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Skills & Research 

Relevant Coursework

Computational and Variational Methods for Inverse Problems (graduate)

Intro to Machine Learning and Data Science (graduate)

Generative Visual Computing

Engineering Computation

Differential Equations and Linear Algebra

Calculus III

Software Engineering and Design

Scientific Computation

Advanced Scientific Computation

Professional skillset

MATLAB

C++

Containerization/API Deployment: Docker, Flask, Redis, Kubernetes

Python, PyTorch, Tensorflow

JAX 

GitHub

Languages

English (native)

Telugu (native, spoken)

French (proficient)

Research

May 2025 - Present

Moncrief Summer Internship/ Research Assistant

Optimization, Inversion, Machine Learning, and Uncertainty for Complex Systems

Oden Institute for Computational Engineering and Sciences

Dr. Omar Ghattas

The University of Texas at Austin

  • Developing derivative-informed neural operators to learn observable-to-parameter maps for PDE-governed inverse problems (real-time methods for applications in linear elasticity, Helmholtz wave inversion, etc.)

  • Utilize active subspace methods to compress parameter field, data, and sensitivities (forward Jacobian or post-optimal sensitivity).

fwd_inv_helmhltz.png

Estimated errors for solving a multisource Helmholtz inverse problem, averaged over 100 samples. The true MAP point is evaluated using a PDE forward model, with all iterative methods (L-BFGS-B with forward surrogate) initialized at the prior.

May - August 2024

Moncrief Summer Internship/ Research Assistant

Willerson Center for Cardiovascular Modeling and Simulation

Oden Institute for Computational Engineering and Sciences

Dr. Michael S. Sacks

The University of Texas at Austin

  • Used the JAX Finite Element Method (JAX-FEM) Software to analyze the effects of strain on aortic valve interstitial cell (AVIC) activation in a 3D hydrogel environment. 

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  • Built an inverse model (with adjoint method, total variance regularization) that identifies the modulus field while minimizing error in simulated nodal displacements. Forward model component solves for gel nodal displacements (per a given modulus field) ~100x faster than FEniCS standard for a 5 million element problem. 

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October 2023-April 2024

Independent Research

Department of Aerospace & Engineering Mechanics,

Dr. Chad M. Landis

The University of Texas at Austin

3D Cube Truss Deformation, Force in -Y Direction

Note: Blue indicates bar compression.

Before Deformation

image.png

  • Applied principles and methodologies to solving structural deformations in linear and non-linear truss problems, with plans of modeling displacements and thermal or electrical conductivity gradients across shape-memory polymers.

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  • Used Python to build a finite element solver for linear systems (small nodal displacement) and two approximations for a non-linear system (considerable nodal displacements) with the Euler’s method and the Newton-Raphson method.

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  • Visualizations (of internal strains or bar forces) were done using Blender.

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After Deformation

image.png

June 2022-June 2023

Independent Research Assistant

Learning and Attention Lab

Department of Psychological & Brain Sciences, Dr. Brian Anderson

Texas A&M University

Screenshot 2023-01-29 150307.png

  • Used MATLAB PsychToolBox to develop a touch-screen-based app investigating attention.

  • The experiment assigns points (score) based on participants' reaction times (RT) to choosing trial-specific targets amidst distractors.

  • Image choices consist of: ​

    • ​salient (large and moving) & non-salient distractors

    • high frequency & high value targets​

  • Program on GitHub​

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  • Joined weekly discussions about current in-lab experiments & academia, cognitive psychology, cognitive neuroscience, statistics, research design, etc. ​

June 2022-September 2022

Research Assistant

  • Primarily gathered data by virtually interviewing children aged 8-12 about their emotions regarding STEM fields. 

  • Learned correlations between various aspects such as race, aspirations, and hobbies with attitudes towards STEM fields. 

  • Worked on improving communication skills.

    • Paid attention to non-verbal cues such as physical actions and facial expressions.​​

    • Used simple language to answer participants' questions.​​

  • Received CITI Human Research Certificate in Group 2 Social and Behavioral Research Investigators and Key Personnel​.

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Beliefs, Emotions, Attitudes about Math (BEAM) Lab

Department of Education Psychology,

Dr. Connie Barroso 

Texas A&M University

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