Indu Kant Deo

I am a 5th year Ph.D. candidate at Computational Multiphysics Laboratory at The University of British Columbia Vancouver. I am working under the supervision of Prof. Rajeev Jaiman towards completing my dissertation on physics-based machine learning reduced-order models for improving long-time-horizon wave propagation predictions. My research focuses on developing deep learning-based reduced-order models and leveraging machine learning frameworks to address complex challenges in fluid dynamics and underwater noise propagation.

In recent years, I’ve had the privilege of collaborating on projects that combine scientific computing methods with advanced deep learning principles, including:

1. Building a convolutional autoencoder and sequence-to-sequence attention network for long-time-horizon wave propagation predictions, published in Physics of Fluids.
2. Developing a space-time reduced order model that combines 3D convolution kernels to efficiently predict fluid flow dynamics, published in Physics of Fluids and selected as Editor’s pick.
3. Introducing a novel finite element-inspired hyper graph neural network, which enables structured, high-fidelity representation of physical domains, enhancing model robustness and predictive accuracy for complex systems, published in Journal of Computational Physics.
4. Developing a conditional convolutional neural network for real-time transmission loss prediction in varying oceanic environments, with a continual learning approach to account for changing bathymetry.
5. Applying parameterized reduced-order modeling techniques to predict distortion in metal 3D printing, which led to a publication in the NeurIPS Machine Learning for Physical Sciences Workshop.
6. Building an optimization framework which reduces the impact of shipping noise on marine mammals, published in Ocean Engineering journal.

Outside the lab, I’m passionate about the outdoors. Whether hiking up scenic trails, climbing rock faces, skiing down snowy slopes, or swimming in open waters, I find balance and inspiration in nature. These activities allow me to approach my work with a fresh perspective, fostering a problem-solving mindset that’s essential in research.

As I look forward to completing my Ph.D. by April, 2025, I am actively exploring opportunities where I can apply my skills and insights toward impactful projects. I’m particularly interested in roles that allow me to integrate computational science and machine learning in environmental and engineering applications.