Vilasini Tinnaneri Sreekanth

I am a data scientist and research engineer with a strong background in machine learning, probabilistic modelling, scientific computing, and high-performance Python. I enjoy turning complex problems into clean, well-engineered solutions whether it involves building data pipelines, designing modelling frameworks, or developing scalable inference systems.

I thrive in roles where I can combine analytical thinking with hands-on engineering, and I am motivated by opportunities that blend ML, simulation-free modelling, numerical optimisation, and real-world data. I’m particularly excited about teams that value clarity, reproducibility, and technically rigorous product development.

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Education

Doctorate of Philosophy - Physics

Université Paris-Saclay · 2022 – 2025 [Expected]

PhD at Université Paris-Saclay with the CosmoStat Laboratory, CEA. Working on simulation-free cosmological inference using wavelet-based higher-order statistics, PDF modelling, and generative models for weak lensing.

Master of Science - Astrophysics

University of Geneva · 2019 – 2021

MSc in Astrophysics with a thesis on relativistic N-body simulations of global cosmic strings using Gevolution, focusing on defect signatures in large-scale structure.

Integrated MSc - Physics

SVNIT Surat · 2014 – 2019

Five-year integrated MSc in Physics with coursework in mathematics, computing, astrophysics, and data analysis. Final master thesis on the formation of dark matter halos in quintessence models.

Research Experience

Likelihood-free Inference with Higher-Order Statistics

PhD Research · CosmoStat, CEA Paris-Saclay · 2022–2025

Developed a Large Deviation Theory-based framework predicting the wavelet ℓ₁-norm for weak-lensing convergence maps, providing an analytical alternative to heavy simulations. Built a likelihood-free cosmological inference pipeline combining theoretical predictions, generative models, and HPC-scale map generation.

Simulations of Global Cosmic Strings

Master Thesis · University of Geneva · 2020–2021

Simulated the evolution of global topological defects and studied their impact on large-scale structure. Combined LATField2 and Gevolution in an automated HPC workflow with batch submission, monitoring tools, and parallel post-processing of scalar modes.

Optimal Extraction of HST Spectra

Astrophysics Lab II · University of Geneva · 2020

Implemented the Horne (1986) optimal extraction algorithm in Python for HST spectra, optimising vectorised operations. Built automated quality checks comparing optimal vs. box-extracted spectra for robust pipeline validation.

GRB Search in INTEGRAL Time Series

Astrophysics Lab I · University of Geneva · 2019

Scripted data ingestion and pre-processing for SPI-ACS light curves from INTEGRAL. Designed peak-detection algorithms to identify GRB candidates, using custom Python analysis scripts for large time-series datasets.

Dark-Matter Halos in Quintessence Models

Master Thesis-1 · University of Trieste · 2019

Modified the PINOCCHIO code to explore dark-matter halo formation in quintessence cosmologies with varying $w_0$ and $w_a$. Generated halo catalogues and performed cluster cosmology validation using statistical analysis and visualisation tools.

Research Internship - IISER Mohali

May 2017 - Supervisor: Prof. Jasjeet Singh Bagla

Worked on cosmological simulations of Quintessence dark energy models, exploring how the scalar-field dynamics alter the expansion history and composition evolution of the Universe. Gained early exposure to cosmology, numerical methods, and large-scale structure.

Skills

Modeling & Inference

Statistical modelling Probabilistic inference Simulation-based inference Generative modelling Uncertainty quantification Wavelet-based features

Data & Experimentation

Exploratory analysis Benchmarking Reproducibility Large simulations Image-like data

Programming

Python NumPy / SciPy pandas scikit-learn JAX PyTorch Git / GitHub Linux SLURM / HPC

Communication

Documentation Talks & posters Mentoring Collaboration

Publications

• Theoretical wavelet ℓ₁-norm from one-point probability density function prediction , Vilasini Tinnaneri Sreekanth, A. Barthelemy, S. Codis, J.-L. Starck, Astronomy & Astrophysics, 2024.
• Generative modeling of convergence maps based on predicted one-point statistics , Vilasini Tinnaneri Sreekanth, J.-L. Starck, S. Codis, Astronomy & Astrophysics, accepted (2025).
• Euclid preparation: Towards a DR1 application of higher-order weak lensing statistics , Euclid Collaboration, S. Vinciguerra, F. Bouché, N. Martinet, et al., incl. Vilasini Tinnaneri Sreekanth, arXiv:2510.04953, 2025.
• Benchmarking Theoretical Wavelet ℓ₁-Norm Predictions Against Cosmological Simulations , A. Tersenov, T. S. Vilasini, J.-L. Starck, S. Codis, M. Kilbinger, in preparation for Astronomy & Astrophysics.

Conferences, Schools, and Talks

• Euclid France Theory and Likelihood Workshop (IAP Paris, 2022)
• Euclid France Meeting 2022 (IAP Paris)
• XV Tonale Cosmology Winter School (Italy, 2022)
• Future Cosmology - IESC Cargese (Poster, 2023)
• ADA X Summer School (Crete, 2023)
• Action Dark Energy Colloque (Talk, 2023)
• TOSCA Reunion Meeting (Talk, 2023)
• Euclid Symposium 12 (Talk, 2024)
• Cosmology & Statistics Days (Talk, 2024)
• Euclid SWG WL Meeting (Talk, 2024)
• COSMO21 (Talk, 2024)
• Euclid Consortium Meeting (Rome, 2024)

Awards & Scholarships

• Excellence Master Fellowship - University of Geneva