🏛️ Hausdorff Trimester Program - Geometric Statistics 2026
Participating in prestigious dual trimester program at Hausdorff Research Institute for Mathematics (HIM) focusing on geometric statistics, theory, applications, and computation.
Experience
Participant, Summer School
London Geometry and Machine Learning (LOGML), Imperial College London
Attended intensive summer school on geometric deep learning covering graph representation learning, equivariant machine learning, algebraic geometry in deep learning, and physics-informed graph neural networks. Collaborated on group project exploring canonicalization of 3D molecular point clouds with focus on symmetry-aware preprocessing for molecular geometry learning.
Machine Learning Engineer
Stellantis, Europe
Designed and deployed end-to-end MLOps pipelines using PySpark, MLflow,
and Databricks. Optimized large-scale data processing workflows for
performance and scalability, and collaborated with cross-functional teams
to productionize ML models.
Business Intelligence Intern
Amazon, India
Reduced forecast processing time by 50% through optimization and AWS
deployment. Built analytics tools using EC2, Streamlit, SageMaker,
and QuickSight.
Data Scientist
ZestMoney, India
Developed credit risk, default prediction, and customer lifetime value
models in the BNPL domain. Worked closely with Credit and Risk teams
on automated decision-making.
Education
PhD Student in Geometrical and Topological Deep Learning.
FSTM, University of Luxembourg
Research in Geometrical and Topological Deep Learning and its application in interdisciplinary domains.
Aspects of under the supervision of Professor Jun Pang.
Dual Trimester Program - Geometric Statistics
Hausdorff Research Institute for Mathematics (HIM), Bonn University, Germany
Participated in intensive trimester program on “Geometric Statistics: theory, application, and computation” featuring schools, workshops, and conferences including “Stochastic Analysis, Statistics, and Computation on Manifolds and Singular Spaces” and “Geometry, Topology, and Learning on Smooth and Singular Spaces.” Engaged with leading mathematicians and researchers at the forefront of geometric and topological methods in mathematics.
Double MSc in Data Science and Business Analytics.
Department of Data Science, CentraleSupélec & ESSEC Business School, Paris, France
CGPA: 15.35/20. Studied machine learning and deep learning models including MLPs, CNNs, and GNNs and theri mathematical foundations. Completed a thesis on the expressive power of GNNs and their applications in drug discovery, molecular dynamics simulations, and combinatorial optimization,
interdisciplinary ml projects in Finance, Marketing.
Bachelor of Engineering minior in Mathematics.
Department of Computer Science & Engineering, Indian Institute of Technology (IIT) BHU, Varanasi, India
CGPA: 8.60/10. Coursework included Machine Learning, Optimization, FEM,
and Mathematical Modeling. B.Tech project on Landslide Susceptibility
Modeling using ML.
Skills & Hobbies
Technical Skills
Python
Machine Learning
Deep Learning
Graph Neural Networks
PySpark
MLflow
Databricks
AWS
Research & Communication
Academic Writing
Scientific Communication
Interdisciplinary Research
Awards
2nd Place Award
CentraleSupélec Data Science Kaggle Competition ∙
February 2022
Link Prediction in Citation Networks using Graph Neural Networks. View Winning Solution
Languages
100%
English
Fluent
100%
Hindi
Native
40%
French
BasicRecent Experiences & Workshops
Latest updates from workshops, summer schools, and research experiences.
📐 London Geometry and Machine Learning (LOGML) Summer School 2026
Attended prestigious summer school at Imperial College London covering geometric deep learning, graph neural networks, and equivariant machine learning.
🌐 Attending AWS Summit Paris 2024
A glimpse into my experience attending AWS Summit Paris with the Stellantis team and exploring the European tech ecosystem.
🌐 AWS Summit Paris 2024
Experience attending AWS Summit Paris with the Stellantis team and exploring the European tech ecosystem.