Departmental research labs and groups are listed below in alphabetical order.
AMHR is a cross-disciplinary lab dedicated to answering the following guiding research questions: How can artificial intelligence make people better reasoners? How can we create better artificially intelligent reasoners? And how can we advance our knowledge of logic and other cognitive-level reasoning processes in order to produce better conclusions, justifications, and arguments? We're devoted to not only creating smarter AI, but ensuring that these advances help improve, rather than replace, human reasoners. We believe that one day, we can do this with advances in computational, logical, rational, and justifiable reasoning / argumentation.
The focus of this lab is on the defense aspect of the cyber space, and the philosophy is to start from real problems, and create solutions that last. Research attempts to address the root causes of the various cybersecurity problems. The lab works closely with industry to ensure that its work both addresses the most pressing problems of the time, and provides the scientific basis for solutions that can stand the test of time.
Research at the USF BioRobotics Lab covers various areas of robotics and biologically-inspired systems. The lab is involved in various domestic and international research collaborations related to behavioral and neural modeling of spatial cognition in animals. Robot research focuses on autonomous mobile platforms, including single and multi-robot wheeled robots, humanoid robots, aerial and marine systems. The biorobotics lab is also home to the USF RoboBulls autonomous robot soccer team.
This lab gathers researchers from diversified disciplines who share an interest in; 1) identifying and understanding the learning barriers encountered by students of the computing disciplines and developing and evaluating innovative, technology-supported pedagogies to address them, 2) developing Evolutionary techniques that are able to tackle challenging application domains that require very significant adaptive capabilities (e.g., interactive or time-dependent optimization problems), and 3) applying our experience with the above to develop Evolutionary-Aided Teaching and Learning approaches (e.g., autonomous design of practice problems, automated discovery of concept inventories). As a result of these interests, our work spans both the Computing Education research and the Evolutionary Computation fields.
The Computer Vision and Pattern Recognition Group Invents Technologies Resulting in Commercial Products that Enhance the Security, Health and Quality of Life. The lab leverages USF's strengths in Video and Image Analysis Technology, Biometric Technology, Affective Computing, Classification and Knowledge Discovery, and Medical Data Analysis Technology to impact domestic security, quality of life, and healthcare.
Cryptographic Engineering and Hardware Security Lab conducts research on the secure hardware design, implementation, and optimization of crypto-systems. In addition, the lab's focus is on emerging topics in side-channel analysis attacks and countermeasures. The research conducted in this lab includes a number of abstraction levels, including hardware micro architecture and platform specific deeply-embedded hardware systems. The research work platforms utilized are broad, e.g., ASIC/FPGA and embedded processors. Moreover, the lab conducts research on pre- and post-quantum cryptography and security of a number of sensitive and constrained applications including low-power and energy-constrained RFID/NFC technology-based applications and wireless nano-sensors.
Cyber Identity and Behavior Research (CiBeR) Lab
In the Cyber Identity and Behavior Research Lab, we focus on creating dynamic and robust solutions to person identification in online environments. To this end, our research efforts are largely centered upon understanding and modeling cyber behaviors for the validation of patterns of behavior as behavioral biometric modalities. We also aim to uncover signatures of behavior which persist across physical and cyber domains to better interpret and make use of the concept of an identity. Our work spans multiple research areas, including person identification and verification, pattern recognition, computer vision, and cybersecurity. Applications for our research are in forensics, homeland security, online marketing, healthcare, and more.
The Distributed Systems Group focuses on research in computational sociology and distributed systems, with current emphasis on understanding behavior in global-scale social networked systems. Research in the group quantitatively characterizes socio-technical phenomena at scale, models them, applies new understandings to the design of distributed systems, and experimentally measures performance. Interdisciplinary collaborations are highly regarded, as the group often uses results and theories from sociology, psychology and political science to build better interpretations of quantitative observations and to inform design and experiments.
ISL research is focused on learning high quality models from data. Unlabeled data is modeled with clustering algorithms. Mixtures of labeled and unlabeled data are addressed with semi-supervised learning approaches. Of particular interest is big data for which Deep Neural Networks are often useful. Ensembles of different (and the same) types of models are considered, imbalanced data is continually addressed. Imprecision in intelligent systems is also a research topic. Some recent work focuses on learning prognostic models from medical images and clinical data, learning models of activity in very large information networks and clustering data in a network environment. An overall goal is to be able to group large sets of unlabeled data in useful ways, uncovering small, but important groups where they exist. Another goal is to be able to make accurate predictions from potentially large (at least partially) labeled data sets.
The Neuro-Machine Interaction Lab studies mediums of new human interaction with physiological sensors, specifically sensors that acquire brain data. The purpose is to investigate novel methods to use the brain as a third arm to assist users perform daily activities. Also, to measure and decode the affective, cognitive, and emotional state of a person to further understand the brain's behavior during human-machine interaction. The work we do is interdisciplinary and benefits from areas like Psychology, Neuroscience, Computer Science, Electrical Engineering, Arts, and others. General research areas within NCIL are Affective-BCI, Brain-Controlled Drones, and Artistic BCI.
People in RPAL have been working on bringing intelligent robots into our daily living life. Their research focuses include robotic grasping, structured knowledge representation, motion generating, visual SLAM for medical robotics, unmanned aerial vehicle (UAV), and neurorobotics. The lab collaborate with many professors, researchers, and medical doctors in USF College of Engineering, Medical School, Psychology Department, and other prestigious institutions and companies around the world.
The Social Computing Research Lab conducts theoretical and experimental research to address critical emerging problems when societies and computing technologies interact closely with each other. There is a significant emphasis within the group on addressing big-data challenges via effective data mining, data fusion and machine learning techniques. The group's research is strongly multi-disciplinary involving collaborators in computer science, engineering, behavioral sciences, clinical psychiatry and education. Practical applications of our research are in cyber security, smart healthcare, disaster management, environmental sustenance and more. Students are constantly encouraged to innovate and transition outcomes from the lab to industry.
This lab conducts research on: software attacks (such as buffer overflows and SQL injections), runtime defense mechanisms (such as CFI, firewalls, and other monitors), theories of security (such as security models, approaches to composing security policies, and enforceability theory), strong type systems for programming languages, and tools for specifying and managing complex security policies.
Ubiquitous sensing is a new area of research that encompasses the integration of smartphones with senors and Internet technologies to address large-scale societal as well as individual problems.