Scientific Computing, Modeling & Simulation
Savitribai Phule Pune University

Research & Consultancy

Multidisciplinary expertise under one roof!

Collectively, SCMS-SPPU possesses expertise in diverse computational areas such as
  • Machine learning, statistical learning, data science
  • Statistical data modeling & analysis
  • Data-intensive decision support and research
  • Computational epidemiology
  • Scientific computing and large-scale numerics
  • Monte Carlo methods
  • Computational physics and materials modeling
  • Industrial and applied mathematics
  • Cryptography
  • Digital signal & image processing
  • Computational fluid dynamics
  • Defence ballistics
  • Free software
For consultancy enquires, please write to
«coordinator AT»

Broad research areas

Publications, preprints, technical reports

Materials Modeling
Image Courtesy: Bhalchandra Pujari

Computational Materials Science and Materials Modeling

Ab initio and other approaches (Vaishali Shah, Bhalchandra Pujari): Centre has an long-standing, intensive research programme in computational materials science Using the state-of-the-art techniques, electronic and atomistic properties are investigated to gain macroscopic insights from microscopic point-of-view. From graphene to quantum dots and from semiconductors to random alloys, the members of this group have a long history of working at the cutting edge of materials science.

Computational Epidemiology

Covid19 Modeling (Snehal Shekatkar, Bhalchandra Pujari): Please visit our Covid19 modeling and Pune 2020 forecast portals.
Covid19 Forecasts for Pune (2020)

Computational Geology
Image Courtesy: Bhalchandra Pujari

Computational Geology

(Bhalchandra Pujari, Mihir Arjunwadkar): Ab initio structure and property calculations of minerals (especially zeolites), meteor impact simulations, crater detection from lunar images.

Statistical Science, Data-Intensive Science, ML/AI

Astrostatistics, etc. (Mihir Arjunwadkar): Development (or: apt and meaningful use) of statistical and computational methodologies for data-intensive science. Current and past focus areas include genomic sequence analysis, areas of astrostatistics such as CMB data analysis, pulsar astronomy, compressed-sensing methods for radio astronomy, etc.

DSP/DIP & Machine Learning (Bhalchandra Gore): Automated identification of voice, gender, features, handwriting recognition for use in congitive detection systems.

Lunar crater detection (Bhalchandra Pujari)
Statistical Science
Image Courtesy

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