Office of Undergraduate Research

Permanent URI for this communityhttp://hdl.handle.net/1903/20157

Emphasizing equitable and inclusive access to research opportunities, the University of Maryland's Office of Undergraduate Research (OUR) empowers undergraduates and faculty to engage and succeed in inquiry, creative activity, and scholarship. This collection includes materials shared by undergraduate researchers during OUR events. It also encompasses materials from Undergraduate Research Day 2020, Undergraduate Research Day 2021, and Undergraduate Research Day 2022, which were organized by the Maryland Center for Undergraduate Research.

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Start date: 2020End date: 2024

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Now showing 1 - 10 of 296
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    Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field And CNNs for Stock Return Predictions
    (2024) Bapanapalli, Abhinav; Mahmud, Ateef; Nadig, Chiraag; Thakker, Krish; Jabeen, Shabnam
    Predicting stock price movements is a complex challenge faced by many traders and analysts. Our research leverages Quantum Gramian Angular Field (QGAF) transformations combined with Convolutional Neural Networks (CNNs) to classify stock price trends as "up" or "down." By transforming 1D time-series stock data into 2D images, we enable CNNs to extract features more effectively, showcasing the potential of quantum machine learning in financial forecasting.
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    Quantum Optimization for Solving NP-Hard Problems
    (2024) Dayal, Arnav; Kalidindi, Raghava; Kosuru, Sohan; Moosavi, Miles; Jabeen, Shabnab
    The University of Maryland has a lot of resources that it seeks to ensure every student has easy access to, ranging from facilities like Wi-fi to basic safety measures such as streetlights. Ensuring these resources are properly distributed amongst campus can grow to be expensive considering the University’s 1,339-acre estate. This optimization algorithm aims to minimize the resources necessary to ensure the entirety of any given area is fully encompassed by whatever facility the user desires. Quantum optimization is the ideal way to accomplish this task as classical optimizers are unable to provide as efficient of a solution due to the risk of getting trapped in local minima and the significantly weaker processing ability. The poorer performance of the classical optimizer is demonstrated in our results.
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    The Truth of Racial Bias in Pulse Oximetry
    (2024-12-09) O'Neill, Caitlin; Sanghavi, Mahi; Bhutani, Arshnoor; Bommareddy, Yasaswini; Kramarczuk, Kristina
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    The Quantum Zeno Effect
    (2024) Haswell, Meg; Ramanathan, Nithika; Ketner, Hannah; Jabeen, Shabnam
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    Data Augmentations on Quantum Wasserstein Generative Adversarial Networks
    (2024-12-11) Lee, Joey; Lai, Devon; Banerjee, Ayan; Jabeen, Shabnam
    The goal of this project is to explore Quantum Wasserstein Generative Adversarial Networks (QWGANs) and address its limitations by incorporating data augmentation techniques such as Elastic Transforms and Gaussian/Poisson Noise to simulate real-world imperfections, such as noise and distortions. With this we test the robustness of the QWGAN framework and compare QWGAN performance with such data modification techniques against one another
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    Hybrid Quantum Vision Transformers for Particle Image Classification
    (2024) Christopher, Darwin; Mahendran, Smithi; Shah, Saloni; Tanjore, Sid; Jabeen, Shabnam
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    One-Pot Ligation LAMP Assay to Detect miRNA-222: A Glioma Biomarker
    (2024) Pallavajjala, Roshni; Adane, Yedidya; Fernandes, Catarina; Kaiser, Jillian; Patel, Khushi; Spirito, Catherine
    Many cancer diagnostic methods are invasive, time-consuming, and expensive. Delayed cancer diagnosis can lower patient survival rates. PCR-based techniques that detect miRNA biomarkers in blood have been utilized as early screening tools for various cancers. As an alternative to PCR, we designed and optimized an isothermal amplification technique, Ligation Loop-Mediated Isothermal Amplification (Ligation LAMP) assay, to detect miR-222, an established biomarker that is found in elevated levels in the bloodstream of early-stage glioma patients. We designed colorimetric and fluorescent Ligation-LAMP assays and demonstrated their specificity and sensitivity in detecting miR-222. We are working on implementing our assay into a One-Pot system, using Thermally Responsive Alkane Partitions (TRAPs) and a strand displacement assay utilizing magnetic beads. We found that the Ligation LAMP assay is sensitive and specific to glioma biomarker miR-222 and different probe lengths for Strand Displacement did not have a significant impact on ligation. With these results, we can further improve the One-Pot assay to be more point-of-care.
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    The Role and Perceptions of Artificial Intelligence in Business Analytics
    (2024-12-09) Gupta, Sanya; Parekh, Mann; Kramarczuk, Kristina
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    AI Bias in Facial Recognition Systems
    (2024) Metukuru, Akhil; Movva, Vineeth; Sun, Rick; Kramarczuk, Kristina
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    EL33T H4X0R5?: Fearmongering and Biases in Cybercrime Journalism
    (2024) Zutchi, Aria; Hao, Emily; Le, Linh; Liu, Sydney; Kramarczuk, Kristina