The Advancing Machine and Human Reasoning Lab (AMHR, pronounced "ah-moor"), led by Professor John Licato in USF's Department of Computer Science and Engineering, 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? 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. Think of what word processing software did for humanity: it has spell-checking, it takes care of document formatting, and allows people to focus more on the creative production of high-quality, professional documents. In other words, the combination of people and word processing software together does better than either one can do alone. We believe that one day, we can do this with logical reasoning.
Main research areas at the BioRobotics Lab: Biologically-inspired Robotics, Neural Simulation and Robot Control, Cognitive Robotics, Humanoid Robots, Multi-Robot Systems, and Soccer Playing Robots.
This lab gathers researchers from diversified disciplines who share an interest in: (1) Identifying & understanding the learning barriers encountered by students of the computing disciplines. Developing & 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. (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.
CUTR was established in 1988 in the College of Engineering at the University of South Florida, in Tampa, Florida. USF’s largest non-health research center, CUTR is an internationally recognized transportation research, education and technology transfer/training/outreach center, with a focus on producing products and people. Our work supports transportation agencies, the transportation profession and community, policymakers, and the public. CUTR provides high quality, objective expertise in the form of insightful research, comprehensive training and education, effective technical assistance and in-depth policy analysis, that translates directly into benefits for CUTR’s project sponsors. CUTR’s faculty of 37 full-time researchers, and 57 students, combines academic knowledge and extensive “real world” experience in developing innovative, implementable solutions for all modes of transportation. The multidisciplinary research faculty includes experts in engineering, planning, computer science, economics, public policy, public health, and geography. CUTR logs nearly $20 million per year in expenditures through contracts and grants to support its research, education, training and technical assistance missions.
The Computer Vision and Pattern Recognition Group conducts research and invents technologies that result in commercial products that enhance the security, health and quality of life of individuals the world over. We leverage USF's strengths in Video and Image Analysis Technology, Biometric Technology, Classification and Knowledge Discovery, Affective Computing, VR/AR, HCI, and Medical Data Analysis Technology to impact domestic security, quality of life, and healthcare.
ISL research is focused on learning high quality models from data. Unlabeled data is modeled with clustering algorithms. Mixtures of labeled an 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.
We're bringing ai into the physical world!
The Social Computing Research Lab conducts theoretical and experimental research to overcome critical emerging problems when societies and computing technologies interact closely with each other, while simultaneously enabling new applications. There is a significant emphasis within the group on addressing big-data challenges via effective data mining, data fusion and machine learning techniques, along with security and privacy of designed services and applications. The group's research is strongly multi-disciplinary involving collaborators in computer science, biology, public health, electrical engineering, behavioral sciences, clinical psychiatry, environmental engineering and education. Practical applications of our research are in cyber safety, digital privacy, cyber security, smart healthcare, disaster management, environmental sustenance and more. Students are constantly encouraged to innovate and transition outcomes from the lab to industry.