Project title: Enabling Real-time, Scalable and Secure Collaborative Intelligence on the Edge

Investigators: Zheng Dong, Weisong Shi

Abstract: With the proliferation of embedded systems, multicore computing devices enable the recent trend of moving computation from the centralized cloud to distributed edge platforms. This trend yields new products and services across smart infrastructures in smart cities. However, as real-time workloads are executed at the edge computing platforms, the performance bottleneck is transferred from the edge-cloud communication to on-chip communication. The system’s real-time performance faces new system-architectural challenges for the Network-On-Chip (NoC), which are scalability and security. These challenges are hinged with dynamic data distributions across different users. This project aims to design a real-time and scalable NoC for implementing real-time collaborative learning algorithms. The key strategy is to orchestrate a system-architecture and algorithm co-design to explore the new design space on the edge computing platform.

To cope with the research challenges, a comprehensive architecture will be developed to address these multifaceted problems through a hardware and software co-design, which consists of three key thrusts: (i) designing an interconnect, which will eliminate non-predictability barrier on the NoC; (ii) establishing a scalable virtualized transaction environment for the collaborative learning system to guarantee that all the real-time transaction tasks can complete at the right time; (iii) implementing a real-time and secure multi-target tracking system on the edge platform in light of the newly proposed architecture. The proposed research will be evaluated using the physical platform Equinox, with indoor and outdoor studies beyond simulation.

This research will open a new dimension of research and educational opportunities. In particular, the success of the project will provide a hardware/software package that can enhance the real-time collaborative computing on the edge. The resulted interconnect and Equinox are ready-to-use platforms that will allow experts/researchers to easily examine their research designs regarding collaborative learning and real-time edge computing, thereby sealing the gap between different research fields. Educational efforts will be devoted to (i) curriculum design for the undergraduate and graduate program, (ii) summer camp development for middle and high school students, and teachers, (iii) broadening participation in computing and engineering, at the Wayne State University.

Project title: Neural Conversational Agent for Automated Weight Loss Counseling

Investigators: Alexander Kotov, April Idalski Carcone, and Elizabeth Towner

Abstract: Obesity is one of the most important medical and public health problems in the United States. According to recent nationally representative studies, every third adult in the U.S. is obese. Motivational Interviewing (MI), a client-centered and directive approach to behavior change counseling, has been widely adapted for treating obesity. Despite the evidence presented in published meta-reviews that suggests that MI is effective at activating behavioral changes, anthropometric changes are less significant. At the same time, there are several access barriers to this type of behavioral health care, such as shortage of human counselors in certain geographical areas, long wait times, cost, and fear of judgment. Recent advances in deep learning have allowed artificial intelligence (AI) methods to expand into the areas of health care that were previously thought to be the exclusive province of human experts, such as clinical diagnostics.

Behavioral health and MI, however, are the areas of medicine that have not yet substantially benefitted from modern AI technologies, such as neural conversational agents. To address this limitation, the proposed project aims to test the feasibility and usability of using neural conversational agents for automated behavioral counseling with a focus on weight loss. Specifically, we build on recent advances in deep learning, such as conversational agents, neural attention, transformers, supervised policy learning, variational autoencoders and adversarial training, and aim to develop and validate Neural Agent for Obesity Motivational Interviewing (NAOMI), a mobile device (smartphone or tablet) application to conduct automated MI counseling focused on weight loss. NAOMI is based on a novel neural architecture, which consists of neural networks that can be independently and collectively trained using the proposed multi-stage procedure to learn communication behaviors, which should be strategically utilized during different stages of an MI counseling session depending on the observed interactions and generate responses that are grounded in session context and reflect patient’s language.

We will recruit 40 obese adults, who will interact with NAOMI and provide their feedback through semi-structured qualitative interviews. We plan on conducting at most 4 iterative development cycles of NAOMI with 10 patients participating in each cycle. We will conduct a mixed-methods sub-study after each development cycle. Quantitative evaluation of NAOMI’s MI counseling skills will be conducted based on the transcripts of participants’ interactions by a coder trained in using the MI Treatment Integrity (MITI) coding system, a standard instrument for assessing MI fidelity. Qualitative interviews with the participants will be analyzed using Framework Matrix Analysis. The methods and techniques proposed in this project can be adapted to other types of psychotherapeutic interventions besides MI and to other conditions besides obesity.

Project title: Elements: MVP: Open-Source AI-Powered MicroVessel Processor for Next-Generation Vascular Imaging Data

Investigators: Zichun Zhong, Jing Hua

Abstract: Effectively acquiring and processing complicated microstructures and their morphology become increasingly important in scientific and engineering discoveries. A growing body of evidence in animal and human studies shows that micro-cerebrovascular abnormalities are the real source of many neurologic disorders and vascular diseases. Therefore, there is an urgent need for better detection and understanding of vascular characteristics in vivo at the micro-level. However, the existing techniques and software solutions are developed based on general-scale medical images for processing macro-level organs and tissues, not applicable to the challenging next-generation vascular imaging data and micro-level vasculature. Furthermore, they are difficult to be customized to specific scientific microvascular imaging applications for domain developers. The overall objective of this project is to design and develop a rigorous, scalable, intelligent, and comprehensive data and software infrastructure, MicroVessel Processor (MVP), which supports and sustains advancements of understanding and analyzing the complicated 3D microvascular networks, provides services to a large community, and fosters continuous innovation in the multidisciplinary domains.
This project integrates new paradigm of data-driven 3D microvascular discovery - generate new knowledge and understanding and accelerate discovery and innovation via the advanced software cyberinfrastructure, along with use-case driven functional applications and evaluations. The capabilities of the newly developed AI-based high-fidelity 3D image enhancement, 3D geometric segmentation / extraction, reconstruction, and analytics techniques are integrated in the MVP system as the core modules in the software infrastructure. The MVP database including a large number of subjects with varying anatomical shapes, morphological features, and different data modalities is collected and constructed as the infrastructure (i.e., knowledgebase), which provides the knowledge for 3D microvascular networks tracking and analysis. The MVP system is designed and built based on an open-source and drag-drop real-time application system, in order to reduce both human labors and computation costs, accelerate the speed and efficiency of re-development and extensibility. This unique framework supports a wide adoption in microvasculature analytics in biomedical engineering, big-data neuroscience, and AI-based clinical diagnosis.