Graduate Events

A virtual information session on the graduate business programs at UMass Dartmouth. - Explore various business graduate programs - Find out how you can complete your degree at your pace - Discover how you can concentrate in a field that meets your interests and career goals - Learn about Charlton's more flexible GMAT waivers - Understand the value of Charlton College of Business degree - Hear about the next steps to enrollment This event designed to answer questions you may have about the various degree and certificate programs.

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Department of Fisheries Oceanography "Modeling Index Selectivity for Fishery Stock Assessments" By: Cole Carrano Advisor Steven X. Cadrin (University of Massachusetts Dartmouth) Committee Members Pingguo He (University of Massachusetts Dartmouth), Gavin Fay (University of Massachusetts Dartmouth), Lisa Kerr (University of Maine) Monday January 6th, 2025 10:00 AM SMAST East 101-103 836 S. Rodney French Blvd, New Bedford and via Zoom Abstract: Abundance indices are crucial components of fishery stock assessments because they provide a time series of relative abundance for estimating absolute stock size, derived from the response of relative indices to the absolute magnitude of fishery removals. Selectivity is the relative vulnerability to a fishery or fishery-independent survey for each species or demographic group within a species (e.g., size or age class). In an age-based assessment model, selectivity parameters are needed to relate observed stock indices to model estimates of abundance at age. Thus, selectivity estimates must be carefully modeled to ensure an accurate depiction of the stock's age structure. The objectives of this research are to improve the accuracy and utilization of indices in fisheries stock assessment models by understanding the effect of alternative approaches to estimating index selectivity. Chapter One provides a general introduction to the topic and a review of the relevant literature. Chapter Two involves splitting a fishery-independent survey into two series to account for vessel and methodological changes by estimating distinct catchability and selectivity parameters for each series. Results indicated improvement in model performance for stocks with sufficient contrast in the new index, and no improvement for stocks with limited years of data or contrast in the recent indices. Chapter Three develops fleet-structured assessment models to improve selectivity estimates for fishery and the fishery-dependent indices. Splitting catch into fleets improves selectivity estimates for respective CPUE indices, but robust catch-at-age data is desirable for fleets that make up a large portion of the total catch. Chapter Four involves simulation cross-testing as a method to evaluate performance of assessments that assume a single index series that is calibrated for changes in survey technology vs. assuming separate indices in stock assessment models. Results from this chapter suggest that the consequences of assuming a split when there truly wasn't one were not severe, but that assuming there wasn't a split when there truly was one can produce significant biases in model results This work examines how decisions about modeling fleet structure or changes in survey systems affect the performance of an assessment model and how sensitive models are to these decisions. This research will emphasize the importance of selectivity estimates to stock assessment and advance our understanding of how to effectively utilize abundance indices in an assessment model. ************ Join Zoom Meeting https://umassd.zoom.us/j/94890073016 Note: Meeting passcode required, email contact below to receive ************** To request the Zoom passcode or for any other questions, please email Callie Rumbut at c.rumbut@umassd.edu

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Topic: Leveraging AI and Physics-based Models for Solving MRI Inverse Problems Location: Zoom https://umassd.zoom.us/j/93562746288?pwd=K2mJgnGVyxrKODTHfdet26mPWd6H5v.1 Meeting ID: 935 6274 6288 Passcode: 860060 Abstract In the forward model of magnetic resonance imaging (MRI), the physical restrictions such as data undersampling, motion corruption, and inaccurate estimation of coil profiles cause aliasing and motion artifacts, low signal-to-noise ratio (SNR), and blurring effects in reconstructed image. State-of-the-art (SOTA) approaches to solving MRI inverse problems combine artificial intelligence (AI) and physics-based models, but they still have some limitations. The limitations include: (1) Aliasing and motion artifacts are intertwined and they are difficult to be separated and suppressed; (2) Pre-trained priors are ineffective for joint estimation of coil sensitivity and the reconstructed image; (3) Out-of-distribution problem arises in training MRI data; (4) Planning has not been explored in the context of solving the MRI inverse model; (5) There is a lack of a general prior to address multiple degradation factors in the forward model. This doctoral proposal presents six key contributions. First, an untrained neural networks (UNN) model has been proposed for Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) MRI reconstruction to suppress blurring and aliasing, which incorporates physical priors. Building on this UNN, a new attention-based architecture comprising of spatial and channel attention for UNN has been developed to accelerate MRI reconstruction. Second, a novel synthetic blade augmentation technique is applied for the first time in a deep unrolled network for enhanced PROPELLER MRI reconstruction, which also introduces a novel synthetic blade generation process. Third, an ensemble-based approach is proposed to address multiple types of motion artifacts in MRI by employing ensemble of three distinct Cycle Consistent Generative Adversarial Networks (CycleGANs). Fourth, two novel priors are introduced to improve the joint sensitivity encoding (JSENSE) approach by incorporating accurate coil profile estimation within an iterative optimization framework. Building on the limitations of the two priors, a general unified prior, which is based on ensemble framework is proposed for joint sensitivity encoding to address multiple degradation factors. Fifth, AI based planning, traditionally not considered in this field, has been preliminarily studied and will be further explored in the dissertation. Finally, instead of using a specialized prior for MRI reconstruction, a general prior will be investigated to solve multiple degradation factors in the inverse model. The proposed methods will be validated through comparisons with SOTA approaches and qualitative assessments by MRI physicists. It is anticipated that these methods will advance MRI inverse problem solving and enhance MRI applications in clinical settings. Advisor: Dr. Yuchou Chang, Department of Computer and Information Science Committee Members: Dr. Haiping Xu, Department of Computer and Information Science Dr. Long Jiao, Department of Computer and Information Science Dr. Donghui Yan, Department of Mathematics For further information please contact Dr Yuchou Chang at ychang1@umassd.edu

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A virtual information session on the graduate business programs at UMass Dartmouth. - Explore various business graduate programs - Find out how you can complete your degree at your pace - Discover how you can concentrate in a field that meets your interests and career goals - Learn about Charlton's more flexible GMAT waivers - Understand the value of Charlton College of Business degree - Hear about the next steps to enrollment This event designed to answer questions you may have about the various degree and certificate programs.

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Title: Optimizing Datasets for Lyme Disease Detection Advisor: Iren Valova PhD, Associate Dean - College of Engineering - Professor, Computer & Information Science - University of Massachusetts Dartmouth Committee: Gokhan Kul PhD, Computer & Information Science - University of Massachusetts Dartmouth Firas Khatib PhD, Computer & Information Science - University of Massachusetts Dartmouth Date: Jan 17, 2025 Time: 1pm Location: Zoom https://umassd.zoom.us/j/98403102776?pwd=VKmd3RikQZbqdTkhOaIhoJdyXQE91k.1 Abstract: This thesis focuses on optimizing image datasets through augmentation methods for the detection of Lyme disease. Lyme disease often is accompanied by an erythema migrans rash, but other sorts of rashes could look similar to it. Using a public crowdsourced dataset, the object is to improve the accuracy of YoloV7 through image enhancements and augmentations. The study utilizes a combination of data preprocessing techniques, including CLAHE, photometric deformation, elastic deformation, and mixup to improve image quality and address dataset imbalances. YoloV7, an object detection model was trained on the enhanced dataset to accurately differentiate Lyme-related rashes from other dermatological conditions. The results favored the CLAHE result over the others. This work contributes to the development of more reliable, automated diagnostic tools for individual user. For further information contact Dr. Iren Valova at ivalova@umassd.edu

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A virtual information session on the graduate business programs at UMass Dartmouth. - Explore various business graduate programs - Find out how you can complete your degree at your pace - Discover how you can concentrate in a field that meets your interests and career goals - Learn about Charlton's more flexible GMAT waivers - Understand the value of Charlton College of Business degree - Hear about the next steps to enrollment This event designed to answer questions you may have about the various degree and certificate programs.

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A virtual information session on the graduate business programs at UMass Dartmouth. - Explore various business graduate programs - Find out how you can complete your degree at your pace - Discover how you can concentrate in a field that meets your interests and career goals - Learn about Charlton's more flexible GMAT waivers - Understand the value of Charlton College of Business degree - Hear about the next steps to enrollment This event designed to answer questions you may have about the various degree and certificate programs.

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