AI Research Intern — Siemens (AI Digital Twins Simulation)

In my current role as an AI Research Intern at Siemens, I am immersed in a collaborative and intellectually stimulating environment, working alongside Amirhossein Nouranizadeh, Alan John Varghese, Dr. Mengjia Xu, Dr. Amit Chakraborty, and Dr. Yu-chin Chan to advance the field of Scientific Machine Learning. My experience extends beyond technical execution to encompass the full lifecycle of industrial research, from initial theoretical formulation to the rigorous validation required for drafting high-impact research papers.

Day-to-day, I am contributing to a major project focused on reducing the high computational costs associated with training deep learning models on large-scale engineering data. I am helping to develop a "Graph Wavelet Compressed Sensing" (GWCS) framework, which leverages graph signal processing to efficiently compress and reconstruct complex physics simulations, such as fluid dynamics and astrophysical phenomena. This role involves a dynamic mix of assisting with mathematical proofs for signal sparsity and conducting hands-on experimentation, where I prototype solutions using PyTorch and Graph Neural Networks. A significant portion of my work focuses on implementing specific architecture components, such as scale-aware encoders and Chebyshev polynomial approximations.

Beyond the engineering, I am refining my ability to synthesize complex experimental results into coherent narratives for publication, actively drafting sections on methodology and ablation studies. I also had the opportunity to present our work at a research forum, communicating findings on sparse wavelet representations for data compression.