Director: Prof. Athina Petropulu
Location: 96 Frelinghuysen Road, Room 532, Piscataway, NJ 08854
Email: athinap @ rutgers.edu
CSPL focuses on research in theoretical issues in statistical signal processing, system modeling, system identification. Applications of interest include wireless communications, networking, radar systems, biomedicine. Recent project include cooperative approaches for low power wireless communications, physical layer security, mobile beamforming, and compressive sensing based MIMO radar.
Director: Prof. Dario Pompili
Location: 96 Frelinghuysen Road, Room 615, Piscataway, NJ 08854
Email: dario.pompili @ rutgers.edu
The Cyber Physical Systems (CPS) Laboratory's overarching mission is to propose novel sensing paradigms to transform raw sensed heterogeneous data into valuable information (by giving semantic meaning to the collected data) and, finally, into knowledge through information fusion and integrat ion. These paradigms will apply to those distributed systems that need to timely react to sensor information with an effective action such as cyber-physical systems, which feature a tight combination of, and coordination between, the system’s computational and physical elements. The significance of the research is to leverage the acquired knowledge to broaden the potential of cyber-physical systems in several dimensions, including: augmentation of human capabilities, understanding of human activities, coordination of heterogeneous (infrared) cameras, operation in dangerous or inaccessible environments, and efficiency.
- Private information retrieval and search in distributed systems.
- Distributed algorithms and codes for the synchronization and deduplication of coded data.
- Secure machine learning algorithms with information theoretical guarantees
As mobile device and technologies continue to have an important impact on our life and eventually become a part of our social fabric, the information infrastructure will increasingly become the tempting targets for malicious attacks. Furthermore, with the huge amount of information transmitted over the networks and eventually displayed in the Internet, the main question that people asked about is what should constitute “fair use” of the information. The Data Analysis and Information SecuritY (DAISY) Laboratory focuses on both addressing the problems in information security, system privacy, and data integrity using statistical approaches and machine learning techniques, as well as building test beds to facilitate research in these areas.
Current Research Areas:
- Cyber Security and Privacy
- Mobile Healthcare and its Security Issues
- Mobile Computing and Sensing
- Connected Vehicles
Email: waheed.bajwa @ rutgers.edu
Research conducted at the Information, Networks, and Signal Processing Research (INSPIRE) Lab provides a fundamental mathematical understanding of and theoretically optimal, computationally efficient, and algorithmically robust solutions for some of the most pressing problems arising in information processing—an umbrella term that subsumes mathematical signal processing, high-dimensional statistics and machine learning—and networked systems, such as (online) social networks, wireless sensor networks, communication networks, multiagent systems, and brain networks.
Email: laleh.najafizadeh @ rutgers.edu
a. Neuroimaging: Functional Brain Imaging, Diffuse Optical Brain Imaging, Brain Connectivity, Cognitive Neuroscience, Brain Computer Interface, Cognitive Rehabilitation; and
b. Analog, Mixed-Signal, VLSI, and mmw Circuit Design: Ultra Low-Power Circuits for Biomedical Applications, Ultra Fast Circuits, System on Chip.
The LIMPH’s mission is to train the next generation of engineers to build innovative biomedical technologies for global health applications with the core objective to realize health equity across the globe. To accomplish this, we design and develop next-generation biosensing technologies for a deeper understanding of immunology, individualized monitoring of infectious diseases, engineering the immune response, and developing point-of-care sensors for global health integration. Our research and training strategy encompass the comprehensive clinical translation pathway from biosensor development, characterization, and finally its validation in multi-center clinical studies.