UT Austin’s student chapter of SIAM was founded in 2008 to foster interactions between members of the applied mathematics community at UT Austin, across departments, institutes, and professional marks. We aim to provide a forum for discussing applied and computational mathematics and to help members prepare for future STEM careers in academia and industry. We also promote publications, conferences, prizes, and other opportunities offered by SIAM.
I am interested in leveraging data assimilation to better calibrate large ice sheets and ocean models in particular using Automatic Differentiation derived adjoint-based gradients. I also dabble with physics-informed machine learning applications in these domains.
Magnetic resonance imaging (MRI) is a widely used imaging modality in modern clinical medicine due to its excellent soft-tissue contrast and lack of ionizing radiation but suffers from a tradeoff between signal-to-noise ratio, image resolution, and scan time that limits the technique's applications and poses a barrier to improved health equity. My research aims to address these limitations by improving the design of MRI scans using techniques from optimal control and generative modeling.
I am interested in scientific machine learning. Currently, I work on applying physics informed neural networks to model fluid flow.
I use optimization and machine learning techniques for process control and energy system. The current focus of my research is production scheduling optimization for fluctuating electricity prices and energy supplies.
I seek to understand computational mechanisms underlying disease states characterized by abnormal behavior in neural oscillators. The current focus of my research is on neuron classification via optimization mechanisms.
I'm a Junior at UT interested in a career in data science or quantitative finance.
I use numerical simulations to study diverse fluid flow problems in planetary sciences such as impact crater lake dynamics on Mars, melting of ice on glaciers and formation of planetary cores.
I work as an undergraduate research assistant developer for Keitt Lab's Biosensing project for whom I have successfully established a Discord Situation-Report automated bot for our environmental sensors, and I am currently leading the Sensor Communication and Audio Source Triangulation Mesh team working to establish a network communication mesh between audio sensors to aid connectivity, file transmissibility, audio source triangulation and 3D-mapping among our future environmental sensor deployment sites. I also hope to utilize most of my applied mathematics knowledge to further my astrophysical studies and research in analyzing data from Caltech's LIGO cosmic gravitational-wave background data collected from the LIGO observatory and its partners' observation runs.
I work broadly on uncertainty quantification methods to enable scalable predictive digital twins as a framework for robust decision making with applications to various engineering and health systems.
As part of the Autonomous Systems Group at Oden Institute, I apply AGI Systems Tool Kit to model drone communications in simulations of real-life scenarios.