I am a Research Engineer at the Institut d'Astrophysique Spatiale (IAS), Orsay, France, working on CMB cross correlations within the Euclid collaboration. I have 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 am particularly excited about teams that value clarity, reproducibility, and technically rigorous product development.

Download CV (PDF)

Education

Doctorate of Philosophy, Physics

Université Paris-Saclay · 2022 – 2025

PhD at Université Paris-Saclay with the CosmoStat Laboratory, CEA. Worked 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 versus box extracted spectra for robust pipeline validation.

GRB Search in INTEGRAL Time Series

Astrophysics Lab I · University of Geneva · 2019

Scripted data ingestion and preprocessing 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