Students Partnering with Faculty Awards 2025

Environmental Health Risks and Vulnerabilities in New Jersey’s Carceral Facilities  
Galia Shokry, Environmental and Sustainability Sciences, CSMT

This project investigates the intersection of environmental health risks and systemic inequities within New Jersey’s carceral facilities. Prisons often operate in environmentally hazardous conditions, exposing incarcerated individuals and staff to contaminants, extreme heat, poor air and water quality, and outdated infrastructure. In New Jersey, approximately one-third of state and federal prisons are located within one mile of contaminated sites. 

The project’s primary goal is to document and analyze the environmental health vulnerabilities that incarcerated populations face, focusing on two New Jersey facilities as case studies. The research will assess the short- and long-term health risks associated with extreme temperature exposure and contaminated water sources. It will examine how environmental injustices within carceral facilities exacerbate broader systemic inequalities, contributing to emerging research on toxic prisons and prison ecology. 

Through qualitative interviews with recently returned inmates and family members, geospatial mapping of environmental hazards, and water quality analysis, the project will create an intersectional understanding of the relationship between environmental risks, climate change impacts, and carceral health. The findings will inform public health advocacy and ongoing legislative and prisoner community awareness efforts. 

Sydnie Bogan, a master’s student in Criminal Justice and Public Administration, will serve as the primary student researcher. As my GRA, Sydnie has already made significant progress on this research, including identifying key environmental hazards affecting prison facilities, collecting geospatial data, and launching a public awareness campaign through Clean Water Action. She has also organized a series of events featuring testimonies from formerly incarcerated individuals about the impacts of contaminated water and extreme heat conditions. Sydnie has begun drafting a commentary piece on the subject for submission to Health & Place or The Lancet Public Health and is working on an empirical manuscript for submission to Environmental Justice or the International Journal of Environmental Research and Public Health. 

This summer, Sydnie will collaborate with Karina Ponze, an undergraduate student in Computer Science and Technology, who will bring additional technical knowledge in GIS mapping and data visualization. Karina has already been receiving training in GIS skills and geospatial research as part of another project with me. The SpF is an opportunity to fund her efforts while she further hones her skills and advances the project through data collection and processing, completing geospatial mapping, and preparing data layers for integration into the interactive Toxic Prisons platform.  The project will produce multiple academic outputs, including conference presentations, peer-reviewed publications, and public health advocacy materials. 

The research will benefit Kean University by addressing critical social and environmental justice issues in New Jersey and providing students with hands-on, interdisciplinary research experience. The SpF team will include members from Environmental and Sustainability Sciences, Computer Science and Technology, and Criminal Justice and Public Administration. The project aligns with broader academic efforts to integrate public health, criminal justice, and environmental policy in addressing systemic inequities. 

Scaling up Vermicompost Tea Treatment in NFT Hydroponic Systems and Assessing Environmental Impacts 
Dongyan Mu, Department of Environmental and Sustainability Science, CSMT

This project aims to test feasibility and assess environmental impacts of using vermicompost tea, an organic nutrient to grow vegetables in a larger scale NFT hydroponic system. It will use both experiment and software modeling in completing this project. The research will focus on three main objectives: 

1. Brew and Apply Compost Tea – Brew compost tea from vermicompost produced at Kean and apply it to grow at least three vegetables in a Nutrient Film Technology (NFT) hydroponic system on campus. Plant yields and resource consumption will be recorded. 

2. Establish a Life Cycle Inventory (LCI) – Develop an LCI for using vermicompost tea in NFT hydroponics, accounting for materials used, direct emissions, discharges, and resource consumption from both the hydroponic system and its supply chain. 

3. Conduct a Life Cycle Assessment (LCA) – Perform an LCA to evaluate the environmental and human health impacts. The assessment will identify key impact factors and propose strategies to improve the environmental sustainability of the system. 

This project focuses on hydroponics, a water-based farming technique gaining prominence in agriculture worldwide. While hydroponic production offers many advantages, the reliance on synthetic nutrients has raised concern over its sustainability. To address this challenge, the project PI has conducted research on various compost teas for hydroponic production. The findings indicate that vermicompost tea performs best in supporting plant growth. In summer 2024, a worm composter was purchased to produce vermicompost using in-vessel compost and food waste collected on campus. The results showed the vermicomposting process enhanced nutrient availability, making it easier for plants to absorb nutrients in hydroponics. Vermicompost tea has been found to sustain lettuce growth in NFT effectively.  

Based on these findings, the PI proposes scaling up the use of vermicompost tea in a larger NFT hydroponic system located in the campus greenhouse. This expansion is essential for evaluating its long-term viability, optimizing nutrient delivery, and assessing its environmental and economic benefits, ultimately supporting the transition to more sustainable and organic hydroponic farming practices. Data collected in a larger scale hydroponic production enables a more comprehensive LCA by providing larger datasets on resource use, energy consumption, nutrient efficiency, and environmental impacts. This ensures more accurate comparisons between vermicompost tea and conventional hydroponic fertilizers, helping to identify sustainable practices that can be applied on a commercial scale. 

The project aims to demonstrate a waste-to-food hydroponic system that integrates waste reduction with organic food production, addressing both climate change and food security in urban areas. Additionally, a peer-reviewed journal article will be published to present the study’s findings, contributing to a broader understanding of organic nutrient use in hydroponic farming. This initiative also has the potential to alleviate food deserts by providing nutritious food options to urban communities, aligning with Kean’s commitment to sustainable development and highlighting its role in advancing sustainable urban agriculture. Furthermore, a LCA of vermicompost tea in hydroponic systems can address knowledge gaps by evaluating its environmental impact, long-term sustainability, and practical applications. The findings will provide critical insights and valuable references for future research. 

Monitoring of Illicit Drug Consumption from The Joint Meeting Treatment Facility Wastewater Samples 
Mingjing Sun, Chemistry and Physics, CSMT

Illicit drug use is a global problem with severe consequences for public health, security, the economy, and social development. In New Jersey, it remains a critical public health issue, as evidenced by the alarming statistics reported in 2022. That year, the New Jersey Department of Human Services, Division of Mental Health and Addiction Services recorded 85,266 treatment admissions and 84,437 discharges. Additionally, 2,914 confirmed drug overdose deaths underscored the urgency of implementing effective drug monitoring and intervention strategies. 

One innovative approach to monitoring drug consumption trends is through wastewater analysis. When individuals consume illicit substances, residues of the parent drug and/or their metabolites are excreted and eventually enter the sewage system. By analyzing wastewater samples, researchers can obtain real-time epidemiological data on drug use within specific communities. This method offers a powerful tool for public health agencies and policymakers to develop evidence-based intervention programs, optimize resource allocation, and implement targeted harm reduction strategies. In Essex and Union Counties, wastewater analysis will provide critical insights into regional drug consumption patterns, allowing for more effective assessment of current policies and early detection of emerging drug threats. 

A key facility in this initiative is the Joint Meeting of Essex and Union Counties, which owns and operates a wastewater treatment plant serving over 600,000 residents across a 64-square-mile area. The member municipalities include East Orange, Hillside, Irvington, Maplewood, Millburn, Newark, Roselle Park, South Orange, Summit, Union, and West Orange, with the City of Elizabeth also served as a customer municipality. Located in Elizabeth, the treatment facility receives and processes residential, commercial, and industrial wastewater, along with stormwater flows from the combined sewers in Elizabeth. Following a complex purification process, the treated wastewater is discharged into the Arthur Kill, a tidal strait separating New Jersey and Staten Island. Given its extensive coverage, this facility presents an ideal location for conducting a comprehensive wastewater-based epidemiological study. 

To ensure precise and reliable quantification of illicit drugs in wastewater, we will utilize the Waters Xevo TQ Absolute Tandem Quadrupole mass spectrometer, an ultra-sensitive instrument designed for trace-level detection. This state-of-the-art mass spectrometer employs multiple reaction monitoring (MRM), the gold standard for quantitative drug analysis. MRM enhances selectivity and sensitivity, ensuring reproducibility and accuracy in detecting illicit drug concentrations. By leveraging this advanced analytical framework, we will establish a robust system for assessing community-level drug consumption. 

The primary objective of this project is to develop a highly sensitive and reliable analytical approach for detecting illicit drugs in wastewater samples collected from the Joint Meeting wastewater treatment facility. Through this initiative, we aim to establish a comprehensive monitoring system to assess drug consumption trends in Essex and Union Counties. The findings from this study will provide valuable insights for refining public health policies, supporting law enforcement efforts, and advancing forensic science methodologies. Ultimately, this research will contribute to a more effective and informed response to the challenges posed by illicit drug use in the region. 

Simultaneous WiFi-Based Gesture and User Recognition on Resource-Constrained Mobile Devices 
Bin Hu, Department of Computer Science and Technology, CSMT

Wireless sensing has gained significant momentum in recent years, particularly in smart healthcare and smart home environments. Recently, WiFi-based gesture recognition systems have emerged to enhance the efficiency and quality of life in these settings. The fundamental principle behind these systems is that WiFi channels are distorted by arm or hand gestures, enabling natural interactions with smart devices and robots. Beyond capturing the semantic meaning of diverse gestures, many applications require accurate user identification — for instance, in smart factories, smart homes, and VR gaming — to support access control, content recommendation, and customization. This project addresses the emerging research challenge of user-identified gesture recognition, paving the way for more intuitive and secure human–machine interactions. Our goal is to develop a novel framework for real-time, ubiquitous WiFi-based sensing that enables simultaneous cross-domain gesture recognition and user identification. By leveraging commodity WiFi, our framework will extract domain-independent motion change patterns from arm gesture signals, capturing both inherent gesture characteristics and personalized user styles. A key innovation is the development of an efficient, seam carving–based pattern extraction method to support real-time performance. These extracted patterns will then drive a deep neural network (DNN) tailored for the dual tasks of gesture recognition and user identification, employing innovative splitting and splicing schemes to optimize collaborative learning between these tasks. The framework’s performance will be validated using public datasets, and benchmarked against state-of-the-art approaches. This project is an extension of our ongoing project, ”WiFi-based Human Activity Recognition.” The students have completed the system design, including feature extraction and model design. They evaluated ten DNN models using a public human activity dataset and will present their findings at Kean Research Day and the NCUR Conference 2025. The students are currently using commodity laptops to collect real WiFi datasets and evaluate the proposed approach.

Freight Trip-Aware Multi-Agent Reinforcement Learning for Dynamic Urban Freight Distribution  
Dan Liu, Department of Management, CBPM

With urban freight demand projected to increase by approximately 78% globally by 2030, reducing empty miles and late deliveries simultaneously remains a major challenge, particularly in dynamic traffic, multi-fleet environments where multiple agents interact within a network adapted to unexpected disruptions. To address the challenge and enhance the efficiency and sustainability of the freight delivery distribution, this study aims to develop a heterogeneous capacitated vehicle routing with stochastic demand (HCVRP-SD) model and multi-agent reinforcement learning approach to optimize the urban freight distribution network, considering the dynamic characteristics of freight trips. To achieve this objective, this study will first map urban freight trips by assigning origin-destination (OD) flows to specific corridors, uncovering freight distribution characteristics. To solve the HCVRP-SD model, this project will develop a Linformer-based multi-agent reinforcement learning (LMA-RL) approach. Finally, the robustness of the HCVRP-SD model and LMA-RL approach will be evaluated and validated using both benchmark and real-world datasets. 

Intellectual Merit: Traditional urban freight distribution network modeling often relies on the "four-step model" that was originally designed for passenger transportation. Although these models can reveal stable distribution patterns of freight transportation, they face significant limitations in capturing the stochastic variabilities of dynamic freight distribution systems. Effectively and intelligently modeling a stochastic, multi-agent urban freight distribution system remains a significant scientific and technological challenge. While multi-agent deep learning approaches have shown promise, most existing research focuses on vehicle and node configurations, often overlooking the dynamic characteristics inherent in urban freight trips. Further, traditional approaches, especially exact and heuristic algorithms, face significant computational challenges when handling stochastic disruptions at larger scales. Thus, this study bridges the knowledge gap and technical challenges by developing a new HCVRP-SD model and an advanced Linformer-based multi-agent deep reinforcement learning framework that integrates freight trip dynamics. This research will contribute to both theoretical advancements in freight network modeling and practical improvements in the efficiency and sustainability of urban freight distribution operations. 

Broader Impacts: The proposed adaptive models and algorithms not only optimize multi-agent urban freight distribution networks but also provide scalable solutions for emerging logistics technologies. They can be seamlessly applied to hybrid delivery systems—such as truck-robot and truck- courier configurations—enhancing vehicle utilization across diverse fleet sizes. Moreover, the models promote smarter city logistics, equitable delivery practices, and sustainable e-commerce distribution, ensuring adaptive and efficient freight delivery in dynamic urban environments. This study's broader impact lies in its potential to transform urban delivery networks by significantly enhancing their efficiency, sustainability, and adaptability, with far-reaching implications for industry, society, and the environment. Additionally, the successful completion of this project and dissemination of the research outcomes will promote the development of the discipline, enrich undergraduate curricula in Supply Chain Management, and also bring more visibility of Kean’s supply chain management research. The attractive research and mentoring plan will engage more undergraduate students’ participation in research at Kean, which necessarily promotes undergraduate research and prepares the next generation of STEM professionals in logistics and supply chain management. 

Integrating Emerging Brain Science with Practical Parenting Strategies for Neurodivergent Population 
Mansi Brat, Counselor Education Department, CHPHS

Caregivers of neurodivergent children have high rates of parenting stress, depression, and anxiety due to the significant demands of their children’s diagnosis (Hayes & Watson, 2013). Families of children with ASD have greater stress than those of children with other developmental disadvantages (Kim et al., 2020). A decreased sense of parenting self-efficacy can cause higher levels of parental depression and anxiety (Rezendes & Scarpa, 2011). Aspects of stress can be viewed as a bi-directional approach that impacts the parent-child relationship and influences co-regulation strategies.

Emerging research in brain science, particularly within the framework of Interpersonal Neurobiology (IPNB), provides valuable insights into how neurodivergent individuals process the world. This knowledge can be effectively translated into practical parenting strategies to support children with autism, ADHD, dyslexia, and other neurodivergent profiles. This study will explore the intersection of neuroscience and parenting, focusing on neuroplasticity, emotional regulation, social cognition, and executive functioning. This research will incorporate disability theories to examine disability from a more inclusive perspective, viewing it as a spectrum of human diversity rather than as a deficit (Creswell & Poth, 2023). By applying critical disability theory, the researchers will explore the intersection of disability with social, cultural, historical, and political contexts (Shildrick, 2020). This approach challenges the societal norms that categorize differences as impairments and amplifies the voices of neurodivergent communities. By focusing on their experiences, researchers can uncover essential insights and identify opportunities for enhancing services, ultimately contributing to a more equitable future (Creswell & Poth, 2023; Darcy et al., 2022; Merten et al., 2011).

Neuroplasticity research highlights the brain’s ability to adapt and change through experience. For neurodivergent children, structured and relationally attuned parenting can help strengthen neural pathways that support self-regulation, attention, and learning. Consistent, responsive interactions with caregivers foster secure attachment, which is crucial for emotional and cognitive development. Parents who use strategies such as positive reinforcement, predictable routines, and individualized learning approaches can optimize their child’s neural growth and resilience.

The role of emotional regulation is another key focus, particularly through the lens of Polyvagal Theory. Neurodivergent children often experience heightened sensitivity to environmental stimuli, leading to challenges in stress regulation. Parents can support their children by employing co-regulation techniques, including deep breathing exercises, sensoryfriendly spaces, and rhythmic activities like music or movement. These approaches help shift the child’s nervous system from a reactive state to a calm, engaged state, enhancing their ability to navigate social and learning environments. Furthermore, emotional regulation is also vital in the Transactional Model of Stress and Coping, as parents who emotionally regulate can modulate their responses to stress-inducing circumstances (Costa et al., 2017; Lazarus, 1999; Lazarus & Folkman, 1987). In this perspective, coping is defined as fluctuating cognitive and behavioral action to acquire, minimize, or endure the internal or external pressures associated with stressful transactions (Folkman, 1984; Folkman & Lazarus, 1980). Through self-awareness and emotion regulation, parents can reevaluate and determine the significance of certain events (Costa et al., 2017; Lazarus, 1999).

Many neurodivergent individuals experience difficulties with social-emotional processing, which can impact peer relationships and self-awareness. Similarly, executive functioning, which encompasses skills such as impulse control, planning, and working memory, is often a challenge for neurodivergent children. Research suggests that these skills can be scaffolded through adaptive parenting techniques (Costa et al., 2017). Strategies such as visual schedules, task chunking, and gamified learning help children build cognitive flexibility and selfmanagement skills. Additionally, parenting strategies that emphasize social modeling, explicit teaching of social cues, and opportunities for structured peer interactions can strengthen social cognitive abilities. Play-based and relationally rich interventions further support the development of empathy and social engagement.

By integrating emerging neuroscience with practical parenting, caregivers can create supportive environments that honor neurodivergent children’s unique needs. From a family resilience lens, these strategies can significantly strengthen the families’ capacity to navigate challenges or crises with confidence (Walsh, 2002). This study advocates for a paradigm shift from deficit-based models to strength-based approaches that leverage brain science to foster resilience, learning, and well-being in neurodivergent populations

Polarized Polity: Critically Evaluating the Visual and Discursive Rhetoric and Exclusionary Politics 
Joshua Guitar, Communication, Media, and Journalism, CLA

Through a broad application of ideology criticism that examines the confluence of visual and discursive rhetoric, my research team and I will analyze reactions to calls for democratic progress. We will examine ideological patterns within prominent, contentious discourses, analyzing the images and corresponding words, phrases, and sentiments that reveal the ideological composition of political polarization in the United States. Analyses will center the discourses of agents and actions of political change as well as the broader social movements and authoritarian reactions that follow them. We build our study from the reality that calls for democratic progress, advanced by engaged individuals and collectives, can illuminate authoritarianism and its correlating infringements of civil liberties and restrictions of political equality. More specifically, we theorize that such ideological presuppositions of a culture provide us with data that can be used to evaluate a society as tending toward democracy or authoritarianism. Although pertinent scholarship focuses heavily upon the decisions and discourses across contemporary media, critical rhetorical studies that center visual rhetoric remain lacking. Yet, visuals can wield significant political influence, especially when accompanied by ideological discourse. Although there is no shortage of overt iterations of such ideological artifacts, polarization often advances through inconspicuous or covert means. Thus, while academic discussions on political communication often center interviews, social media posts, speeches, campaign advertisements, and other such utterances, this project substantially engages visual rhetoric in concert with discursive utterances to reveal the prevailing ideologies informing the tense polarization presently fettering the advancement of American democracy. 

Through a conglomerate of case studies, this broader project will constitute an ideological analysis of the discourses that follow calls for democratic social change. In our case studies, we will focus on recently prominent protest actions, like Black Lives Matter, #MeToo, and other well-known truth-telling or social justice initiatives. While ideology critique operates as an overarching method of critical rhetorical analysis, we intend to apply, where appropriate, three distinct approaches to critical rhetorical inquiry: ideographic criticism, rhetorical psychoanalysis, and abstruction analysis. Whereas we theorize that the main component of our presently polarized citizenry is the tension between the advancement of democracy and the preservation of an authoritarian status quo, societal responses to calls for democratic action serve as a metric by which democracies can be evaluated. These analyses will help us compose a humanistic method for evaluating how democratic societies situate themselves across this spectrum of political power. Through this work, we will be able to provide strategies for mitigating the trending authoritarianism and corresponding polarization across our society, from the local to the national level. We are intent that our research advances human knowledge; however, it is paramount that work like this helps us address present societal problems. While we intend to author a book and/or numerous academic journal articles and book chapters from this work, we also plan to use this research to reach citizens and policymakers who are laboring to address the currently caustic political environment. 

Adopting Zero Trust Security in Cloud: A Comparative Study  
Jiazon Lei, Department of Computer Science and Technology, CSMT

Cybersecurity threats are becoming increasingly sophisticated, with data breaches, ransomware attacks, and insider threats posing serious risks to businesses and individuals. Many organizations rely on cloud computing to store sensitive data, run applications, and support remote work. However, conventional security models, which assume that users and systems inside a trusted network are safe, have proven inadequate against modern cyberattacks. Zero Trust Architecture (ZTA) is emerging as a promising cybersecurity approach, based on the principle of “never trust, always verify.” Under this model, all users, devices, and applications must continuously prove their legitimacy before gaining access to resources.

While Zero Trust has gained attention as a security framework, its implementation in cloud environments remains a complex challenge. Cloud systems operate in dynamic, distributed environments, where traditional security controls may not be effective. This research explores how Zero Trust security can be applied in cloud-based infrastructures, particularly in virtual machines (VMs) and containerized applications. The study will perform a comprehensive study on existing Zero Trust solutions, comparing their effectiveness in different cloud settings and identifying key challenges organizations may face when deploying them. By conducting hands-on tests and evaluations, this research aims to provide practical insights into how Zero Trust security can be integrated into cloud environments. Key considerations include security enforcement, performance impact, and compatibility with modern cloud architectures. Additionally, the study will discuss potential improvements and future research directions to enhance Zero Trust adoption in the cloud.

The findings from this research will be valuable for organizations and businesses looking to strengthen their security strategies, as well as for cloud service providers seeking better ways to protect digital assets. Furthermore, this research will provide practical insights for students who are aspiring cybersecurity professionals, equipping them with a deeper understanding of modern security challenges and solutions in cloud environments. By bridging Zero Trust principles with real-world cloud deployments, this study contributes to the ongoing effort to build safer, more resilient cloud systems capable of withstanding modern cyber threats

Nantucket XR: Designing Immersive Experiences for National Historic Landmark Districts sites  
Henry Stankiewicz, Robert Busch School of Design: Graphic Design, MGC

Extended reality design as it relates to interactive graphic design experiences at cultural heritage sites is a contemporary area of research and collaboration between historic sites/museums and academic institutions. How might we create learning experiences for audiences which provide a similar level of immersion to being inside an inaccessible historic structure or building?

Specifically, this SpF application focuses on students engaging in on-site research, practicing 3D modeling of selected National Historic Landmark District sites on Nantucket, MA, capturing 360 degree VR media of those spaces for reference and for further immersive experience assets, and 3D scanning of artifacts that are in the Nantucket Historical Association’s (NHA) museum collection and are either too fragile or too precious to exhibit on the property sites themselves. The end goal is for students to produce a refined prototype of an immersive environment experience that can be delivered and installed/exhibited in the NHA’s mobile app, broadening access to sites that are not ADA compliant, are too precious/fragile to exhibit regularly, and/or are not fully intact.

Stankiewicz and a previous team of design students, in conjunction with site and digital experts at the NHA, have completed an immersive environment project that has been approved for a unique outdoor exhibition in the nationally accredited Whaling Museum as well as stored in the museum’s online archive. The NHA has identified the Old Gaol and the Jethro Coffin House as primary interest properties for future immersive experiences.

The Old Gaol, or Old Jail, comprised of four holding cells for those awaiting trial on the island. Constructed as a more secure facility than the previous iteration after multiple jailbreaks, Old Gaol was restored in 2014 and is a main stop on the Nantucket Historical Association’s tours due to the interesting stories surrounding inmates and unique procedure. For example, inmates often went home in the evening and returned to their cell the next day as the jailer did not keep night hours. The Jethro Coffin House, also known as the Oldest House, was constructed in 1686 and is believed to be the oldest residence on Nantucket in its original site. The Coffin and Gardner families, two of the oldest families and once-pitted rivals, were joined by a marriage that calmed the significant turmoil on the island. The Jethro Coffin house represents their unity and the growth of Nantucket as an island. Creating these designed learning experiences will be inherently interdisciplinary; creating digital content to be used in virtual worlds is a distinct research activity from creating interactive interfaces to experience that content. My SpF proposal pairs students from both areas, driving a collaborative research team that will be take on designing immersive worldbuilding assets and programming interfaces for those assets to be experienced on the web and in the Whaling Museum. The intention of this application is to involve student researchers in creating forward-thinking learning experiences within the context of design technology

Kean University & Community Access Unlimited Concert Series: Building Inclusion & Expanding Impact 
Lynann Schraer-Joiner, Department of Music, CLA

Since 2015, Kean University's music education program has cultivated a musical ecosystem with local K-12 music programs and the Academy for Continuing Education (ACE), a division of Community Access Unlimited (CAU) that supports adults with physical and intellectual disabilities. This collaboration has sought aims to promote diversity, equity, inclusion, and belonging through joint performances and related activities involving ACE participants and their communities. To date, this partnership has produced nine concerts, the latest in January 2025.

These collaborative concerts and activities have enriched the lives of ACE participants, serving as a platform for creativity, collaboration, and community building. They have enabled CAU individuals to make creative choices, express themselves, and contribute meaningfully to discussions about the significance of the music they engage with. This transformative experience has provided opportunities that have often been out of reach for them. The Kean/CAU initiative has also inspired and guided Kean music education interns. Through teaching and mentoring ACE participants, these interns have demonstrated increased awareness, openness, and commitment to diversity and community. Feedback from K-12 partners has been overwhelmingly positive.

In January 2025, students and faculty participated in our 8th and 9th UNITY concerts. These concerts met the goal of offering two performances to accommodate diverse groups with scheduling constraints. The additional concert expanded participation, receiving enthusiastic responses and requests for future performances. Plans for expansion include collaboration with the Opportunity Project, an organization that supports individuals with brain injuries and the New Jersey Symphony Orchestra, as well as integrating iPads and new rehabilitative technologies.

Therefore, the purpose of this project is to further enhance the Kean/CAU Collaborative Concert series by involving additional adult day programs and inviting more K-12 students to perform with us in our 10th and 11th concerts. Additionally, we aim to expand community engagement using iPads and rehabilitative instruments, allowing more individuals with disabilities to engage musically. Plans also include a pre-concert program featuring an instrument petting zoo and experiential sound platforms and a post concert "Accessible Music Day: A Celebration of Inclusive Sounds and Experiences for All

Decoding Demographics: An Integrated Strategy for Analyzing and Visualizing U.S. Population Data  
Ching-yu Huang, Department of Computer Science and Technology, CSMT

In a contemporary landscape saturated with digital information, grasping the intricate dynamics of U.S. population data necessitates a sophisticated and comprehensive analytical strategy. This research endeavors to untangle the complexities inherent in demographic patterns through a multifaceted approach—termed "Decoding Demographics." A substantial demographic dataset, sourced from the 2020 US Census, forms the foundation of this project. Automated tools will be developed to analyze and visualize this extensive dataset. Our methodology encompasses pivotal demographic indicators such as gender, race, age distribution, family income, education, unemployment rate, location, and medical insurance.

By integrating these variables, our objective is to provide a nuanced perspective on the interconnected factors shaping the demographic landscape of the United States. Central to our analytical approach is the application of advanced data analytics techniques to extract meaningful patterns from this extensive dataset. A meticulous examination of genderbased trends, racial dynamics, educational patterns, income distribution, geographical influences, and healthcare accessibility forms the cornerstone of our exploration, seeking to uncover the intricate tapestry of the U.S. population.

An integral facet of this research lies in the use of cutting-edge visualization tools to translate complex data into insightful representations. Interactive graphs, charts, and geographical graphs serve as mediums to transform intricate demographic trends into accessible visual narratives, facilitating a deeper understanding of the relationships between diverse demographic factors.

Our investigation delves into gender disparities in education and the workforce, racial patterns in socio-economic status, the correlation between family income and healthcare access, and the geographical distribution of demographic characteristics. Beyond merely identifying existing trends, our research aims to shed light on potential correlations and causations, providing a comprehensive picture of the U.S. population landscape.

This integrated strategy offers a dynamic exploration of data interactions, enabling stakeholders to pinpoint critical areas for policy interventions, resource allocation, and community development. The user-friendly nature of our visualizations ensures that policymakers, researchers, and the public can engage with and comprehend the intricate dynamics shaping the U.S. population.

As we navigate the intricate landscape of demographic data, our research emerges as a guiding force for comprehensive analysis and visualization. The outcomes are poised to contribute to evidence-based decision-making, fostering a deeper understanding of societal trends and empowering stakeholders with the tools needed to address challenges and inequalities within the American demographic framework. This research stands as a pioneering effort to demystify the complexity of U.S. population data, paving the way for informed and equitable societal progress

Leveraging AI-Driven Emotion Detection to Optimize Digital Marketing Strategies 
Raza Rafique, Department of Management, CBPM

In our research, we will examine the transformative role of artificial intelligence in analyzing and responding to consumer emotions to enhance marketing effectiveness. Specifically, we will explore the potential of AI-powered emotion recognition technologies in digital marketing, focusing on optimizing brand-consumer interactions and improving campaign strategies. The research will thoroughly review existing literature and analyze contemporary AI techniques in emotion detection, particularly in digital advertising and consumer engagement. Different modalities of emotion recognition will be explored, including facial expression analysis, sentiment analysis of social media content, and multimodal approaches that integrate visual, audio, and textual data. Advances in machine learning and natural language processing have significantly improved the precision of these technologies, allowing marketers to gain real-time insights into consumer sentiment, preferences, and reactions to advertisements. Based on that, the study aims to assess how AI-driven emotion recognition can enhance personalized marketing by catering to consumers’ growing expectations for tailored experiences. Additionally, it will address ethical considerations and challenges associated with emotional AI, such as privacy concerns and the need for transparent data usage. By contributing to the expanding research on AI applications in marketing, we plan to offer practical insights for industry professionals seeking to leverage emotion AI for more effective and personalized customer engagement. Ultimately, it aims to bridge the gap between academic research and marketing practice, fostering innovation and the development of emotionally intelligent brand interactions.

The Effectiveness of Virtual Reality Stressors in Immune Function and Reinforcement Learning 
Yu Sun Chung, Department of Psychology, CLA

Anhedonia – the loss of pleasure or lack of reactivity to pleasurable stimuli – is a core feature of depression and a transdiagnostic construct in psychopathology. According to the National Institutes of Health's recent update on anhedonia within the Positive Valence Systems of the Research Domain Criteria, anhedonia is thought to reflect impairments in reinforcement learning (RL), which is the ability to learn stimulus-outcome associations from reward feedback to guide future behavior. This process is supported by the corticostriatal dopaminergic (DA) reward circuit. 

Theoretical frameworks and compelling preclinical evidence implicate stress-induced inflammation as a key psychobiological pathway leading to anhedonic behavior. Elevated markers of inflammation have been linked to both anhedonia and dysregulated reward circuitry in the corticostriatal system. Stress activates the immune system, triggering the release of pro-inflammatory cytokines like IL-6. These cytokines not only promote inflammation but also alter DAergic corticostriatal circuitry, which is involved in RL and motivation. This disruption can occur by decreasing DA release, reducing sensitivity to DA, or impairing communication between the prefrontal cortex and the striatum, ultimately leading to impaired reward learning and motivation. A commonly used method to examine the influence of stress on behavioral responses in adults is acute laboratory stress. Acute psychosocial stress, measured using the Trier Social Stress Test (TSST), has been shown to increase inflammatory signaling. This effect correlates with increased reward response bias scores in female adults, highlighting the sensitivity of the DAergic RL system to inflammatory signals, even after relatively mild alterations induced by a single episode of acute psychosocial stress. 

The goal of this proposal is to validate a virtual-reality adaptation of the TSST (VR-TSST) as a sensitive tool for detecting changes in stress-induced inflammation and RL in college students. The VR-TSST offers a standardized, efficient alternative to the traditional face-to-face TSST, which combines public speaking and mental arithmetic tasks to induce robust endocrine stress responses in both adults and adolescents. In collaboration with Dr. Joiner, co-PI, we will validate whether the VR-TSST can successfully modulate stress responses and immune function among college students, as measured by salivary IL-6 and cortisol levels, respectively.  

Kean University students (ages 18-25, n=80) will be randomly assigned to either the acute psychosocial stress condition (i.e., VR-TSST) or a no-stress active control group. All participants will complete a probabilistic reward learning task (developed by PI Dr. Chung) 90 minutes after undergoing the VR-TSST, which coincides with the peak of the stress-induced inflammatory response. Salivary IL-6 levels will be measured before and after the VR-TSST, as IL-6 is a key pro-inflammatory cytokine responsive to acute stress. Additional saliva samples will be used to assay pubertal hormones (estradiol and testosterone) and cortisol levels, in collaboration with the Kean Clinical Diagnostic Lab. All research activities, including the development of the VR-TSST, collection and analysis of RL behavior, and hormone assays, will be conducted by the proposed students. Thus, this work will not only enhance Kean students’ research experiences but also contribute significantly to our understanding of stress-induced immune function in relation to depression in college students. 

Building Resilience: Adaptive Reuse as a Catalyst for Community Transformation 
Venesa Alicea-Chuqui, School of Public Architecture, MGC

Adaptive reuse involves repurposing an existing structure for a different use than originally intended. When done purposefully, it combines sustainability with creativity by minimizing waste and reducing the need for new construction. Preserving the core structure saves valuable resources and contributes to a more sustainable built environment. This practice often involves transforming abandoned warehouses, schools, or, in this case, correctional facilities, into vibrant, functional spaces.

The focus of this research is an abandoned correctional facility consisting of 11 buildings on 320 acres of land in Jayuya, Puerto Rico. Transforming this neglected site into an adaptive reuse project presents a unique opportunity to breathe new life into a once dormant and negatively perceived complex. By repurposing the facility into a community with housing for seniors and homeless youth, educational facilities, and vocational workspaces, we can address pressing social needs. The existing architecture, characterized by strength, security, and resilience, provides a solid foundation for innovation, blending the past, present, and future to create an inspiring environment for residents and visitors alike. This research will explore how to promote community engagement, generate jobs, provide sanctuary spaces, while also creating visions on how the adaptive reuse of the site becomes a symbol of renewal, progress and community resilience.

Ultimately, this research aims to demonstrate how adaptive reuse can serve as a catalyst for sustainable community change. Repurposing abandoned correctional facilities for educational, vocational, and housing purposes revitalizes both the physical space and the social, economic, and environmental fabric of the community. The project will culminate in visualizations of these reimagined spaces, illustrating the potential of adaptive reuse to create thriving, resilient communities. These visualizations will serve as a tool to inspire future adaptive reuse initiatives, showing how former sites of confinement can be transformed into symbols of opportunity, renewal, and empowerment.

Acute Effects on Circuit Training and Resistance Exercise on Exerkine Response in Young Adults  
Pragya Sharma Ghimire, Health and Human Performance, CHPHS 

Evidence supports that exercise is an essential component of healthy aging, and it has been widely accepted that a physically active life contributes to health-related quality of life. Many studies have quantified the influence of physical activity on bone health through longitudinal studies, cross-sectional studies, and meta-analyses of different populations. Many overlapping signaling pathways, including Wnt, Hedgehog, Growth Hormone (GH), Interleukin 6 (IL-6), Irisin, Tumor necrosis factor alpha (TNF-α), and receptor activator of NF-kB ligand (RANKL), serve as a fundamental mechanism for understanding muscle and bone metabolism as exerkines. Exerkine includes protein, peptide, metabolite, or cytokine produced and released by tissues, particularly skeletal muscles, in response to physical exercise. These molecules play a key role in mediating the beneficial effects of exercise on various organs and tissues throughout the body. Currently, the acute effects of exercise protocols on circulating exerkines are unclear. This study aims to compare acute serum sclerostin, DKK-1, RANKL, TNF-α, Irisin, and IL-6 responses to circuit training and traditional resistance exercise.In this randomized crossover study, participants will perform two protocols separated by 2-week wash-out periods: 1. Circuit training (cycle ergometer, push-up, step-ups, medicine ball twist, and front squats with kettlebell for three sets) and 2. Traditional resistance exercise (3 sets 10 repetitions 80% 1RM for leg press, seated cable row, barbell bench press, kettle bell dead lifts, and dumbbell seated shoulder press. Fasting morning blood will be taken before exercise training (PRE), immediately post-exercise (IP), and 30 minutes post-exercise (30P). Using ELISA, blood samples for lactate, hematocrit, sclerostin, DKK-1, IL-6, TNF- α, RANKL, and irisin serum concentrations will be analyzed. Based on our observations about the acute bone turnover response to exercise and previous studies related to sclerostin and PTH, we hypothesized that circuit training would elicit a more significant release of circulating exerkines than traditional resistance exercises. The results would contribute to a better understanding of how different exercise interventions can interact with the physiological systems associated with these exercises and bone remodeling. Recognizing the significance of these bone, muscle, and inflammatory markers aids in developing novel screening methods for predicting adverse health events in clinical and sports-performance populations. Additionally, these markers can be utilized to establish links with training regimens and monitor healthcare outcomes. 

Measuring Language Proficiency in Monolinguals, Bilinguals, and Multilinguals  
Anne Neveu, Communication Sciences and Disorders, CHPHS 

The proposed research project aims to measure language proficiency in monolinguals and diverse groups of bi/multilinguals. While measuring language proficiency is fundamental in research on bi/multilingualism and in clinical settings, there is no standardized method for measurement despite years of calls for its development across multiple related subfields of bilingualism. The most common measures are self-reports on scales from e.g. 1 to 7, which are known to be biased. This is a problem for replicability and generalizability of findings.  

I investigated this question in Spanish-English and Chinese-English bilinguals (two manuscripts with revise and resubmit decisions in Bilingualism: Language and Cognition, Q1, H index=82, a peer-reviewed journal) and am now examining a broader population. I plan to publish the present project in the same venue. 

We aim to: 

1. Examine to what extent self-reports of proficiency predict a widely used and reliable objective measure of proficiency, in mono-, bi-, and multilingual users of spoken and signed languages, 

2. Examine to what extent do individuals reconsider their initial self-reports based on how they performed on the objective measure, across groups of mono-, bi- and multilinguals. 

For 1), we expect less variability in the alignment between self-reports and objective scores in monolinguals compared to bi- and multilinguals, based on previous work. It is possible that we observe increasing variability in alignment between self-reports and objective scores per additional language known. 

AI Capital Deepening and Its Impact on Economic Growth and TFP 
Nazif Durmaz, Department of Marketing, Global Business and Economics, CBPM  

The rapid advancement of Artificial Intelligence (AI) is transforming industries, shifting capital investment patterns, and affecting labor markets.  Existing research mainly focuses on AI’s impact on employment, while its effect on economic growth and Total Factor Productivity (TFP) remains underexplored.  

The present study shows that estimates of total factor productivity TFP over 7 years of panel data for 36 economies improve adding AI capital deepening input to capital and labor as a factor of production.  Annual TFP series derived from the estimates of panel methodology led to cumulative effects over economies and time spans.  The aim of the project is to use the estimates of TFP to compare with and without AI capital deepening factor of production and across different techniques and time spans.  The levels of TFP seem more reasonable based on cumulative adjustments in estimated marginal productivity.    

Solow (1956) introduced the Solow Growth Model which incorporates TFP into economic growth study.  In this model capital, labor, and a residual factor form output growth.  The main idea in Solow’s model is technological progress is considered in the sustainable long-term contributor in growth, not just capital accumulation.   

TFP in economic growth refers to the shortcoming of neoclassical production in accounting for observed changes in per capita income over decades or longer.  The reasons for this Solow residual between observed and theoretical output include properties of the assumed production function, improved technology, increased labor skills, and omitted variables.   

Including AI capital deepening input increases explanatory power by more than half of units.  The large AI capital deepening share coefficient based on marginal productivity implies AI capital deepening is underused and other inputs are overpaid.   The point is that estimation of underlying production provides a measure based on marginal productivity.  The estimate of total factor productivity is sensitive to the time and whether AI capital deepening input is included in the estimate.  The present cumulative effect of yearly discrete adjustment provides a reliable foundation to gauge TFP.  The advantage is reliance on marginal productivity revealed in the estimates rather than assuming constant returns and competitive pricing.     

Breast Cancer Detection in Ultrasound Images Using Weakly Supervised Techniques and Multimodal Model  
Kuan Huang, Department of Computer Science and Technology, CSMT   

Breast cancer remains a significant health concern, ranking as the most diagnosed cancer among women in the United States and the second leading cause of cancer-related deaths. It disproportionately affects Black and Hispanic women. Numerous computer-aided diagnosis (CAD) systems leveraging deep learning have been developed for breast ultrasound (BUS) imaging to enhance early detection and reduce mortality. Breast cancer detection involves two steps: 1) detecting the tumor's location in ultrasound images and 2) classifying the tumor as cancerous or non-cancerous. Developing a fully supervised model for breast cancer detection requires a large amount of annotated data at both the location and cancer/non-cancer levels. While cancer/non-cancer labels are relatively easy to obtain, location labels require doctors to mark them, which makes them harder to acquire manually. Existing studies have explored training detection models using weakly supervised approaches, such as only classification supervision and multi-instance learning supervision, to avoid the need for detailed detection-level labels. With the advancement of large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA-3, and Google’s Gemini, it has become clear that a wealth of clinical data within medical datasets remains underexploited for training weakly supervised detection models. Given the progress in LLMs and the availability of textual descriptions accompanying medical images, we pose two questions: “Can textual information be integrated to improve weakly supervised image detection frameworks?” and “Can LLMs contribute to better breast cancer detection?” In preliminary studies, the research team successfully explored a fully supervised breast cancer detection framework. It integrated a language model with a vision model to leverage clinical text data for improving breast cancer detection in BUS images. To extend the use of LLMs in weak supervision and enhance breast cancer detection with no location annotations, we aim to develop a novel weakly supervised framework for breast cancer detection in ultrasound images, leveraging LLaMA 3.2 Vision.

Objective: To implement the proposed weakly supervised framework, we aim to achieve three key objectives: 1) Train the multimodal LLM, LLaMA 3.2 Vision, for breast cancer classification and clinical data generation. 2) Develop a novel algorithm that utilizes clinical descriptions for additional supervision, enhancing existing weakly supervised detection training performance. 3) Leverage Meta's vision model, Segment Anything, to refine breast cancer detection results.

Significance and expected outcomes: The significance of this project lies in several key areas. First, it aims to enhance breast cancer detection model training using fewer detection-level labels, contributing to advancements in AI-driven healthcare and women’s health. Second, the approach aligns with the emerging AI research focus on multimodal LLMs, providing students with hands-on experience in applying cuttingedge LLMs, which will benefit their academic and professional growth. The expected research outcomes include: 1) Open-source software for the weakly supervised breast cancer detection LLM on GitHub. 2) A conference publication at IEEE EMBC 2026. 3) A publication in an SJR Q1 journal. 4) Foundational results will support the PI in preparing external funding applications

Dual-Target Inhibitor Discovery for Zika Virus: Integrating Ligand-and Structure-Based Drug Design   
Supratik Kar, Department of Chemistry and Physics, CSMT   

The primary objective of this study is to identify potential dual-target inhibitors against the Zika virus by screening the ChemDiv chemical library against both NS5 RNA-Dependent RNA Polymerase (RdRP) and NS5 Methyltransferase (MTase). Under the current SpF award (2024), we focused on inhibiting the NS5-RdRP target and proposed three novel molecules from DrugBank (DB11938, DB12615, and DB01698) subjected to further in vitro and in vivo studies. This year, our research aims to develop a dual-target approach, leveraging computational and network pharmacology techniques to enhance drug discovery efficiency. By integrating ligand-based drug design (LBDD) and structure-based drug design (SBDD), we will try to identify and evaluate potent lead molecules, ultimately leading to the identification of 2-3 hit molecules with promising dual-inhibitory potential.

Methodology

We aim to follow a structured computational workflow integrating Quantitative Structure-Activity Relationship (QSAR) modeling, molecular docking, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) profiling, molecular dynamics (MD) simulations, Molecular Mechanics/Generalized Born Surface Area (MMGBSA) calculations, and meta-analytical techniques.

The Psychology of Blaming Investors: How People Attribute Responsibility for Harmful Investments 
Peter Kardos , Department of Psychology, CLA    

Socially responsible and ethical investment is a central theme of business ethics and an important topic of public discourse. Yet, the moral connotations of individual investment decisions are not well understood. Building on and extending theories of blame attribution, we aim to investigate the psychological processes underlying blame attribution to investors for their investments in companies with socially harmful behavior. We argue that people blame individual investors for their investments in harmful companies and that influential theories of blame attribution cannot sufficiently explain why. We propose that the character judgments of the investors, as well as the perceived association between the investor and the harmful companies, can explain such blame attributions.

Through a series of experimental studies utilizing scenarios about investment decisions in companies with unethical practices, we will examine (1) if and when investment decisions influence responsibility and blame attribution to individual investors, and (2) the mediating roles of moral character and the perceived association between investors and the company they invest in.

Answering these research questions has significant theoretical implications for understanding vicarious responsibility and blame attribution and practical implications for socially responsible investing. Particularly, the project can reveal why investing in harmful companies may elicit blame attribution even without direct control over the harmful outcomes or intentions to produce them. In that regard, the project bridges fundamental questions between moral psychology and applied business ethics, promising both theoretical advances and practical implications.

Two undergraduate students will be fully involved in all research phases, from study design to manuscript preparation, gaining valuable experience in experimental methods, data analysis, and scientific writing. I have built successful mentoring relationships with multiple students during the past years at Kean University. Our regular individual and group research meetings are key for the students in gaining hands-on experience with advanced experimental methods as well as social and moral psychology. I hope that with the help of the SpF 25-26 grant, I will be able to continue the students’ training, in which they can further develop their research skills and prepare for graduate school.

The students will present their work at Kean Research Days and collaborate on the manuscript to be submitted for publication in a Q1 journal.