The Data Science Graduate Student Seminar Series is an informal weekly gathering of NYU Data Science graduate students (though not exclusively!), in which a speaker presents a topic of their choosing i.e. their own research or just a paper they find interesting. The presentation is meant to last 45-60 minutes, with additional time for conversation and questions. For the Spring 2023 semester, the series meets Tuesdays from 2-3pm in Room 204 at CDS.
The lunch seminar is organized by CDS PhD student Aram-Alexandre Pooladian. Please contact Aram if you are interested in giving a talk.
Spring 2023 Seminars
- 3/21: Sanae Lofti, “PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization”
- 3/7: Harvineet Singh, “Fair and Robust Machine Learning”
- 2/28: Aram-Alexandre Pooladian, “Estimating optimal transport maps with entropic regularization”
Spring 2022 Seminars
- 2/18: Aram-Alexandre Pooladian, “Entropic Estimation of Optimal Transport Maps”
- 2/25: Carles Domingo-Enrich: “Tighter Sparse Approximation Bounds for ReLU Neural Networks”
- 3/4: Zhouhan Chen: “Information Tracer — an automatic framework to continuously monitor multi-platform information spread”
- 3/11: Taro Makino: “Generative multitask learning mitigates target-causing confounding”
- 3/25: Sayam Kapoor
- 4/1: Lily Zhang
- 4/8: Sanae Lotfi