Satish S. Nair - US grants

9411866 Nair This project deals with the development of a high-speed integrated control station for carrying out fundamental studies in modeling and intelligent control of nonlinear dynamic systems using neural networks, fuzzy logic, and neuro-fuzzy architectures. The control station is also equipped to interface with real-world hardware systems for performing on-line control studies. The equipment acquired under this grant facilitates real-time implementation and analytical studies such as parameter convergence, tracking control, learning and adaptation issues with unmodeled dynamics, high speed operation, disturbance rejection control and stochastic control. ***

9871288
Pai

The overall objective of the award is to purchase a Polytec PSV-200 Scanning Laser Vibrometer (SLV) and to 1) integrate it into existing research setups of the investigators, 2) strengthen new collaborative research between the PIs, and with other researchers, 3) integrate it into selected undergraduate and graduate courses in the Department of Mechanical and Aerospace Engineering at the University, and 4) make the equipment available to other researchers at the University. An SLV measures velocities of a vibrating surface by measuring the change in frequency of a laser beam reflected from the moving surface. Physically different from the electro-mechanical principles of accelerometers, the SLV offers non-contact measurements, a finer velocity resolution, a wider operating dynamic range, and higher linearity in a fully automated scanning package with ultra-high optical sensitivity.

Name: Satish S. Nair
Institution: University of Missouri-Columbia
Proposal Number: 9979234
Title: Reduced Order Aeroelastic Dynamic Modeling of Flexible Airplanes


Project Abstract:

The objective of this GOALI Graduate Student Industrial Fellowship project is to develop accurate reduced order models for analysis and prediction of both quasi-steady and dynamic loads acting on aircraft. A fundamental theoretical basis for integration of a rigid aircraft simulation that includes a nonlinear aerodynamic database with a flexible aircraft representation that includes an unsteady aerodynamic database is to be developed. Appropriate mathematical techniques will be identified to allow development of a new time domain reduced order 'integrated aeroelastic model'. Next, techniques based on this integrated model will be developed to predict both quasi-steady and dynamic loads for aircraft design. The equations used to predict flight paths of an aircraft, and the loads acting on the aircraft, are inherently nonlinear and complex. Accurate simplification of these equations, incorporating aeroelastic effects, is important to facilitate the development of design tools for predicting flight paths and dynamic loads early in the design cycle for advanced airplanes.

The industrial partner for this project, Boeing, Inc., St. Louis, will provide mentoring at the industry end for the graduate student supported, including access to the structural and aeroelastic models and analytical tools presently used by them. The techniques developed will be useful to develop reliable integrated aircraft simulators, which would shorten the design and development cycle for advanced airplanes.

0244045 Nair

This award provides funding for a three-year Research Experience for Undergraduates (REU) Site at the University of Missouri Colombia for twelve students per year during ten weeks in the summer to focus on research in the area of mathematical modeling and analysis in the biosciences. The REU site is designed as a ten-week summer experience and will involve a multidisciplinary team of faculty from the University of Missouri Colombia College of Engineering, the Institute of Biophysics in the College of Arts & Sciences, the Department of Physiology in the School of Medicine, and the Department of Veterinary Pathobiology in the College of Veterinary Medicine. With rapid advances in biotechnology, the role of engineering and quantitative approaches has grown tremendously in several areas of the biosciences. Quantitative modeling and system theory represent well-defined areas that combine the diverse disciplines of engineering and the biosciences. Even with significant technological advances in several areas of the life sciences, modeling and system curricula are only beginning to be formalized. At this REU Site, students will have a unique opportunity to work with a strong interdisciplinary team of well-established faculty researchers to learn how to explore biosystems through research using quantitative modeling and system theory analysis.

PROPOSAL NO.: 0230593
PRINCIPAL INVESTIGATOR: Nair, Satish
INSTITUTION NAME: University of Missouri-Columbia
TITLE: Engineering Design in Middle and Secondary Math & Science Education

Abstract

There is increasing consensus for the need to incorporate technological literacy into the curriculum of
future teachers. Innovative collaborations are necessary to affect such a change. The Colleges of
Engineering and Education at the University of Missouri-Columbia (MU) will lead a meaningful partnership with the Columbia school district, one rural school, an inner city school in Kansas City, a community college, engineering businesses, and the Missouri Department of Elementary and Secondary Education, to develop a strategy to increase the engineering content, with particular focus on design content, in the curriculum of future mathematics and science teachers in the 7-12 th grades, using hands-on engineering exemplars.
The project will also test the hypothesis that engineering design is an ideal domain for reaching under served groups because it utilizes hands-on activities that students find motivating, and it also shows the connection to jobs.

The proposed project will help to develop infrastructure for deeper and sustained partnerships,
centered on engineering design, among middle and secondary level mathematics and science educators,
higher education, industry, and the department of elementary and secondary education of the state. The
prototype resources developed in the planning grant stage and subsequent larger effort, will be made
available to middle and secondary mathematics and science teachers throughout the region, the state, and
the nation, with the expectation that they will utilize them whenever appropriate. These changes will, in the long term, lead to improved academic performance in mathematics and science, with meaningful engineering design experience, and to an increase in the number of pre-college students who enroll in engineering degree programs.

In this 3-year project, the Colleges of Engineering and Education at the University of Missouri-
Columbia will collaborate with three central Missouri school districts (one small city and two
rural), engineering businesses, and the Missouri Department of Elementary and Secondary
Education to improve the pedagogy and team-building skills of engineering graduate students,
increase the engineering content for G6-9 science and mathematics teachers, and provide
opportunities to increase interest in science and mathematics among G6-9 students using hands-
on engineering design projects.

Intellectual Merit. Engineering and education faculty working together with Fellows and G6-
9 science teachers blends content and pedagogy skills for better overall impact on the Fellows'
development. This is further emphasized by requiring the Fellows to include a chapter about this
outreach activity in their dissertation. Empowerment of the Fellows and development of
leadership skills is included by explicitly involving them in decision making; infusion of
engineering design into K-12 education, a nationally recognized need, is furthered by the project,
using a unique team approach; development of Future Scientists and Engineers Clubs in schools links the proposed activities with curricular ones, enhancing effectiveness and improving sustainability, and; the proposed global connections via distance links adds an important educational dimension to all the activities, for all the participants.

Broader Impact. The proposed project will help develop infrastructure for deeper and sustained
partnerships, centered on engineering design, among G6-9 science and mathematics educators,
higher education, industry, and the department of elementary and secondary education of the
state. The prototype resources (for graduate students, teachers, and G6-9 students) developed will
be made available to graduate students, middle and secondary science and mathematics teachers,
and higher education throughout the region, the state, and the nation, with the expectation that
they will utilize them whenever appropriate. Infusion of engineering design projects should also
facilitate the development of content-rich and inquiry-based approaches. The graduate students
will also benefit from enhanced skills transferable to a variety of occupations. The importance of
actively engaging students in design has the potential not only to enrich teaching of science and
mathematics, but also to forge connections to daily life by providing approaches to meet other
challenges in diverse areas ranging from ecological issues to personal life. The inclusion of women and minorities in all aspects of this program will be stressed.

Partial funding for this project is provided by the Directorate for Engineering.

Life Science Biological (61). An interdisciplinary team of faculty from Colleges of Engineering and of Arts and Sciences are collaborating to develop and teach a new course in computational neuroscience. The intellectual merit is to introduce more quantitative experience to students from biological and behavioral science while exposing students from quantitative sciences to some interesting questions and experimental techniques from the biological sciences. By collaborating, the faculty from both colleges will extend their expertise and be able to involve students with investigations of emerging questions in computational neuroscience. The new course represents a first step for defining a minor in computational neuroscience. For broader impact, a summer workshop is providing opportunities for other faculty to learn about the course so they can increase their own capacity to apply more mathematics within the biology curriculum at other institutions.

PI: Reginald B. Cocroft

Proposal # IOS-0820533

COLLABORATIVE RESEARCH: Novel mechanisms of mate localization in plant-dwelling insects: an integration of behavior, neurobiology and biomechanics.


Plant-feeding insects are among the most abundant and diverse organisms on earth, with a major impact on both natural ecosystems and agriculture. For many of these insects, survival and reproduction depend on the detection of low-amplitude vibrations of the plant surface, generated by the activity of potential mates, competitors, or natural enemies. However, in spite of the importance of plant-borne vibrations for insect behavior and ecology, two fundamental questions remain unanswered. First, how can a small insect determine the direction of a vibration source elsewhere on the same plant? And second, can an insect use the complex motion of plant stems and leaves during vibration transmission to estimate its distance from the source? To answer these questions, an interdisciplinary team has been assembled with expertise in behavior, neurobiology, mechanical engineering, and computational modeling. The team will develop new research tools and use them to understand how insect behavior is guided by mechanical vibrations, using computational models to integrate the results of behavioral experiments, sensory neurophysiology, and biomechanical measurements. Previous research by this team has revealed surprising sources of information that insects may use in sensing; for example, the motion of an insect body, resting on its six legs, can be highly sensitive to the direction of travel of a plant-borne vibration. Results of the proposed research will transform the current understanding of how mechanical vibrations influence the behavior of one of the most ecologically and economically important groups of organisms.
Broader impacts: Because collaborative teamwork is increasingly important for scientific progress, a major goal of the project will be to train graduate students in the skills needed for a career in interdisciplinary research. Finally, insights gained during the study could lead to the design of directional vibration sensors that could have an impact on many industries.

DESCRIPTION (provided by applicant): The overall objective of the proposed cross-disciplinary research is to use an integrated computational/experimental approach to study the acquisition and extinction of conditioned fear associations in the neural components of the fear circuit of mammals. We propose an interdependent series of experiments and biologically realistic simulations, using a 'from biology to model, to predictions, and back to biology'theme where experiments will constrain the design of the models ('from biology to model') and discrepancies between the models and expected outcomes will lead to the formulation of hypotheses ('to predictions') that will be tested experimentally ('back to biology'). The computational models will be developed using experimental data from laboratories of two neuroscience Co-PIs. Preliminary models, developed by our group over a period of 21/2 years demonstrate that they can provide significant insights into the intrinsic and synaptic mechanisms associated with learning and neuroplasticity in conditioned fear. The proposed research will expand this collaboration with the following specific aims: 1.To investigate the underlying mechanisms of learning and neuroplasticity in the amygdala related to the acquisition and extinction of conditioned fear using a biologically realistic computational model, and to test model predictions in experiments. From biology to model: Use published biology data (in vitro and in vivo), to investigate neurocomputational properties of single cell models of amygdala nuclei including lateral amygdala (LA), basal amygdala (BA), intercalated cells (ITC), and central nucleus (CeM and CeL). From biology to model and to predictions: Investigate how the key amygdala nuclei interact to acquire and extinguish conditioned fear memories using a biologically realistic network model that includes the single cell models. Make predictions to quantify the relative contributions of the various projections from LA to CeM, and about other mechanisms. From predictions to biology (and back): Assess the effects of fear conditioning and extinction on synaptic responses in the projections from LA to CeL, and CeL to CeM, in an in vitro slice preparation (to be performed in the Par[unreadable] lab). Incorporate findings from experiments and refine the model. 2. To investigate the mechanisms involved in the regulation of amygdala-dependent conditioning and extinction fear memory by the ventro medial prefrontal cortex, using a biologically realistic computational model, and to test model predictions in experiments. From biology to model: Use published biology data (in vitro and in vivo), to investigate the neurocomputational properties of single cells and networks in the pre-limbic (PL) and infra-limbic (IL) regions of the ventral medial prefrontal cortex (vmPFC). From biology to model and to predictions: Determine how the vmPFC regulates amygdala-dependent fear and extinction memories by developing an overall biologically realistic model including the vmPFC and the amygdala (from specific aim 1). Make predictions about the possible connections between vmPFC and the amygdala that may regulate these memories, and the effect of vmPFC inactivation on the tone responses of BA and Ce neurons. From predictions to biology (and back): Assess the effects of vmPFC inactivation on tone responses of BA and Ce neurons during fear conditioning and extinction (to be performed in Quirk lab). Incorporate findings from experiments and refine the model of vmPFC regulation of the amygdala in a single context. Intellectual Merit. The proposed interdisciplinary research will be the first to develop a biologically realistic computational model of the fear circuit. It will facilitate discovery of the learning and neuroplasticity mechanisms that underlie acquisition and extinction of conditioned fear in mammals, and will lead to valuable predictions, and novel directions for experimental research. The approach proposed will also lead to a better understanding of the systems and design principles governing the fear circuit. Broader Impact. The proposed computational model will provide new insights and understanding of a spectrum of psychiatric disorders including PTSD and anxiety disorders, which are thought to arise from deficits in the fear circuit. It will also be a key tool for the development of novel agents and strategies for the treatment of such disorders. Finally, the collaboration will also contribute to the generation of new curricula and materials for undergraduate, graduate and medical student education, and for K-12students.

This REU Site award to the University of Missouri (MU), located in Columbia, MO, will support the training of 10 students for 10 weeks during the summers of 2017- 2019. MU is the largest institution of higher learning in Missouri and the flagship campus of the University of Missouri System. The program offers a wide range of very exciting interdisciplinary projects in neuroscience, with both wet-lab and computational components. The projects are drawn from all levels in neuroscience (i.e., intracellular/cellular, systems and behavioral levels), with participating faculty coming from the colleges of arts & science, engineering, and veterinary medicine. Starting with a 1-week boot camp, students attend a weekly class on computational neuroscience, conduct lab research as well as participate in various seminars and workshops on topics such as the responsible conduct of research, professional skills, career opportunities, and the graduate school application process. Each student will have the opportunity to write reports, and design and present a scientific poster. They will form part of a group of 100+ undergraduate researchers from all over U.S. who participate each summer in a vibrant summer research program (http://undergradresearch.missouri.edu/). All participants will be recruited from outside MU, and selected based on academic record, research performance, and potential for research in neuroscience. Participant support during the REU experience will include a stipend, lodging and food, travel to and from the university, and funding for the research project.

It is anticipated that a total of 30 students, primarily from schools with limited research opportunities, will be trained in the program. Applications from underrepresented groups and minority institutions are encouraged.

A common web-based assessment tool used by all REU Site programs funded by the Division of Biological Infrastructure will be used to determine the effectiveness of the training program. Students will be tracked after the program in order to determine their career paths. Students will be asked to respond to an automatic email sent via the NSF reporting system. More information about the program is available by visiting
http://engineering.missouri.edu/neuroreu/, or by contacting the PI (Dr. Satish S. Nair at nairs@missouri.edu) or co-PI (Dr. David J. Schulz at schulzd@missouri.edu).

This Improving Undergraduate STEM Education (IUSE) project from the University of Missouri - Columbia addresses the need for professional development for biological sciences faculty in the area of computational neuroscience. Computational neuroscience is an emerging area that studies brain function in terms of the information processing properties of the structures that make up the nervous system. It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science, and psychology with electrical engineering, computer science, mathematics, and physics. While leading biology educators call for strong interdisciplinary curricula that include physical science, information technology, and mathematics, many biological and behavioral scientists lack adequate training in the quantitative sciences, limiting their understanding and use of tools from this area. Similarly, engineers and quantitative scientists lack the training in biological sciences and neuroscience necessary to understand the details of the diverse systems in biology, and facilitate improved interactions with biologists to develop relevant and advanced computational tools.

The PI team is building on the knowledge they have gained through their prior NSF-funded work in the development of a computational neuroscience course, as well as on their seven years of experience providing a professional development workshop entitled "Hardware and Software Experiments to Teach Undergraduate Neuroscience." The workshops offered through this project are enhancing the teaching expertise of faculty in neuroscience at both 2- and 4-year institutions. Faculty-student teams participate in one-week workshops that include instructional methodologies emphasizing computation via free software experiments. Bringing this expertise back to their home institutions, workshop participants are increasing the number and diversity of undergraduates studying computational neuroscience. In addition, an intensive two-week curriculum development course will be offered to four faculty-student teams. These rigorous workshops provide training in mathematics, software, teaching, and development of curricular modules in computational neuroscience and include a one-year long support program for faculty-student teams as they develop and implement software modules into their curriculum. In the course of the project, the PI team is also augmenting our knowledge of obstacles to the implementation of effective instructional practices by carrying out studies to identify barriers to student learning and to faculty professional development and implementation of effective teaching practices. Understanding these impediments will facilitate development of strategies to overcome them so that effective instructional practices can be more widely adopted in the nation's institution of higher education.

? DESCRIPTION (provided by applicant): Truly integrative and interdisciplinary training in neuroscience is necessary to understand brain function in both normal and pathological states. And such training is not available presently at the pre- and post-doctoral and junior faculty level due to a multitude of reasons. We propose an integrated approach to train the next generation of `neuro' research scientists from several disciplines including biology, psychology, medicine, engineering, physics and mathematics. Specifically, we will build on past successes with training in interdisciplinary neuroscience, to enhance the research expertise of graduate students, post-doctoral scholars and junior faculty in the growing area of computational neuroscience. This enhancement will be achieved via a 2-week short course, with 24 participants/year, held on the University of Missouri campus in Columbia. The course will begin with an in-depth emphasis on neurophysiological concepts via free software (virtual lab) experiments and some wet-lab experiments using a from biology to model and back again approach. It will then provide training in hands-on software development both at the individual (exercises) and two-person group (projects) levels using the software package NEURON to model single neurons and circuits of neurons. We will place importance on the development of individual computational research projects by participants, to enhance their confidence and ability to integrate such tools into their research careers. Since neuroscience concepts and software development tend to be complex, we will provide follow up to participants for one year on all aspects of the course, and in the process also identify barriers to research training in thi new interdisciplinary area. For interested faculty participants, we are willing to visit their institutions to foster interaction across disciplines, research programs, and institutions, in computational approaches. Our experience and findings will be published in science and engineering education journals and presented at appropriate conferences. Our team includes four interdisciplinary faculty, three with expertise in content (1 computational + 2 neuroscientists) and one in pedagogy and evaluation. Two of the faculty (Nair and Schulz) have been collaborating in research in and teaching interdisciplinary neuroscience for the past 8 years. Over that period they have also been hosting annual summer workshops for 2 and 4-year college faculty with focus on teaching undergraduate neuroscience. Our experience and findings will be published in science and engineering education journals and presented at appropriate conferences.

This project develops advanced cyber-physical sensing, modeling, control, and optimization methods to significantly improve the efficiency of algal biomass production using membrane bioreactor technologies for waste water processing and algal biofuel. Currently, many wastewater treatment plants are discharging treated wastewater containing significant amounts of nutrients, such as nitrogen, ammonium, and phosphate ions, directly into the water system, posing significant threats to the environment. Large-scale algae production represents one of the most promising and attractive solutions for simultaneous wastewater treatment and biofuel production. The critical bottleneck is low algae productivity and high biofuel production cost.

The previous work of this research team has successfully developed an algae membrane bioreactor (A-MBR) technology for high-density algae production which doubles the productivity in an indoor bench-scale environment. The goal of this project is to explore advanced cyber-physical sensing, modeling, control, and optimization methods and co-design of the A-MBR system to bring the new algae production technology into the field. The specific goal is to increase the algal biomass productivity in current practice by three times in the field environment while minimizing land, capital, and operating costs. Specifically, the project will (1) adapt the A-MBR design to address unique new challenges for algae cultivation in field environments, (2) develop a multi-modality sensor network for real-time in-situ monitoring of key environmental variables for algae growth, (3) develop data-driven knowledge-based kinetic models for algae growth and automated methods for model calibration and verification using the real-time sensor network data, and (4) deploy the proposed CPS system and technologies in the field for performance evaluations and demonstrate its potentials.

This project will demonstrate a new pathway toward green and sustainable algae cultivation and biofuel production using wastewater, addressing two important challenging issues faced by our nation and the world: wastewater treatment and renewable energy. It will provide unique and exciting opportunities for mentoring graduate students with interdisciplinary training opportunities, involving K-12 students, women and minority students. With web-based access and control, this project will convert the bench-scale and pilot scale algae cultivation systems into an exciting interactive online learning platform to educate undergraduate and high-school students about cyber-physical system design, process control, and renewable biofuel production.

This project will develop cyberinfrastructure-based training modules that advance the existing training methods used for learning and research in data-intensive neuroscience communities. The project outcomes will enhance research into our understanding of both normal and abnormal brains, contributing to NSF's mission of advancing progress in both science and health. The project activities will address important gaps in existing training methods that arise because neuroscience research and education activities are increasingly becoming data-intensive. There is a growing need to integrate and analyze voluminous data being generated at multiple levels to explore the functioning of normal and abnormal brains. Consequently, research and training in the area now necessitates access to distributed resources, including multiple software packages, high-performance computing with large numbers of cores, virtual desktops with data sharing/collaboration capabilities, neuro-data archives, and also requires multi-disciplinary expertise (e.g., engineering, biology, psychology). Computational neuroscience researchers, undergraduate and graduate students and teachers (three targeted communities in this project) face challenges in accessing such resources and expertise in a scalable and extensive manner. Further, they lack the necessary training in the use of advanced cyberinfrastructure (CI) technologies and distributed resources to improve their scientific productivity and to pursue large-scale data-enabled investigations.

The transformative nature of project's training modules is in the "self-service" nature planned for the modules that make them accessible to neuroscience users in an "on-demand" and "personalized" manner. The training modules development will be based on survey of training needs, and will be focused on having students/teachers/multi-disciplinary researchers use, apply and create hands-on laboratory exercises and tools that can be deployed locally (i.e., within institutional CI) and be supplemented with publicly accessible national resources such as the NSF-funded Neuroscience Gateway (NSG). The training modules will considerably enhance existing traditional neuroscience courses covering foundational concepts at undergraduate, graduate and teacher-training levels with hands-on laboratory exercises related to managing scientific workflows, CI middleware and application programming interfaces (APIs) to integrate geographically distributed resources. The proposed activities will leverage existing active training programs in cloud computing and in neuroscience, and will use NSF-supported advanced CI resources that are available locally at University of Missouri and at NSG. Project outcomes will be integrated into on-going courses (with its 50+ neuroscience faculty spanning 10 departments, and 5 colleges), into on-going NSF and NIH summer training programs, which recruit diverse participants including under-served and under-represented students, and into an on-going K-12 outreach program in neuro-robotics. The summer trainees that are being recruited in this project include over 50 students, neuroscience faculty and cyberinfrastructure engineers interested in advanced cyberinfrastructure capabilities for diverse research and education efforts. In addition, over 80 students will benefit from the training modules within formal classroom courses in existing neuroscience and cyberinfrastructure courses at the University of Missouri, and over 150 students will benefit from outreach activities that include webinars and tutorials at conferences.

Throughout the brain, specialized systems carry out different but complementary functions, sometimes independently but often in cooperation. However, we do not understand how their activity is dynamically coordinated, and dysregulation of this is associated with many mental health conditions. Neuronal oscillations, which are detectable in local field potentials (LFPs) at various frequencies, are a promising target for this coordination. Gamma oscillations (40-100 Hz) in particular have been singled out since they enhance stimulus responses, facilitate interactions between brain regions, and are expressed ubiquitously across cortical and subcortical regions. Indeed, gamma oscillations occur in the basolateral nucleus of the amygdala (BL), an important regulator of emotional behaviors. BL gamma oscillations are enhanced during periods of heightened vigilance during a foraging task, following emotionally salient experiences, and upon presentation of socially-relevant stimuli. The variety of circumstances that engage it make it a promising target for interventions affecting emotional behaviors in general. However, technical challenges abound because gamma manifests as brief intermittent oscillatory bursts, layered atop numerous ongoing activities in other frequency bands. This precludes manipulating gamma exclusively with traditional pharmacological, optogenetic, or chemogenetic approaches, since these have substantial effects on ongoing non-gamma activities, and are delivered irrespective of whether gamma bursts are present or absent. To overcome this, a closed-loop algorithm was developed that monitors the LFP in real-time for gamma oscillations and delivers precisely timed optogenetic stimulation capable of enhancing or suppressing gamma strength on a cycle-by-cycle basis. While this improves upon the status quo,, further refinement is needed. Aim 1 of this proposal seeks to clarify how the gamma modulation technique operates via biophysically detailed modeling of the local circuits in the BL that generate gamma, the effects of optogenetic stimulation, and the closed-loop algorithm. Aim 2 designs better signal processing routines for detecting and parameterizing gamma in real-time. Aim 3 develops an approach to create customized biophysical models that reproduce the properties of gamma observed in individual subjects, which when combined with the results of Aims 1 and 2 should allow for optimized control over gamma oscillations in individual subjects. RELEVANCE (See instructions): Gamma oscillations occur in the basolateral amygdala, a brain region implicated in emotional regulation. By developing improved methods to manipulate these oscillations, we hope to better understand their function and improve our ability to control emotional states and behaviors.

By designing a chatbot that harnesses the power of machine learning to connect scientists/educators with existing tools, datasets, and other experts, this sociotechnical project aims to remove significant user support barriers in the US STEM community. A chatbot called Vidura will be developed to provide personalized user support to novice and expert users in the form of a conversational agent to facilitate interdisciplinary research and education collaborations. The chatbot investigations in the project benefit the investments in science gateways (SG) and cyberinfrastructure (CI) made by the NSF and other federal agencies for over two decades. Vidura chatbot works around the clock and makes the greatest impacts when user support by human agents (i.e., domain specialists or CI support persons) is not available and/or is too costly as a science gateway surges in user uptake. The Vidura chatbot will initially be prototyped in CyNeuro, a neuroscience SG, but will be made accessible to be adapted in SGs across multiple domains to benefit the broader research/education communities.

This project integrates human communication science, conversational agent design, recommender algorithms, machine learning techniques, domain topic modeling in a synergistic way that advances social science, computer science, and neuroscience. In addition, this sociotechnical project provides research opportunities to benefit undergraduate and graduate students in both social and computer sciences and creates new interdisciplinary courses. The project activities will benefit students with diverse backgrounds as it will be carried out at two large public universities, one of which is a Hispanic serving institution. The project objectives and activities will focus on answering three main research questions: (i) How to design a chatbot for gathering user requirements and creating user profiles with proficiency in order to provide personalized expert service support and maintain adoption? (ii) How to equip the chatbot communication and custom dialogue flows during support actions with underlying recommender algorithms using un-supervised machine learning within/across science domains? (iii) How to implement a chatbot framework in research and education workflows of data-intensive/computation-intensive application communities (such as the neuroscience community, as well as in CI provider communities of SGCI, XSEDE, NSG, CyVerse, and JetStream) to evaluate utility across domains and identify best practices for ongoing relevance? The project findings will ultimately advance knowledge on how to enable conversational agents implemented as chatbot interfaces with pertinent underlying recommender algorithms in order to provide expert services to scientific domain users with reduced cost and increased convenience.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.