Laboratories and Facilities

Coding and Securing Information (CSI) Lab

At CSI Lab, we are interested in solving reliability, security and privacy problems arising from communicating, storing and processing large amounts of data in distributed systems, using tools from information theory and coding theory. Our current research projects include:
- 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

Faculty Lead: Salim El Rouayheb

Communications and Signal Processing Laboratory (CSPL)

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.

Faculty Lead: Athina Petropulu

Computer Vision and Robotics Laboratory

The Computer Vision and Robotics Laboratory, conducts innovative research at the intersection of computer vision and robotics. Focused on areas in machine learning, artificial intelligence and computational photography, the lab is developing new methods in the application areas of precision agriculture, remote sensing, and socially cognizant robotics. Project examples include visual navigation, change detection in satellite imagery, drone-based cranberry crop evaluation, pedestrian behavior analysis, novel cameras for BRDF measurement, photographic steganography, and quantitative dermatology. The lab's interdisciplinary approach reflects the lab's commitment to exploring and understanding the multifaceted applications of AI in our world.

Faculty Lead: Kristin Dana

Cyber Physical Systems (CPS) Lab

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.

Faculty Lead: Dario Pompili

Data Analysis and Information Security (DAISY) Lab

As the influence of information technologies and mobile devices persists and integrates further into the fabric of society, there is a notable surge in the volume of sensing data. Consequently, the demand for efficient data analysis models escalates. Moreover, the information infrastructure is increasingly susceptible to malicious attacks. Given the substantial volume of data transmitted from mobile devices across networks, eventually reaching the Internet, a primary inquiry pertains to the extent to which information can be "sensed" and the delineation of "fair and responsible use" thereof. The focus of the Data Analysis and Information Security (DAISY) Laboratory encompasses research and educational endeavors in Applied Machine Learning in Mobile Computing and Sensing, Internet of Things (IoT), Security in AI/ML Systems, Smart Healthcare, and Deep Learning on Mobile Systems.

Faculty Lead: Yingying (Jennifer) Chen

Hardware Secure Experience Lab (HWSEL)

The Hardware Secure Experience Lab (HWSEL) conducts hardware security and multimedia systems research, aiming to achieve secure experiences in both hardware systems and user-facing applications via cross-layer designs and optimizations. In the hardware security area, we focus on the security of heterogeneous system architectures involving hardware accelerators and disaggregated computing resources. In the multimedia systems area, we address the performance, energy, and security challenges in emerging multimedia systems, such as virtual reality (VR) and augmented reality (AR) systems.

Faculty Lead: Sheng Wei

Information, Networks, and Signal Processing Research (INSPIRE) Laboratory

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.

Lead Faculty: Waheed U. Bajwa

Integrated Systems and NeuroImaging Laboratory

The increasing complexity of today's real-world problems demands new technologies that are developed through multidisciplinary and interdisciplinary approaches. The research in our laboratory focuses on advancing science at multiple frontends, in particular neuroimaging, signal processing, applied machine learning, and bio-electronics, to pursue multidisciplinary solutions for a variety of real-world problems. We are specifically interested in tackling problems in neuroscience and health care, and collaborate with biomedical engineers, neurobiologists, and psychologists.

Faculty Lead: Laleh Najafizadeh

Laboratory of Immuno-engineering and Micro-nano Technologies for Personalized Healthcare (LIMPH)

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.

Faculty Lead: Umer Hassan

NanoBioTechnology Lab

The NanoBioTechnology Lab applies nanotechnology with biomedical applications to solve current health and medical problems. The high cost of diagnostic exams in the clinical setting has resulted in a healthcare crisis both nationally and globally. The lack of sensitivity in current state-of-the-art biosensing platforms used in the clinical setting has resulted in slow and expensive diagnostic exams. This makes it economically unfeasible to regularly screen patients for a wide panel of biomarkers, making impossible the diagnosis of diseases at early stages while still curable. By making use of the advantages offered by micro and nanotechnologies, we aim to develop sensing platforms which will decrease cost, increase assay speed, and improve limit of detection in biomolecular assays.

Faculty Lead: Mehdi Javanmard

Quantum Materials and Devices Laboratory

The laboratory focuses on synthesis and electrical properties of quantum semiconductors for brain-inspired computing, artificial intelligence hardware, and electromagnetic systems. Research is centered around controlled synthesis of complex semiconductor thin film crystals, understanding their electronic and ionic properties. The research is highly collaborative, spanning neuroscience, physics, chemistry, computational sciences and engineering.

Faculty Lead: Shriram Ramanathan

The Swarm Intelligence Lab

In the Swarm Intelligence Lab, we are interested in understanding and engineering the mechanisms enabling the emergence of collective behavior in complex network systems, such as teams of co-robots or schools of fish. Our interdisciplinary research integrates and advances dynamical systems theory, controls, and data science to study pressing problems in biology and engineering, such as collective navigation and learning in animal groups and distributed inference and control in engineered network systems.

Faculty Lead: Daniel Burbano Lombana