Rust Belt Renaissance? The Experience of Refugees from Burma in Buffalo, New York

Cavello, Seth Michael State University of New York at Buffalo ProQuest D 2019 해외박사(DDOD)

Buffalo, New York receives the most refugees for resettlement in the state, and has constructed a narrative as a welcoming place for this population. While this narrative is mostly true, refugees from Burma are facing a new set of challenges arising from Buffalo's broader urban redevelopment that need to be addressed. This study asks the following specific research questions: 1) How do refugees from Burma build community in Buffalo? In particular, how do they build social capital through ethnic and religious organizations, and shared identities? 2) How do refugees adapt their housing strategies in the face of neighborhood change? In particular, how are they responding to gentrification of the West Side neighborhood?.

Multi-objective Optimization of Big Data Transfers

Nine, Md S. Q. Zulkar State University of New York at Buffalo ProQuest D 2020 해외박사(DDOD)

The amount of data moved over dedicated and non-dedicated network links has been increasing at a much faster rate than the increase in the network capacity. However, the existing solutions fail to guarantee even the promised achievable transfer throughput. High throughput data transfers require increased utilization of the underlying resources with side effects such as high-energy consumption at the end systems and network infrastructure. As a result, the global data movement over the Internet generates an energy footprint of 100 terawatt-hours per year, costing billions of dollars to the world economy. Although a considerable amount of research has rendered power management techniques for the hardware-level networking infrastructure, there has not been much prior work focusing on the joint optimization of data transfer throughput and energy consumption at the end systems.In this dissertation, we propose a novel approach for multi-objective optimization of big-data transfers and high-speed packet processing. In our work, we consider the following optimization objectives: (1) data transfer throughput, (2) energy efficiency, (3) Service Layer Agreement (SLA) based joint optimization of achievable throughput and energy consumption, (4) device-specific optimization, (5) cross-layer throughput and energy optimization, and (5) energy-efficient optimization of high-speed packet processing. To achieve these goals, we investigate various factors that can affect the data transfer performance and energy consumption in both compute servers and mobile devices, such as – concurrency, parallelism, pipelining, buffer size, CPU core allocation, dynamic frequency scaling, last level cache (LLC) size, and DMA buffer size. We have collected real data transfer logs to analyze the impact of various factors on the data transfer task and high-speed packet processing.Our novel approach to end-system optimization for data transfer and energy efficiency is based on mathematical modeling with offline knowledge discovery and adaptive online decision making. In the offline analysis phase, we mine the historical data transfer logs to gather knowledge about the data transfer characteristics. The online adaptation phase uses the discovered knowledge from the offline analysis, along with the real-time feedback of the network condition, to dynamically optimize the protocol parameters. As the real-time investigation is expensive and provides only partial knowledge about the current network status, our model uses historical knowledge and data about the network to reduce the real-time investigation overhead while ensuring near-optimal throughput for each transfer. We also explored various challenges and optimization opportunities in mobile data transfers, where energy efficiency is crucial. Then we investigated cross-layer optimization of data transfer where we jointly optimized application layer parameters with lower-layer parameters. We also studied extensively high-speed packet processing framework (e.g., Network Function Virtualization) and proposed Reinforcement Learning (RL) based framework to optimize its throughput and energy efficiency.Our novel approach on throughput optimization is tested over different networks with different datasets, and it has outperformed its closest competitor by 1.7x and the default case by 5x. It also achieved up to 93% accuracy compared to the optimal achievable throughput possible on those networks. Our energy-efficient solution for bulk data transfers, GreenDataFlow, supports SLAs that give the service providers and the consumers the ability to fine-tune their goals and priorities in this optimization process. Our preliminary results show that GreenDataFlow outperforms the closest-competing state-of-the-art solution in this area by 50% for energy-saving and by 2.5x for the achieved end-to-end transfer performance. Moreover, our mobile device-specific solution, FastHLA, can achieve significant energy savings at the application layer during mobile network I/O, without sacrificing the performance. FastHLA can increase data transfer throughput by up to 10x and decrease energy consumption by up to 5x compared to state-of-the-art solutions. Our cross-layer optimization algorithms outperform state-of-the-art solutions, achieving up to 80% higher throughput while consuming 48% less energy. Our energy-efficient packet processing model, GreenNFV, achieves 4x throughput improvement over the baseline settings and 1.5x energy efficiency.

Three Essays on Social Dynamics in Virtual Communities

Liao, Ruochen State University of New York at Buffalo ProQuest D 2020 해외박사(DDOD)

The advance of information technologies and systems in the era of Web 2.0 has led to the proliferation of virtual communities. These communities provide an interactive platform for people who share common interests and concerns to connect with each other and exchange various information. Previous studies have noted the various social functions afforded by virtual communities, such as the formation of health support forums, knowledge sharing groups, and even grass-root movements that petition for social changes (e.g. Coursaris & Liu, 2009; Liu, Zhang, Susarla, & Padman, 2017; Mo & Coulson, 2008; Ridings & Gefen, 2004; Wang, Kraut, & Levine, 2015; Yan & Tan, 2014). However, the heterogeneity in the motivation and goals of the users means that their experience in the virtual community is often contingent on the way that they interacted with other members in the process and can produce highly individualized outcomes. The proposed dissertation attempts to address three research questions: First, how do users express themselves and elicit response from other members of the community? Second, how do the different outcomes of the interactions influence users' continued engagement in the community? And third, do different users derive higher level of satisfaction from interactions that are more coherent to their goals? The three essays listed below examines the different social dynamics in two virtual communities under distinct contexts and provides a nuanced understanding of how this series of interrelated questions can shed more light on the studies of virtual communities. The first essay examines user satisfaction in a knowledge-sharing virtual community targeting older adults. Despite the troves of potential benefits of an inclusive digital society for older adults, the adoption of technology among older adults has been lagging behind (Braun, 2013; Coleman, Gibson, Hanson, Bobrowicz, & McKay, 2010). A growing body of studies has focused on the phenomenon of digital divide and aims to address the obstacles for older adults’ adoption of virtual communities. Prior research indicates that internet and social networking media adoption rate is higher among seniors who are relatively younger or have younger "subjective ages" (Choi et al., 2014; Smith, 2014). However, older adult is not a monolithic group. Aging could bring different socio-psychological changes to different individuals, and each individual also view the aging process different and embrace the changes in their own ways. Therefore, a fundamental understanding of the effects of aging and its implication on the different characteristics of older adults is required to examine older adults’ use of virtual communities. Rooted in socioemotional selectivity theory, this paper develops hypotheses about the extent to which older adults derive satisfaction from their knowledge-seeking (KS) and knowledge-contributing (KC) activities based on their prioritization of future-oriented goals and emotionally-meaningful goals, and how their reactions to perceptions of remaining time in life moderate the effects of KS and KC activities on satisfaction. We test our hypotheses using data from three online knowledge-sharing virtual communities that focus on users born before or during the 1950s. Our results show that while both activities positively affect older adults’ satisfaction, they also substitute each other in their effects on satisfaction. We also found that older adults with a more positive attitude towards aging derive more satisfaction from a given level of KS activity as compared to others with a similar level of KS activity but who don’t have such a positive attitude towards life. Similarly, older adults who value meaning in life derive more satisfaction from a given level of KC activity as compared to those with a similar level of KC activity but lower meaning in life. This study sheds new light on how older adults, an understudied user group, derive satisfaction from their activities in knowledge-sharing virtual communities and expands our understanding on how the socioemotional states of older adults affect their goals in virtual communities and their behavior patterns. The second essay examines the antecedents that influence the exchange of informational and emotional support between support seekers and support providers in online health support communities (OHSCs) where users exchange social support in the form of Q&A through threads and replies. OHSCs have gained particular popularity amongst patients of chronic and socially stigmatizing conditions such as HIV and breast cancer, where the majority of the care and management of the disease takes place at home (Attai et al., 2015; Dissanayake, Nerur, Singh, & Lee, 2019; Eysenbach, Powell, Englesakis, Rizo, & Stern, 2004), and users can enjoy the safety of anonymity when talking about personal and sensitive conditions. Much research in healthcare and information systems has shown that both informational and emotional support from OHSCs can improve the physical and mental health conditions of those in need. However, the virtual relationships that support seekers develop and maintain in OHSCs play an important role in determining how much of the collective knowledge and experience from other members can be leveraged (Berkman & Glass, 2000; Yan & Tan, 2014). For users who attempt to seek help from the community, receiving no replies at all or replies that are not helpful can be detrimental to the overall experience. We propose that as the support seekers initiate their questions, their interactions with support providers are shaped by the way in which they present their problems, the emotions they express, and their past social network activities. Using data from a community for people living with HIV/AIDS, we employ text analytics to analyze the emotions of grief, which are common among patients of severe chronic diseases, as well as past support seeking activities of the users. Our results show that depending on the type of social support that the seekers are seeking looking for, the grief emotions they express and support network characteristics can either reinforce or dampen their efforts. Specifically, support seekers who are seeking information-related support receive more informational support when they display emotions of disbelief and yearning, while those seeking emotion-related support receive more emotional support when they display emotions of anger and depression. Moreover, receiving more informational support in the past would dampen the support seekers' current effort in seeking informational support, while receiving more emotional support in the past would enhance their current effort in seeking emotional support.The third essay extends the scope of online health support communities by developing a hazard model to examine the effect of social support on users' continued engagement in an online health support community. Engaging users in the community not only leads to better health outcome for each individual user (Coursaris & Liu, 2009; Yan & Tan, 2014), it is also critical for the success of the communities, as a large number of active members helps create activities, generates useful content, and serves as resources for other members (Butler, 2001). This paper seeks to examine users’ continued engagement in online communities based on whether the support they receive meet their needs and their roles in the support network. Users’ need for social support is a dynamic process as the users may need different types of support when they are at different stage of the disease or their lives. Using the same dataset from the HIV/AIDS community, the results show that receiving informational support increases the chance of leaving the community while emotional support increases the chance of staying. Users with emotions of disbelief and yearning are more likely to leave, while those with anger and depression are more likely to stay. In addition, for users who show emotions of disbelief and yearning, receiving informational support further increases their chance of leaving, as their questions and doubts are satiated. On the other hand, for users who show emotions of anger and depression, receiving emotional support decreases the chance of leaving as it creates emotional attachment between users. The next step is to conduct a social network analysis which examines the roles of the users in the support network. We hypothesize that those users who act as brokers in the network would have less chance of leaving. Brokers bridges the support providers and support seekers and are thus exposed to higher flow of social support. Brokers are also more actively engaged in the community and are valued for their mediating role between users of longer tenure and new-comers.

Community Building through Economic Opportunity: Entrepreneurship among Female Refugees in Buffalo, New York

Root, Laurel D State University of New York at Buffalo ProQuest D 2019 해외박사(DDOD)

Buffalo, New York is a national hub for refugee resettlement. Each year as a mid-sized city it welcomes some of the highest numbers of refugees in the country. The West Side is home to the city's largest concentration of refugees. The West Side is also home to the Westminster Economic Development Initiative (WEDI). Opportunities for entrepreneurship are offered to refugees by WEDI through a small business incubation space (the West Side Bazaar), business coaching, and low-interest microloans. Other organizations for encouraging entrepreneurial activity are present on the West Side, including Stitch Buffalo and Sew REDI (Refugee Economic Development Initiative). These organizations aid refugee women by teaching them a skill, in this case sewing. The women directly earn profit from the sale of their work. This in turn teaches economic empowerment and self-sufficiency. This dissertation examines the experiences of women refugee entrepreneurs from a variety of countries. These women had either been the recipients of training or microloans from WEDI or members of Stitch Buffalo or Sew REDI. Culture takes on a fluid manifestation for these women. Due to the event of relocation, many times leading to multiple relocations, individuals are exposed to a variety of cultural frameworks in which they must operate. Women often spend many years in refugee camps in multiple countries outside of their home countries. This creates a necessity for adaptation and renegotiation of culture and identity in order to survive. Because of the complicated intersection of cultural influences it makes it difficult, and nearly impossible, to define how individual cultures change. Instead, the women that have participated in the research display common themes related to the experience of what it means to be a woman refugee entrepreneur. These women come from all over the world, including the Middle East, Africa, and Southeast Asia.While there is ample literature on the areas of entrepreneurship and refugee studies separately, very little scholarship has explored the connection of these two topics, particularly within the context of the United States. This dissertation design is a multi-sited approach conducted at the West Side Bazaar and the sewing collectives. Data was collected through a research design with rigorous qualitative methods through participant observation, informal interviews, and formal interviews.

“Finding My Own Way”: Exploring the Academic Experiences of First-Generation College Students through Stories of Success

Pytlak-Surdyke, Marissa State University of New York at Buffalo ProQuest D 2020 해외박사(DDOD)

The purpose of this qualitative interview study is to explore the experiences of first-generation college students (FGCS) through a unique perspective, specifically, through stories of success. While the majority of research surrounding first-generation college students frames their experiences from a deficit perspective, this study highlights the experiences of FGCS who are successful completers of postsecondary education and are now pursuing graduate education in a variety of disciplines. The in-depth interviews of eighteen “successful” FGCS were analyzed to reveal the challenges, opportunities, motivations and successes these students experienced during their time as undergraduates at a very competitive, R1, university. Student narratives reveal their perceptions of how identifying as a FGCS has impacted their academic experience, the factors that helped facilitate their success, and the advice they would give to incoming FGCS. Additionally, tensions emerged as students negotiated their evolving identities as college students, which reveal gender, class and race implications. Findings are framed through three theoretical frameworks: Social Construction, Social Capital and Cultural Capital. Following the discussion of findings, practical implications and advice for first-generation college students, faculty and the University are offered.

A Graph-Time Signal Processing Approach for Modeling and Monitoring with Applications in Healthcare and Occupational Safety

Hajifar, Sahand State University of New York at Buffalo ProQuest D 2022 해외박사(DDOD)

Development of sensing and imaging technologies have provided industries with novel measuring systems. Two of the most noteworthy areas where measuring systems and analytics based on those systems are causing big changes are occupational environments and healthcare. In occupational environments, wearable sensors can assist workers in monitoring worker's vital signs or detect problems such as, fatigue. In healthcare, medical imaging systems have the potential to support medical diagnostics and evaluations. Statistically, the mentioned measurements can be modeled as a function of one or multiple independent variables and we refer them as signals. Classical signal processing models the measurements as a function of time and benefits from a wide variety of well-established tools. Recently, the emerging field of graph signal processing extended the classical signal processing tools to the graph domain, where signal is a function of node and resides on nodes rather than being measured along time. Most recently, time-vertex signal processing integrated classical signal processing and graph signal processing, and defined the signal as a function of time and node. In this dissertation, we aim to solve different problems from occupational safety and healthcare areas, capitalizing on classical signal processing, graph signal processing and time-vertex signal processing tools.First part uses time series analysis to present a participant-independent method that can be used to forecast ratings of perceived exertion (RPE) based on wearable sensors. In particular, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a walking-squatting-walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, four models were compared: autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of two or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute scaled error (MASE) < 0.76 and median MASE < 0.49 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker’s condition, and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.Second part focuses on liver pre-transplant quality evaluation using thermal images. Accurate evaluation of liver viability during its procurement is a challenging issue and has traditionally been addressed by taking invasive biopsies of the liver. Recently, people have started to investigate the non-invasive evaluation of liver viability during its procurement using liver surface thermal images. However, existing works include the background noise in the thermal images and do not consider the cross-subject heterogeneity of livers, thus the viability evaluation accuracy can be affected. In this paper, we propose to use the irregular thermal data of the pure liver region, and the cross-subject liver evaluation information (i.e., the available viability label information in cross-subject livers), for the real-time evaluation of a new liver's viability. To achieve this objective, we extract features of irregular thermal data based on tools from graph signal processing (GSP), and propose an online domain adaptation (DA) and classification framework using the GSP features of cross-subject livers. A multiconvex block coordinate descent-based algorithm is designed to jointly learn the domain-invariant features during online DA and learn the classifier. Our proposed framework is applied to the liver procurement data, and classifies the liver viability accurately.Finally, last part revisits the liver pre-transplant quality evaluation problem by expanding the second part to time-vertex signal processing and capitalizing on change-point estimation methods. In particular, a Generalized Autoregressive Conditional Heteroskedasticity procedure is used to estimate temperature variance related to different spots over the surface of the liver and results in time-vertex signals. Afterwards, a joint Fourier transform is applied to the time-vertex signals in order to extract spatiotemporal features. Finally, an online non-parametric change-point estimation method is utilized to estimate when the liver transitions from being viable to being unviable. The proposed framework is benchmarked against another approach in which the same change-point estimation method is applied to spatial features obtained using graph Fourier transform. Based on the results, the change-point estimated using spatiotemporal features is more consistent with literature than that of spatial features.In summary, in this dissertation, we demonstrate how signal processing, including classical signal processing, graph signal processing and time-vertex signal processing, can contribute to healthcare and occupational safety areas in a positive way. Firstly, in the occupational safety area, the ability to make accurate and personalized physical fatigue forecasts can inform the assignment and scheduling of appropriate interventions, which can eliminate or mitigate the negative health outcomes of fatigue on the worker, as well as the productivity losses, worker compensation costs, and high employee turnover experienced by the organization. Secondly, in the healthcare area, the ability to achieve accurate and personalized organ pre-transplant monitoring can decrease organ transplant rejection rate and overall waiting time in organ waiting list.In the future, our occupational safety work can be extended from a fatigue forecasting to a fatigue intervention perspective and its impacts on recovery needs could be studied. Also, in our organ evaluation studies, donor level information, including, but not limited to, alcohol consumption and tobacco use behaviors, can be incorporated in proposed models.

An Exploration of the Mental Health Literacy of School Resource Officers

Tamulonis, Jessica P State University of New York at Buffalo ProQuest D 2022 해외박사(DDOD)

This study explored the mental health literacy, training, and experiences of School Resource Officers (SROs) using an electronic survey composed of various questions informed by Curran et al. (2019) as well as Layton and Shaler’s (2019) research. SROs were asked questions pertaining to demographics, training experiences, roles, and responsibilities along with an adapted version of the Mental Health Literacy Scale (MHLS; O’Connor & Casey, 2015). A final sample of 421 SROs was obtained. Statistical analyses were performed, and several significant relationships were identified among hours of training, personal experiences with a person with mental illness (PWMI), and aspects of the SRO role with MHLS scores. Specifically, SRO training, personal experience with PWMI, female gender identity, and engagement with the “counselor role” were significantly and positively related to SROs’ mental health literacy. Information gleaned from SROs’ reported training experiences indicated that although being a member of the National Association of School Resource Officers (NASRO) did not predict higher MHLS scores. There was a positive relationship between SRO training hours and being a member of NASRO, suggesting that SROs affiliated with NASRO receive more training than those who are not. However, SROs in this study, regardless of NASRO membership, identified a need for increased training especially in mental health and disability. Lastly, this study provided support for use of the MHLS among SROs within the United States.

Investigating U.S. State-Level Income Inequality as a Determinant of Population Health: Theory, Evidence, and Directions Forward

Irish, Andrew Joseph State University of New York at Buffalo ProQuest D 2022 해외박사(DDOD)

Background: The income inequality (II) hypothesis (IIH) suggests that greater II negatively affects population health. Scholarly literature on the effects of economic inequality extends back millennia. Modern literature on the IIH as well as related but peripheral literatures are ongoing. These literatures are often not conversant and integrative. Several key questions remain open in IIH literature including how II is to be measured, what II represents, which outcomes II applies to, what theoretical effect pathways might mediate the relationship between II and health, and at which level(s) of analysis II is most meaningful. Each of these questions will be addressed, but the latter two are primarily of interest in this study. I attempt to glean insight into what the historical, peripheral, and IIH-specific literatures have to say generally, about effect pathways, and about levels of analysis, as well as to provide some new empirical evidence, each aimed at making improvements in our research moving forward.Methods: I undertake a review of historical and IIH-peripheral literatures. This has several aims. First, it is an assessment of whether the entire project is wrongheaded or on good footing. Second, it assesses what the key considerations made in this literature have been. Finally, it assesses this literature for insights about the theoretical pathway and level of analysis questions. I also undertake a review of IIH-specific literature with respect to the theoretical pathway and level of analysis question, but also reviewing empirical findings at the level of U.S. states, trying to assess overall support at this level. Finally, I analyze empirical data from two independent nationally representative datasets, across a range of health outcomes, contrasting multiple theoretically based model formulations, with sensitivity checks for variable formulation and II measurement, ultimately bringing new evidence to bear regarding the IIH at the state level.Results: Historical and peripheral literature strongly affirm that the IIH examination is not unreasonable and stands in line with prior thinking. This literature also tends to emphasize considerations of justice and strongly favors theoretical pathways in the domain of policy/politics. Individual level effects are also common considerations. Further, the level of the nation, especially historically, is far more widely considered than smaller regions or local levels. The IIH-specific literature more heavily considers the smaller regional levels such as U.S. states than other literatures and has little exploration of the policy/politics domain, instead favoring effects on groups or individuals. Theory in general has not been well-established and model construction inconsistencies abound. The empirical literature overall leans toward supporting the IIH at the state level, but not strongly. My empirical findings suggest that across 400 models, only slightly more than half (53%) support the IIH at the state level. This inconsistency in support held when looking at: any specific outcomes; from any of the three decades; and for alternate formulations of outcomes, alternate measures of II, and alternate model specifications.Conclusion: The new empirical findings presented in this study are not strongly supportive of the IIH at the state level. I provide empirical and theoretical arguments about why the state-level effect may be null or weak and why nation-level effects are empirically and theoretically likely to be stronger. I further suggest that based on the historical and peripheral literature, IIH-specific research would benefit from considering perceived injustice as a potential mechanism in the production of diminished health. Finally, considering the historical and peripheral literature as well as IIH-specific suggestions, I strongly suggest that the near omission of the policy domain from IIH research should be reversed. Theory more generally should be better established. We should not disregard compositional effects and should improve modelling and theory agreement across research which would more clearly entail falsification or support criteria for the IIH.

The Influence of State-Run Media on Civil War Severity

Anderson, Collin State University of New York at Buffalo ProQuest D 2019 해외박사(DDOD)

This study investigates the relationship between state-run media and the severity of civil wars. Drawing on literature and theories from a wide range of disciplines, this dissertation illuminates a largely understudied aspect of the conflict environment - namely, how the structure of the media environment and governmental involvement in media through propaganda or outright ownership shapes a domestic conflict. Much of the conflict literature, references signaling as an important part of the conflict process (Thyne 2012). However, the same literature tends to black-box the actual signaling environment itself away. I argue in this work that ignoring the media environment in a country is a mistake being repeated throughout conflict literature. The analysis conducted here is done in a threefold series of tests, each examining a different aspect of a civil war's life cycle. The first hypothesis tests the violence committed during a conflict by using an OLS regression to examine the correlation between casualties and press freedoms. The second stage uses a survival model to investigate the likelihood of achieving a cessation of hostilities. The final stage looks at the durability of negotiated peace settlements, employing a probit model to look at the likelihood of collapse at different stages of press freedoms. I find varying levels of support for all three of my hypotheses. Taken together, however, my results suggest that there is a link between the press environment and conflict structure.

Indoleamine 2,3-dioxygenase Regulates PD-1 Expression on CD8+ T Cells via Kynurenine Activation of the Aryl Hydrocarbon Receptor

Amobi-McCloud, Adaobi Ekenma State University of New York at Buffalo ProQuest D 2019 해외박사(DDOD)

Epithelial ovarian cancer (EOC) is the deadliest gynecologic malignancy in the United States. Despite initial response to surgery and platinum- and taxane-based chemotherapy, the majority of patients relapse and ultimately die from their disease within five years. Work by our group has shown that the presence of intra-epithelial CD8+ tumor-infiltrating lymphocytes is associated with favorable outcomes in EOC patients. However, suppressive mechanisms in the tumor microenvironment inhibit antigen-specific immunity, impacting tumor growth and metastasis. Our group has also shown that tumor-derived antigen-specific CD8+ T cells from ovarian cancer demonstrate impaired effector function and enriched expression of the inhibitory molecule programmed cell death protein 1 (PD-1). Moreover, we demonstrated that expression of the enzyme indoleamine 2,3-dioxygenase (IDO) in ovarian tumor correlates with poor prognosis and poor tumor infiltration by CD8+ T cells. However, a complete understanding of the cellular and molecular mechanisms of IDO-mediated suppression of T cells remains unclear, and is therefore the focus of my thesis work.As such, this dissertation focuses on IDO-mediated immune suppression in EOC. IDO is a major contributor to immune suppression within the tumor microenvironment of ovarian cancer and other solid tumors. It is known that IDO expressing tumor cells and myeloid cells mediate tryptophan deprivation leading to T cell cycle arrest, and production of the catabolite kynurenine, which mediates differentiation of CD4+ T cells into regulatory T cells via activation of the aryl hydrocarbon receptor (AHR). Because the IDO catabolite kynurenine is an endogenous ligand for AHR, we propose that there is a link between the IDO/PD-1/PD-L1 pathways via AHR activation by kynurenine. Therefore, to delineate the mechanisms by which IDO mediates immune suppression, we generated a stable IDO1-expressing EOC cell line by retroviral transduction of an aggressive murine ovarian cancer cells, and confirmed that the IDO1-expressing tumor cells expressed the murine IDO gene and that the gene product demonstrated functional IDO enzyme activity, as measured by elevated kynurenine production compared to empty vector controls tumor cells. We identified novel genome-wide chromatin accessibility in regulatory regions of the inhibitory molecule PD-1 in kynurenine-treated CD8+ T cells using Assay for Transposase Accessible Chromatin sequencing (ATAC-seq). We defined the transcriptional mechanism of kynurenine-mediated AHR activation via the interaction with regulatory regions in the promoter sequences of the inhibitory molecule PD-1.This dissertation fills a major gap in the knowledge of the contribution of IDO enzyme activity on the development of dysfunctional PD-1+CD8+ T cells in the ovarian cancer microenvironment. Here we demonstrate that tumoral IDO expression lead to profound changes in tryptophan, nicotinate/nicotinamide, and purine metabolic pathways in the ovarian tumor microenvironment, and to an increased frequency of PD-1+CD8+ tumor infiltrating T cells. We identified that activation of AHR by kynurenine induced PD-1 expression, and that this effect was significantly abrogated by the AHR antagonist CH223191. Mechanistically, we demonstrated that kynurenine modifies chromatin accessibility in regulatory regions of T cell inhibitory genes, allowing AHR to bind to consensus XRE motifs in the promoter region of PD-1. These results suggest that strategies targeting the IDO/KYN/AHR pathways will be advantageous in enhancing anti-tumor immunity in ovarian cancer.