2024
Hadeel R A Elyazori
2024-2026
Project Title: Artificial Intelligence and Motivational Interviewing for Pain Management and Opioid Use Reduction
Chronic pain affects 20% of adults and is a leading cause of disability, significantly impacting healthcare costs and workforce productivity. This project addresses the critical need for improved diagnostic tools and treatment strategies for chronic pain by integrating patient narratives with Natural Language Processing (NLP) and generative Artificial Intelligence (AI). It comprises three tasks: developing a Motivational Interviewer system that leverages Large Language Models (LLMs) to embody MI techniques within a structured conversational framework. Secondly, creating a Patient Simulator that generates realistic patient interactions by combining linguistic, medical, and behavioral profiles to engage in synthetic MI sessions for comprehensive testing. Lastly, integrating and refining the Motivational Interviewer and Patient Simulator through Reinforcement Learning from Human Feedback (RLHF) and AI Feedback (RLAIF) ensures effective and empathetic therapeutic dialogues. This project democratizes access to advanced pain management techniques, promoting equitable healthcare. By merging clinical practice, AI, and psychology, we advance both AI technologies and patient outcomes, addressing health disparities and improving engagement in chronic pain management.
Team of faculty mentors:
Kevin Lybarger, Assistant Professor, Department of IST College of Engineering and Computing
Siddhartha Sikdar, Professor Department of Bioengineering College of Engineering and Computing
Samuel Acuña, Assistant Professor, Department of Bioengineering College of Engineering and Computing
Lynn Gerber, Director for Research Medicine Service Line, Inova Health System
Learn more about Hadeel's research:
Md. Ridwan Hossain Talukder
2024-2027
Project Title: Anticipatory Task and Motion Planning for Service Robots in Large-Scale Home Environments
The increasing demand for assistive technologies underscores the importance of advanced service robots in households. Existing mobile robots face significant challenges in these settings. Due to their inability to predict the long-term side effects of their immediate action, these robots execute tasks that seem optimal in the short term but are costly for subsequent tasks, leading to inefficient performance.
Our research addresses these issues by developing a mobile robot with anticipatory planning capabilities that enable robots to prepare for future tasks. By integrating task and motion planning with learning, we aim to achieve scalability and efficiency. Incorporating Large Language Models (LLMs) will enhance human-robot interaction, allowing robots to adapt to user instructions and preferences. These refinements will create more autonomous and efficient service robots exhibiting common-sense-like behaviors, improving the daily lives of individuals requiring assistance and pushing the boundaries of domestic robotics toward more proactive and self-sufficient technologies.
Team of faculty mentors:
Gregory Stein, Department of Computer Science, College of Engineering and Computing
Xuesu Xiao, Department of Computer Science, College of Engineering and Computing
Ziyu Yao, Department of Computer Science, College of Engineering and Computing
Elizabeth Phillips, Department of Psychology, College of Humanities and Social Sciences
Learn more about Ridwan's research:
Lauren Dennedy
2024-2025
Project Title: Real Time Crowd Analysis from Video Streams Utilizing Computational Crowd Dynamics and Behavioral Psychology Infused Machine Learning
Crowd analysis is a field that seeks to understand the behavior and movement patterns of crowds, which is crucial to innovating new techniques to mitigate risks from movement activities of high density population areas. The aim of this research is to develop and train machine learning models to predict and minimize potential crowd dynamics to reduce the risk of injury to individuals within crowds from video streaming data, with interdisciplinary approaches to modeling crowd movement with physics based computational crowd dynamics simulations and leveraging human factors from behavioral psychology. The technical foundation for the research is accomplished with a machine learning methodology that incorporates simulated data and video data to develop new physical laws to be encoded in a crowd model. Experimentation with this model aims to provide new insights on the behaviors of pedestrian crowds, which can be utilized to design safer spaces for urban planning and event coordination
Team of faculty mentors:
Daniel Barbará, Professor, Department of Computer Science, College of Computing and Engineering
Rainald Löhner, Professor, Department of Physics and Astronomy, College of Science
Gerald Matthews, Professor, Department of Psychology, College of Humanities and Social Sciences
Learn more about Lauren's research:
2023
Alonso Gabriel Ogueda
2023-2025
Project Title: A New Data-Driven Machine Learning Framework to Predict Dynamics of Infectious Diseases Incorporating Human Behavior in Epidemiological Models
We propose a novel methodology to minimize unintended outcomes of public health interventions in response to pandemics by integrating research from social and/or behavioral sciences along with efficient data-driven predictive analytics to enhance mathematical epidemiological models.
This project proposes a new framework modeling infectious diseases with Agent-Based Modeling incorporating human behavior (for example, isolation geo-data) combined with compartmental models and efficient predictions using Physics-Informed Neural Networks algorithms. There are two fundamental aspects, Modeling and Simulation and Parameter Identification and Data-driven Decision Making. The ability of simulating different scenarios of human behaviors, such as mobility, policies and decisions which will all be considered, gives the flexibility of study a range of possibilities. To evaluate the predictive capability of our methodology, we hope to validate the framework against the available limited real data.
Team of faculty mentors:
Dr. Padmanabhan Seshaiyer, Professor, Mathematical Sciences Department, College of Science
Dr. Brian Levy, Assistant Professor, Sociology and Anthropology Department, College of Humanities and Social Sciences
Dr. Taylor Anderson, Assistant Professor, Geography & Geoinformation Science Department, College of Science
Learn more about Alonso's research:
Dibyendu Das
2023-2026
Project Title: Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning
Technological advances are enabling global market shifts from industrial robots to service robots. Traditional robotics relies on repetitive planning every time a new setting is encountered, regardless of its similarity to past environments. This is computationally expensive and limits robots’ performance. This research aims to address these limitations by: (1) Using machine learning techniques to recall past planning experiences, thereby reducing the need for replanning in similar situations; and (2) Designing advanced motion planners that benefit from past experiences. This approach will reduce robots’ computational needs, making them cost-effective and efficient for wider deployment in real-world environments. This project has societal implications: facilitating an ecosystem where robots are accessible to diverse groups, including the disabled and elderly, for whom efficient robotic assistance can be life changing. Additionally, the exposure of such technology in everyday life, outside of restrictive industrial or academic settings, provides educational opportunities, fostering interest in robotics and STEM for underrepresented communities.
Team of faculty mentors:
Dr. Xuesu Xiao, Assistant Professor, Department of Computer Science, School of Computing, College of Engineering and Computing
Dr. Xuan Wang, Assistant Professor, Department of Electrical and Computer Engineering, Volgenau School of Engineering, College of Engineering and Computing
Dr. Daigo Shishika, Assistant Professor, Department of Electrical and Computer Engineering, Volgenau School of Engineering, College of Engineering and Computing
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Erika P De Los Santos
2023-2025
Project Title: Human-AI Cooperative System to Detect Automated Deepfake Deception
A pervasive threat to cybersecurity is deepfakes, which convincingly mimic real images, speech, and text. Tools such as Artificial Intelligence (AI) and Machine Learning (ML) can be used to support systems for deepfake detection. However, AI and ML on their own have significant limitations. Our solution is to design platforms for AI-human collaboration that leverage and synthesize the strengths of both entities to optimize deepfake detection. Our goal is to develop a framework that encompasses the benefits of AI/ML detection with significant consideration of human cognition (e.g., biases, vulnerabilities, and trust). Ultimately, we hope to promote inclusivity by designing cybersecurity training that is generalizable across diverse groups.
Team of faculty mentors:
Dr. Daniel Barbará, Professor, Department of Computer Science, School of Computing, College of Engineering and Computing
Dr. Gerald Matthews, Department of Psychology, College of Humanities and Social Sciences
Dr. Giuseppe Ateniese, Professor, Department of Computer Science, School of Computing, College of Engineering and Computing
Dr. Tyler Shaw, Department of Psychology, College of Humanities and Social Sciences
Learn more about Erika's research:
2022
Jinyi Kim
2022-2025
Project Title: A Multi-level Approach to Measuring Risks for Positive Youth Outcomes
Youth involved with the juvenile justice system tend to show higher rates of adverse childhood experiences, increased health needs, and learning disabilities. Many of these already vulnerable youth have received interventions through other systems (e.g., schools, mental health), which have been ineffective. We move beyond conventional approaches to studying youth risk, which are limited by (1) a narrow focus on juvenile-justice samples and delinquency outcomes and (2) a lack of attention to community-level factors. The present study will increase the use of youth-oriented research by moving beyond questions of “what works” to developing knowledge of “how to use” existing knowledge. Specifically, the proposed research will extend the Positive Youth Development (PYD) Framework by theorizing and developing a standardized measure of community-level risk factors (e.g., service deserts) through computational approaches. The proposed framework and measurements will inform youth-related policymakers and practitioners in designing and assessing multi-level interventions to promote youth’s development.
Team of faculty mentors:
Myeong Lee, Assistant Professor, Department of Information Sciences and Technology, School of Computing, College of Engineering and Computing
JoAnn Lee, Associate Professor, Department of Social Work, College of Health and Human Services
Hamdi Kavak, Assistant Professor, Department of Computational and Data Sciences, College of Science
Learn more about Jinyi's research:
Soelem Aafnan Bhuiyan
2022-2025
Project Title: Developing a Data Assimilation Technique to Improve the Monitoring and Forecasting of Coastal Hazards
Hurricanes, cyclones, and typhoons are major global natural hazards causing frequent and recurrent devastation worldwide. Providing accurate and reliable water levels and flood inundation extents to inform early warning systems remains a challenge at the operational level. The NASA Surface Water and Ocean Topography (SWOT) mission (planned to be launched in November 2022) and its observations of sea water levels have the potential to fundamentally transform our ability to predict flooding from tropical storms.
This IDIA pre-doctoral fellowship will allow me to test the hypothesis that, by assimilating SWOT products within a storm surge model, we can improve the estimation of storm surge process dynamics and operational forecasting. This project will advance our understanding of such processes to build resilience of our coastal infrastructure and economy, therefore, promoting security, prosperity and the well-being of coastal populations.
Team of faculty mentors:
Viviana Maggioni, Associate Professor, Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, School of Engineering, College of Engineering and Computing
Celso Ferreira, Associate Professor, Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering, School of Engineering, College of Engineering and Computing
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Facundo Nicolas Airaudo
2022-2025
Project Title: Optimal HVAC Outlay to Minimize Pathogen Transmission
The experiments and simulations carried out before and during the COVID-19 pandemic have made it clear that current Heating, Ventilation, and Air Conditioning (HVAC) systems can lead to very unfavorable airflows, resulting in increased pathogen transmission. This project aims to minimize the amount of viral matter inhaled by human beings in built environments. This will be carried out by modeling and solving optimization problems within the physical constraints of the equations describing fluid dynamics, temperature, and particle transport. The control parameters are viral matter source, location of vents, inflow velocities, etc. The project can potentially help update the CDC guidelines on how to keep people safe in built environments, not just from COVID but other airborne pathogens as well. It will also help provide optimal outlays for HVAC vents with minimal pathogen transmission. The ideas generated under this project are equally applicable to airplanes, hospitals, stores, courtrooms, classrooms, etc.
Team of faculty mentors:
Rainald Lohner, Professor, Department of Physics and Astronomy, College of Science
Harbir Antil, Professor, Department of Mathematical Sciences, College of Science
Juan Raul Cebral, Professor, Department of Bioengineering, School of Engineering, College of Engineering and Computing
Learn more about Facundo’s research:
Michael Timothy Crawshaw
2022-2025
Project Title: Multiple Guarantees in Distributed Deep Learning: Computation, Communication, and Sample Efficient Algorithms
The goal of our research is to develop distributed deep learning algorithms with theoretical guarantees and deploy these algorithms for diverse applications. Deep learning is a powerful area of machine learning with two barriers for real-world deployment: high resource demand and lack of theoretical guarantees. We aim to address these weaknesses by developing the theory and practice of distributed deep learning, that is, deep learning methods which utilize computational power and data from many communicating devices. Distributed learning can ease the resource requirements of deep learning by eliminating the need for an expensive, monolithic training environment, lowering the barrier to entry. Also, by developing distributed learning algorithms with provable guarantees of computation, communication, and sample efficiency, we aim to enable confident real-world deployment of deep learning models. Our work will enable easier access to deep learning while increasing its real-world scope and energy demands, with the ultimate goal of improving societal well-being.
Team of faculty mentors:
Mingrui Liu, Assistant Professor, Department of Computer Science, School of Computing, College of Engineering and Computing
Wanli Qiao, Assistant Professor, Department of Statistics, School of Computing, College of Engineering and Computing
Ozlem Uzuner, Associate Professor, Department of Information Sciences and Technology, School of Computing, College of Engineering and Computing
