Vansh Bansal

prof_pic.jpg

vansh at utexas.edu

I am a second-year doctoral student in the Department of Statistics and Data Sciences at the Univesity of Texas at Austin, where I am fortunate to be co-advised by Alessandro Rinaldo and Purnamrita Sarkar. My research focuses on the intersection of high-dimensional statistics and deep learning, with an interest in developing computationally efficient sampling algorithms and applying them to key problems in statistical inference, uncertainty quantification, and fairness in machine learning models.

Previously, I completed my bachelor’s degree in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kanpur, where I had the privelege to be advised by Dootika Vats. I also had the opportunity to work with the amazing Piyush Rai, Vipul Arora and Ashutosh Modi.

Beyond work, I am an Indian classical vocalist and I enjoy going for hikes.

PS: I’d try my best to give my advice and feedback to those who are applying to graduate programs in statistics, machhine learning or related fields, particularly to those for whom this type of feedback would usually be unavailable. The best way to reach out to me is by email.

news

Jan 22, 2025 Our paper “Conditional diffusions for neural posterior estimation” got accepted at AISTATS 2025!
Jan 22, 2025 Our paper “2-Rectifications are Enough for Straight Flows: A Theoretical Insight into Wasserstein Convergence” got accepted at ICLR 2025!
Aug 21, 2023 Joined the Department of Statistics and Data Sciences, UT Austin as a PhD student.

selected publications

  1. ICLR 2025
    Straightness of Rectified Flow: A Theoretical Insight into Wasserstein Convergence
    Vansh Bansal, Saptarshi Roy, Purnamrita Sarkar, and 1 more author
    2024
  2. AISTATS 2025
    Conditional diffusions for neural posterior estimation
    Tianyu Chen, Vansh Bansal, and James G. Scott
    2024