Biography
I am a postdoctoral researcher at the University of Luxembourg. Currently, I am working on developing machine learning and reduced-order modeling frameworks for wind engineering and CFD problems.
I was an MSCA fellow at the institute of mathematics at the Technical University of Berlin, where I received my Ph.D. in applied mathematics. My thesis was a cross-disciplinary work, where I developed complexity reduction methods using machine learning algorithms for financial risk analysis.
I have an interdisciplinary background, a bachelor’s in mechanical engineering, a master’s in computational science focusing on computational methods and high-performance computing, and I am currently working in the field of numerical mathematics. Additionally, I am highly skilled in Python and MATLAB.
My primary research interest is in applied mathematics, and specifically, I find problems in differential equations, numerical analysis, reduced modeling, stochastic analysis, and machine learning interesting.
- Numerical Mathematics
- Computational Mathematics
- Machine learning
- Model Order Reduction
- Computational Fluid Dynamics
- Computational Finance
- Stochastic Analysis
- Optimization
- High performance computing
-
PhD in Mathematics, 2022
Technical University of Berlin
-
MSc in Computational Science and Engineering, 2018
University of Rostock
-
BEng in Mechanical Engineering, 2015
University of Pune
Skills
Advanced
Advanced
Advanced
Intermediate
Advanced
Intermediate
Experience
Responsibilities:
- Reduced order modeling and machine learning for computational fluid dynamics (CFD).
- Testing developed tools for wind flow over urban location based on the experimental and CFD datasets.
- Surrogate modeling optimization for faster computations.
- Wind energy harvesting and urban comfort studies.
Responsibilities:
- Parametric model order reduction for financial risk analysis.
- Analysis of stochastic differential equations (SDE) in finance.
- Numerical methods for nonlinear partial differential equations (PDEs).
- Implementation of CFD techniques in finance.
- Artificial neural network for faster computations.
Responsibilities:
- Designed thermoelectric generators (TEGs) for energy autonomous implants for medical applications.
- Computational methods for nonlinear PDEs in fluid dynamics and thermodynamics.
- Thermal and electrothermal simulations of TEG.
- Implemented nonlinear model order reduction for faster computation.
Responsibilities:
- Tutor two courses as a tutor involving numerical methods for PDEs, linear algebra, and compact modeling (model order reduction).
- Supervised and guided students on their software projects.
- Designed and conducted exams.
Featured Publications
This paper presents the error analysis of the model order reduction (MOR) framework developed in https://doi.org/10.1186/s13362-021-00105-8