I am a PhD student at Rice University, where I work on deep learning theory and generative modeling, advised by Dr. Richard Baraniuk. I am jointly working as a Student Researcher at Google on adapting text-to-image generative models.
My interests lie in interpretation and improvement of deep neural network models via approximation theory, e.g., spline theory. I have received the Lowernstein Fellowship at Rice and have published in ICLR, CVPR, ICASSP, and INTERSPEECH, to name a few. I have also founded Bengali.AI, a non-profit initiative that crowdsources datasets and open-sources them through international ML competitions, e.g., Out-of-Distribution Speech Recognition @ Kaggle.
The first provably exact method for computing the geometry of ANY DNN's mapping, including its decision boundary. For a specified region of the input space, SplineCam can be used to compute and visualize the 'linear regions' formed by any DNN with piecewise linear non-linearities, e.g., LeakyReLU, Sawtooth.
We study the phenomenon of training new generative models with synthetic data from previous generative models. Our primary conclusion is that without enough fresh real data in each generation of a self-consuming or autophagous loop, future generative models are doomed to have their quality (precision) or diversity (recall) progressively decrease.
Using spline theory, we present a novel method for imposing analytical constraints directly on the decision boundary for provable robustness. Our method can provably ensure robustness for any set of instances, e.g. training samples from a specific class, against adversarial, backdoor or poisoning attack.
A provable method for controllable generation based on quality and diversity from any pre-trained deep generative model. We show that increasing the sampling diversity helps surpass SOTA image generation.
A novel and theoretically motivated latent space sampler for any pre-trained DGN, that produces samples uniformly distributed on the learned output manifold. Applications in fairness and data augmentation.
Using spline theory, we present a method for exact visualization of deep neural networks that allows us to visualize the decision boundary and also sample arbitrarily many inputs that provably lie on the model's decision boundary
Repeated samples and sampling bias may manifest imbalanced clustering via K-methods. We propose the first method to impose a hard radius constraint on K-Means, achieving robustness towards sampling inconsistencies.
IEEE Signal Processing Cup Honorable Mention for Real-time Music Beat Tracking Embedded System.
Bengali.AI is a non-profit in Bangladesh where we create novel datasets to accelerate Bengali Language Technologies (e.g., OCR, ASR) and open-source them through machine learning competitions (e.g., Grapheme 2020, ASR 2022)