Gurjeet Sangra Singh

Ph.D student at the University of Geneva (Switzerland)
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Geneva, Switzerland

About me

Hi! I am a Ph.D student from the University of Geneva at the DMML group, under the supervision of prof. Alexandros Kalousis and prof. Stephane Marchand-Maillet.

Previously during my Master’s degree in Data science (University of Padova), I joined the Machine and Human Intelligence (University of Helsinki) under the supervision of prof. Luigi Acerbi.
During this period, I worked on my master’s thesis for developing a Python version of BADS (Bayesian Adaptive Direct Search, Acerbi and Ma, 2017), called PyBADS (Singh and Acerbi, 2023). The work also involved the investigation of the algorithm convergence towards stationary points of non-smooth stochastic functions.

Research Interest

I am fascinated by Probabilistic models and Bayesian inference methods.
My research focuses on Simulation-Based Inference (SBI) and spans Generative Models and Optimization problems.

Research topics of interest:

  • Likelihood-free inference
  • Stochastic Optimization
  • Probabilistic Numeric
  • Bayesian Inference techniques
  • Deep Learning

News

Jun 11, 2023 PyBADS 1.0 is out on PyPi!
A Python package for fast & robust black-box optimization, with applications to model fitting.
The preprint of the software is available to the following arXiv link: PyBADS: Fast and robust black-box optimization in Python.
More information can be found on the GitHub repo.

Selected publications

  1. PyBADS: Fast and robust black-box optimization in Python
    Singh, Gurjeet Sangra, and Acerbi, Luigi
    arXiv 2023