systems lab is founded in summer 2020 by Dr. Jia Zou with a team of talented ASU students, the lab develops CACTUS data systems---Creative, Automatic, Calibrated, Thrifty, Unified, and Smart--- for managing and integrating Big Data to support AI and advanced applications. Our projects include novel database systems for automatic inference query optimization, accuracy- and privacy-aware AI/ML model management, and LLM-based data management. Over its first five years, the lab has graduated with one postdoc, one Ph.D., and six thesis-based master’s students. Alumni have joined Marquett University (as tenure-track assistant professor), Google, Amazon (3), Nexxen, OuterBounds, and ASU’s Ph.D. program. Our research has been supported by NSF, DHS CAOE, Amazon, and IBM awards, with additional support from Lawrence Berkeley National Laboratory, OpenAI, and Google. Lab publications have appeared in top venues including SIGMOD, VLDB, ICDE, CIDR, EDBT, and others.

• Database systems for automatic inference query optimization,
• Accuracy- and privacy-aware AI/ML model management
• LLM-based data management.

CIPS Lab
Prof. Hasan Davulcu, Director

Cognitive Information Processing Systems (CIPS) Lab's research focuses on developing novel data mining techniques and tools for structuring and organizing unstructured sources such as text, Web, and social network data into semantic machine-processable information. Such representations enable the creation of conceptual maps to allow users to search and browse without information overload.

• Sociocultural Modelling
• Social Media and Web Mining
• Information Extraction and Database Systems
• Behavioral Analytics for Detecting Fraud.

Data Mining and Reinforcement Learning Lab (DaRL)
Prof. Hua Wei, Lead

The Data Mining and Reinforcement Learning Lab (DaRL) conducts research on data-driven learning and decision-making under uncertainty, integrating data mining, machine learning, and reinforcement learning. The lab focuses on reliable and uncertainty-aware learning systems, sim-to-real transfer, and multi-agent decision-making, with applications in intelligent transportation, urban computing, and sustainability. Through open-source platforms and reproducible evaluation, DaRL aims to bridge foundational AI research with deployable real-world systems.

EMITLab
Prof. K. Selcuk Candan, Head

EMITLab in the Center for Assured and Scalable Data Engineering (CASCADE) at Arizona State University carries out research on  data-, machine learning-, and AI-systems that couple real-world data, simulations, and causal models to support informed and effective decision making  in critical complex dynamic systems, such as disasters, pandemics, water resources, and energy infrastructure. Grounded in NSF, DoE, and USACE-funded work, including DataStorm, PanCommunity, PANAX, pCAR, CausalBench, Designing Nature to Enhance Resilience for Built Infrastructure in Western U.S. Landscape , PIRE: Building Decarbonization via AI-empowered District Heat Pump System, and the NSF Center for the Analysis of the Epidemic Expansion, the lab turns sparse, noisy, and complex data and models into actionable insights for robust and resilient human-centric environments.

• Incremental Data Analytics,
• Tensor Decomposition
• High Dimensional Time Series Analysis
• Causal Machine Learning

VISA Lab
Prof. Ming Zhao, Head

Virtualization is an enabling technology for creating important new system abstractions and addressing various challenges faced by today's computing systems. Autonomics plays a key role in handling the increasing complexity of these systems by automatically managing and optimizing them. The fundamental goal of the VISA Research Lab is to explore innovative techniques in virtualization and autonomics to efficiently utilize resources in large-scale, dynamic, and complex computing systems. The Lab focuses on supporting efficient, robust, and secure computing for challenging applications across various domains.

• Cloud Computing
• High-Performance Computing (HPC)
• Big Data Analytics
• Operating Systems & Storage Systems