Rajalaxmi Rajagopalan

I’m a Ph.D. student in the Electrical and Computer Engineering Department at the University of Illinois, Urbana Champaign (UIUC).
I am working with Dr. Romit Roy Choudhury in the Signals and Inference Research Group (SiNRG)
Research Interests
My research interest is in Black-box optimization problems and other problems in signal processing and acoustics. My current research is in audio personalization in earables and universal speech enhancement.
Current Research Projects
Finding h is difficult as the function f(h) is unknown. Optimizing f(h) using the human in the loop, while constrained by the number of samples is a sample-efficient black-box optimization problem. We build on the Bayesian Optimization framework. We achieve sample efficiency by developing kernel learning techniques that learn a unique kernel that models function structure. Our techniques are extended to other black-box problems in many areas beyond personalization.
We use a pre-training-finetuning framework. A Masked Spectrogram Autoencoder based on Vision transformers is the model of choice. The pre-trained embeddings are then used by fine-tuning models trained on a small amount of paired data for specific downstream tasks like denoising, dereverberation, source separation, and bandwidth extension.
CV
Here’s my CV.