CS faculty are actively engaged in multi-disciplinary collaborations with colleagues in other departments at Mason as well as at other universities
Probabilistic Search Algorithms: Powerful Novel Tools for Peptide Modeling
Description:
This project advances modeling and simulation of biological peptides central to disease and therapeutic treatment by providing a detailed characterization of the structures and structural transitions modulating biological activity. The project establishes seminal interdisciplinary collaborative research integrating cutting-edge computational approaches in Shehu's computational biology laboratory for modeling protein structures and motions with rigorous quantitative approaches in Blaisten-Barojas's laboratory for estimating stability and accessibility of molecular states. The proposed research activities provide a detailed microscopic treatment of the Met-Enkephalin peptide and its structure-function relationship, leading to a better understanding of this peptide and its potential role in pain inhibition, alcoholism, and cancer treatment.
Cyber-Enabled Understanding of Complexity in Socio-Ecological Systems via Computational Thinking
Description:
The project forges interdisciplinary collaboration among anthropologists, political scientists, earth scientists, and computer scientists to advance the science of complex adaptive systems. The core intellectual merit of the project is its contribution to basic understanding of multiscale complexity in climate-society dynamics, by creating a cyber-enabled integrated computational framework for modeling, simulating, and exploring scenarios. The new suite of models will focus on two geographic regions where climate change has significant consequences for humans and ecosystems: Sub-Saharan Africa (over a billion people at high risk of displacement, disease, starvation) and the Arctic Circumboreal region (where the fastest ecological changes are now occurring with shifting patterns comparable to earlier major climate events).
City and County Cross Jurisdiction Cybersecurity Collaboration Capacity Building
Description:
While the nation's cities and counties are often closest to residents in providing citizen services, public safety and critical infrastructure such as public health and transport, many have limited staffing, expertise and cybersecurity budgets. Not only are the residents and cities and counties themselves potentially at risk, public safety, public health and critical infrastructure systems are part of larger connected state and national systems. For example, counties own 45% of the U.S. road miles, 40% of the bridges and operate of 30% of public airports and 1,550 health departments. City and county cybersecurity is an important yet under-focused aspect of our national cybersecurity efforts. Further, current cybersecurity education programs are not addressing some of the special but critical needs facing local governments. This project is contributing to addressing the local government cybersecurity challenges by developing and providing local government specific cybersecurity education and training modules that can augment existing cybersecurity curricula or be provided on a standalone basis.
A Novel Biomechatronic Interface Based on Wearable Dynamic Imaging Sensors
Description:
The problem of controlling biomechatronic systems, such as multiarticulating prosthetic hands, involves unique challenges in the science and engineering of Cyber Physical Systems (CPS), requiring integration between computational systems for recognizing human functional activity and intent and controlling prosthetic devices to interact with the physical world. The objective of this research is to investigate a new sensing paradigm based on ultrasonic imaging of dynamic muscle activity. The synergistic research plan will integrate novel imaging technologies, new computational methods for activity recognition and learning, and high-performance embedded computing to enable robust and intuitive control of dexterous prosthetic hands with multiple DoF. The interdisciplinary research team involves collaboration between biomedical engineers, electrical engineers and computer scientists. The specific aims are to: (1) research and develop spatio-temporal image analysis and pattern recognition algorithms to learn and predict different dexterous tasks based on sonographic patterns of muscle activity (2) develop a wearable image-based biosignal sensing system by integrating multiple ultrasound imaging sensors with a low-power heterogeneous multicore embedded processor and (3) perform experiments to evaluate the real-time control of a prosthetic hand.
Deep Insights Anytime, Anywhere (DIA2) - Central Resource for Characterizing the TUES Portfolio through Interactive Knowledge Mining and Visualizations
Description:
This TUES Central Resource Project is designed to help those engaged in improving STEM education to synthesize knowledge produced through NSF investments through a web-based knowledge mining and interactive visualization platform. The Deep Insights Anytime, Anywhere (DIA2) project allows users (e.g., current and potential principle investigators, NSF/TUES program staff, and administrators at academic institutions) to interactively mine, synthesize, and visualize data at a scale that is not possible with currently available tools. DIA2 is based upon a more narrowly scoped Interactive Knowledge Networks for Engineering Education Research (iKNEER) prototype that targeted the engineering education research community, expanding the functionality by an order of magnitude in scale; integrating newer approaches in data mining and visualization into a fully deployed system.
Learning Data Analytics: Providing Actionable Insights to Increase College Student Success
Description:
The six-year higher-education graduation rate has been around 59% for over 15 years; less than half of college graduates finish within 4 years. This has high human, economic and societal costs. The National Research Council has identified a critical need to develop innovative approaches to improve student retention, graduation, and workforce-preparedness. The objective of this project is to develop new computational methods to analyze large and diverse types of education and learning data to help (a) discover successful academic pathways for students; (b) improve pedagogy for instructors; and (c) enhance student persistence and retention for institutions. The project outcomes are designed to help students select courses that fit their needs, capabilities, and learning styles, and are likely to lead to (faster) graduation; help instructors to better meet student needs; and give advisors and institutions the analytics needed to improve retention and persistence.
Molecular Mechanisms Underlying Menthol Cigarette Addiction
Description:
his project investigates the molecular biology of addiction, focusing on elucidating the role of menthol, a common additive in cigarettes, and its interactions with nicotinic receptors in the brain. The project combines computational and experimental techniques to study how menthanol directly binds to dopamine, a neutrotransmitter that controls the pleasure and pain receptors in the brain. The combined research activities between the Shehu and Kabbani laboratories show that the direct interaction with dopamine allows menthol to kick-up the addictive buzz and thus make it harder for smokers to quit.