Education

University of Calgary, Canada

May 2021 - June 2025

Advisor: Dr. Artem Korobenko
Advancing the in-house wind turbine simulation tool for faster and accurate wind farm simulations using ALM for environmental flows. Investigating the role of atmospheric stratification on wind turbine wake in different layouts.
Developed and integrated projection-based reduced-order modeling utility for in-house fluid flow solver written using FORTRAN and OpenMPI for turbulent flow simulations. Developing neural network based efficiency enhancement for reduced order models using PyTorch and Tensorflow. Exploring non-intrusive techniques like DMD and PINNs.
Consistent use of HPC clusters available locally (ARC UCalgary) and nationally (Digital Research Alliance of Canada) to run simulations ranging from few thousand elements to couple million elements. Employed remote visualization techniques.
Utilized a range of specialized software tools, including ANSA and Rhino for complex meshing, VisIt and ParaView for CFD post-processing and MATLAB and Python for data analysis.

March 2023 - August 2023

Advisor: Dr. Gianluigi Rozza
Conducted collaborative research with leading researchers through the Mitacs Globalink research award. Acquired in-depth knowledge of reduced order modeling methodologies and implemented a reduced order modeling framework.
Explored interdisciplinary research topics on applied mathematics with peers in the field of uncertainity quantification (UQ), scientific machine-learning (SciML) and fractional calculus among others.
Attended seminars and courses from established professors and learnt basic concepts of new and upcoming simulation tools like RBniCS and PINA.

June 2023 (Received 5 ECTS credits)

Teachers: Dr. Geoge Em Karniadakis & Dr. Khemraj Shukla
Completed a two-week intensive summer school on Physics-Informed Neural Networks (PINNs) at KTH Royal Institute of Technology, earning 5 ECTS credits (or 125 study hours). Gained hands-on experience with TensorFlow, PyTorch, and JAX, with applications to scientific machine learning and PDEs.
Collaborated with international researchers, resulting in a conference paper and poster presentation at an AI-focused workshop on differential equations in science.
Developed advanced skills in implementing PINNs for various applications, including UQ and multi-GPU training, utilizing tools like DeepXDE and NVIDIA Modulus.

August 2018 - December 2020

Advisor: Dr. Clement Kleinstreuer
Developed a hybrid Computational Fluid-Particle Dynamics (CFPD) and Physiologically Based Pharmacokinetic (PBPK) model to predict drug transfer from the nasal cavity to the bloodstream.
Demonstrated high accuracy of the model with a deviation of less than 7% from in vivo data for various corticosteroids, providing robust predictions for drug absorption and plasma concentrations.
Explored the impact of drug solubility and partition coefficients on plasma concentration, showcasing the model’s ability to inform nasal drug formulation and dosage optimization.
Conducted simulations by successfully integrating fluid-particle dynamics with pharmacokinetics for patient-specific intranasal drug delivery analysis.
Utilized specialized software tools, including OpenFOAM as a CFD solver and ANSYS-ICEM to mesh nasal and lung geometries.

August 2014 - May 2018

Minor in Finance
Undergrad research advisor: Dr. Sanjay Jain
Advanced the low-speed wind tunnel at the university by installing new smoke visualization techniques.
Conducted computational based research as undergrad final project on the topic “Design and efficiency of centrifugal compressor” and presented at the university-level conference organized by Taylor & Francis.
Utilized specialized software tools ANSYS-Fluent and ANSYS-CFX for computing flow field of a centrifugal pump.