Hello, I’m Sarah Amiraslani. Driven by deep curiosity and a passion for impactful work, I am committed to building innovative solutions and forming lasting professional relationships. I thrive on learning new technologies and challenging myself to reach new heights.
I hold a Bachelor’s degree in Cognitive and Behavioral Neuroscience from the University of California, San Diego, and a Master of Science in Applied Data Science from the University of Michigan, Ann Arbor. My diverse skills in analytics and machine learning have significantly impacted various industries, including academic research, manufacturing technology, and banking.
I am a recent graduate and currently seeking early career opportunities in Quantitative Analytics, Data Science, and Machine Learning.
Selected projects
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Predicting Sunspot Activity with Machine Learning
This project leverages ensemble and deep learning models to forecast sunspots and Heliospheric Current Sheet (HCS) indexes, enhancing the prediction of solar wind structures.
Technologies used: ARIMA, Seasonal naïve, LTSM, Prophet, AR-Net, Extra Tree
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Tracing the Origins of Solar Wind with Machine Learning
This project uses in-situ measurements and unsupervised learning to cluster solar wind with similar properties and map it to its coronal origins, aiming to enhance predictions of heliospheric phenomena through machine learning.
Technologies used: Unsupervised Learning, PCA, t-SNE, DBSCAN
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Predicting Top 10 Formula 1 Finishes and Clustering Race Tracks
This project applies supervised learning to predict top 10 race finishes, pinpointing key factors that influence these outcomes. Additionally, it employs unsupervised learning to cluster race tracks based on characteristics like layout and surface, offering novel insights into racing strategies and optimizing performance.
Technologies used: Random Forest, Logistic Regression, Neural Networks, DBSCAN
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Draw to Learn: A Strategy to Learn Abstract Concepts
This project examines the effectiveness of student-generated drawings in learning abstract science concepts. By comparing various visualization strategies and important covariates in a lab setting, we explore how different methods impact retention and understanding of complex topics like black holes.
Technologies used: Experimental Design, Multivariate Analysis, General Linear Models
Latest blog posts
| Jul 22, 2024 | Exploring Breaking Bad Relations |
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| Jun 28, 2024 | Unraveling Complexity: Network Science Applied to Star Wars Films |