Research Laboratories

Lab Website

The mission of CPSLab@Marquette is to advance the design of data-driven computer systems with an emphasis on improving cybersecurity and user privacy across Cyber-Physical Systems (CPS) and the Internet of Things (IoT). We tackle real-world privacy and security challenges in smart homes, smart buildings, and renewable energy systems.

The research of CPSLab sits at the intersection of systems, security, and machine learning:

• Cyber-Physical Systems & IoT: Designing secure and efficient CPS architectures for smart homes, smart buildings, renewable energy systems and IoT device ecosystems.
  Network Security & Privacy: Preventing user privacy leakage through network traffic analysis, traffic fingerprinting defense, and privacy-preserving data analytics.
• AI & Machine Learning at Edge: Tiny Machine Learning (TinyML), AI@Edge computing, federated learning, and intelligent automation for resource-constrained environments.
• Distributed & Embedded Systems: Design and optimization for security-aware embedded environments, distributed computing architectures, and edge-cloud orchestration.

The Database, AI, and Spatial Systems (DAISS) Lab focuses on developing AI-based frameworks for geospatial and spatiotemporal datasets. The lab also works on optimizing database systems and AI systems, utilizing the best of both domains. Focus areas include Geospatial AI, Geospatial Data Analytics, Machine Learning Systems, and Database Systems.

The projects of DAISS Lab can be summarized as following:

• Spatiotemporal Representation Learning: learning vector embeddings for geospatial and temporal datasets focusing on capturing geospatial object geometry, topology,and contextual semantics. Target applications include but are not limited to disaster management, emergency response, and transportation management.
• AI for Databases: utilizing machine learning techniques for automatic optimization of geospatial database query engines and translating natural language questions into geospatial database queries.
• Database for AI: co-optimizing end-to-end data science pipelines comprising data preprocessing and machine learning inference, mainly focusing on In-Database ML systems.
• AI for Raster Imagery: application oriented research combining satellite imagery and AI to solve real-world problems in the domain of agriculture and natural disasters.

Lab Website

The DATA Lab focuses on solving real-world problems by applying techniques from the broad area of data science and data analytics on both structured and unstructured data. The lab also conducts research on applying machine learning techniques to analyze textual and social media data.

In MODLab, we conduct research on algorithms, analysis, and application of machine learning, optimization and statistical signal processing. With a focus on data science, network science, and intelligent systems, current research interests include the following: 

• Online machine learning and time varying optimization for real-time control and decisions 
• Graph signal processing/machine learning and network data analytics 
• Robust machine learning  
• Privacy-aware machine learning 
• Stochastic and chance constrained optimization  
• Federated learning and distributed optimization 

Application domains include smart power and energy systems, wireless communications and sensor networks, social networks, and healthcare. 

The Seed Scholars Program unifies the missions of Marquette University and Feeding America Eastern Wisconsin who are committed to expanding our experiential learning model. This proven model pairs applied student learning with authentic community partners who share a focus on solving real-world problems, impacting diverse systems at scale, and empowering students with the resources needed to demonstrate excellence, leadership, and service.

Objectives:

• Form an alliance of academic, community, and industry partners focused on creating innovative, mission-oriented solutions for real-world problems
• Recruit, train, and develop an exceptional generation of mission-aligned data scholars
• Host an annual Symposium and Job Fair showcasing mission-aligned scholarly work

Directors: Michael Zimmer and Sabirat Rubya

Lab Website

Our lab focuses on the study of the broad social and ethical dimensions of computing technologies and related implications of our data-driven society. Starting from a sociotechnical stance, we leverage technical, quantitative, and qualitative methods to engage with issues including:

• Human-computer interaction
• Data privacy and surveillance 
• Data and AI ethics
• Social computing
• Health informatics

Director – Dennis Brylow

The lab creates new tools and methods for building and studying complex computer systems. Our emphasis is on embedded, real-time, and network systems, with strong ties to the electrical and computer engineering community, and the computer science education community. Current projects include:

• Experimental Embedded Networking Platform. Creation of laboratory infrastructure and software for research and education in the area of embedded networking appliances, particularly wireless routers and IP telephony. Collaboration with Cisco Systems Advanced Research Division.
• Experimental Embedded Operating System Laboratory. Creation of laboratory infrastructure and software for research and education in area of embedded operating systems. Collaboration with University of Buffalo and University of Mississippi, with funding from the National Science Foundation.
• Embedded Software Transactional Memory. Exploration of an innovative transactional memory model for guaranteeing process synchronization in embedded operating systems. Collaboration with Intel Research.

Director – Iqbal Ahamed

This lab focuses on the research issues in pervasive/ubiquitous computing systems and applications. Current projects include:

• Middleware services for pervasive/ubiquitous computing systems and applications -developing energy efficient infra-structure less device discovery, secure service discovery, location detection and self healing services on wirelessly connected PDAs and Sensors. Using these services to build different applications such as assessment tool, asset tracking, home monitoring, and healthcare applications. Got some equipment from Microsoft research. 2 MS students and 2 undergrad students are working.
• Security, trust and privacy in pervasive/ubiquitous computing- developing initial results for writing grants. 2 MS students are working. NSF support is being requested.
• Pervasive healthcare - applying pervasive computing technologies for wellness monitoring, elderly care, cancer patient care. Collaborating with Medical college of Wisconsin, Milwaukee, WI and Marshfield Clinic, Wausau, WI. 1 undergrad and 1 MS student are working. Microsoft research grant has been requested. NIH support is being requested.
• RFID security - developing security solutions for RFID. Developing initial results for writing grants. 1 MS student is working.