Vivek Dhand
Vivek Dhand uses his background in pure mathematics to address complex real-world problems. He has led and contributed to several applied research projects involving data fusion, computer vision, and natural language processing. He strives to develop robust and explainable systems with transparency and accountability, in order to minimize bias and protect individual privacy.
Vivek received his Ph.D. in mathematics from Northwestern University. His research interests include representation theory, category theory, algebraic combinatorics, and visualizations of mathematical structures.
Session
Modern machine learning models typically utilize extremely high-dimensional feature spaces, which inhibits robustness and explainability. Finer-grained control over model training requires more powerful tools for observing and interacting with latent features as they evolve over time. In this talk, we give several examples of visualizations of nearest-neighbor graphs that illuminate common training pitfalls and provide practical insights for diagnosing model performance issues.