Description: An in-depth approach to detecting ink in ancient manuscripts using convolutional neural networks.
Description: Predicting RNA sequence behavior using advanced neural network architectures to unravel the mysteries surrounding RNA's behavior and stability.
Description: Predicting how small molecules change gene expression in different cell types, with potential applications in drug discovery and basic biology.
Description: A self-driven research initiative focusing on predicting cardiovascular diseases using diverse machine learning models. The project stands out for its independent creation and preprocessing of a dataset, derived from combining two primary sources, and its emphasis on model optimization for the highest predictive accuracy.
- Project Notebook
- UCI Machine Learning Repository - Heart Disease Dataset
- Kaggle - Heart Disease Dataset by YasserH
Description: Detecting the presence of LLM generated content in essay's written by middle and highschool students.
Description: Developed a model trained on the world's most extensive ovarian cancer dataset of histopathology images obtained from more than 20 medical centers to classify ovarian cancer subtypes.
- Project Notebook TBD
- Competition Overview
- Dataset
Description: Detect and classify seizures and other types of harmful brain activity by developing a model trained on electroencephalography (EEG) signals recorded from critically ill hospital patients.
- Project Notebook TBD
- Competition Overview
- Dataset
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Vesuvius Challenge: Successfully deciphered ancient scrolls by designing and implementing a custom 3D convolutional neural network.
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RNA Kaggle Challenge: Pioneered a deep learning solution for predicting RNA sequence behavior, attaining an accuracy of ≈70%.
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Cardiovascular Disease Prediction: Spearheaded an independent research initiative by curating a unique dataset from multiple sources.
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Detecting AI Generated Writing: tbd
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Deep Learning Frameworks: Leveraged the power of TensorFlow, Keras, and PyTorch to design state-of-the-art neural networks tailored to specific research challenges.
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Data Analysis and Visualization: Employed Pandas for comprehensive data manipulation, alongside Matplotlib and Seaborn for insightful data visualizations, ensuring clear data insights at every project phase.
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Machine Learning and Numerical Computation: Relied on Scikit-learn for model selection and optimization, and utilized NumPy for numerical operations, ensuring robust and efficient data processing.
I'm always open to new challenges, collaborations, and learning opportunities. Feel free to connect with me on LinkedIn. I appreciate feedback and am always eager to engage in meaningful discussions that propel the boundaries of technology and research.