ÏòÈÕ¿ûÊÓƵ

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. 

Directors: Michael Zimmer and Sabirat Rubya

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 computer ethics
• Algorithimic bias and fairness
• 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:

1. 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.
2. 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.
3. 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:

1. 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.
2. Security, trust and privacy in pervasive/ubiquitous computing- developing initial results for writing grants. 2 MS students are working. NSF support is being requested.
3. 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.
4. RFID security - developing security solutions for RFID. Developing initial results for writing grants. 1 MS student is working.