Ph.D. Student • Machine Learning • Data Analytics
I build machine learning models that read the Sun's magnetic field as multivariate time series and predict the flares that follow.
Deep learning, time series mining, and the problem of learning from events that almost never happen.
Background
Hello! I am Onur Vural, a Ph.D. student and research assistant with expertise in machine learning, deep learning, and data analytics, particularly in the field of solar physics. My primary focus is on leveraging advanced machine learning techniques to analyze and predict solar phenomena, such as solar flares, using multivariate time series data.
My research spans representation learning, time series mining, and the development of novel algorithms for imbalanced datasets. I am passionate about using innovative computational methods to extract meaningful insights from complex datasets, enabling advancements in both science and technology.
When I'm not immersed in research, I enjoy collaborating on projects that push the boundaries of artificial intelligence and sharing my knowledge through talks, videos, and publications. Feel free to explore this website to learn more about my work, ongoing projects, and other contributions.
Published work
A transformer that injects learnable global tokens, cross-attending over the full sequence to surface the non-contiguous moments that actually carry flare signal.
A comparative analysis of vector, time series, and graph formulations of the same magnetic field data, measured against a shared benchmark.
Selects the most distinctive instance per class, then trains a temporal embedding module with a custom extreme reconstruction loss. Transfers to univariate and multiclass settings.
Contrastive objectives applied where the positive class is vanishingly rare, pulling like instances together and pushing the rest apart in the embedding space.
Talks
What solar observatories actually record, and how those recordings become a dataset.
Methods for classifying long, noisy, heavily imbalanced sequences.
Talks, walkthroughs, and paper explanations.
Press
University coverage of the group's work on predicting extreme solar events.
Conference talks, paper acceptances, and shorter updates.