Mandar H. Chandorkar


The generic can be more intense than the concrete. - Jorge Luis Borges

I’m a scientist and engineer currently based in Rotterdam, the Netherlands.

Currently, I work as a Data Scientist at Connecterra B.V, a dairy-tech scale up in Amsterdam. During my current tenure, I gained valuable experience in:

  • End-to-end machine learning:
    • Analysis, prototying and proof of concept for new models / services.
    • Building ML experimentation software stacks.
    • Large scale data preparation and model evaluation pipelines, going beyond text-book metrics.
    • Engineering real-time services serving ML models.
  • tinyML model optimisations such as quantisation, enabling power savings on edge deployments.

Previously I used to be a researcher at the CWI Amsterdam and the TAU group at the INRIA Paris-Saclay. During my PhD days, my research interests broadly covered machine learning applications in physical systems. I am interested in all the interesting problems that fall in between, such as, forecasting, simulation, uncertainty quantification, and system identification.

In my free time, I enjoy literature; fiction and (sometimes) non-fiction, and expanding my horizons with music from around the world.


Dec 1, 2019 Started working at Connecterra B.V :sparkles: :smile:
Nov 14, 2019 Defended PhD at the TU Eindhoven

selected publications

  1. AGU
    Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach
    Chandorkar, MCamporeale, E, and Wing, S
    Space Weather 2017
  2. Chapter 9 - Probabilistic Forecasting of Geomagnetic Indices Using Gaussian Process Models
    Chandorkar, Mandar, and Camporeale, Enrico
  3. ICLR
    Dynamic Time Lag Regression: Predicting What & When
    In International Conference on Learning Representations 2020