Ahmed Imtiaz Humayun

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.

My work has been featured in multiple news sources, e.g., WIRED, New Scientist, Futurism, Tom's Hardware, The Daily Star, The Business Standard, Prothom Alo (Bengali), and IEEE Signal Processing Magazine.

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My research transects the domains of deep learning theory, generative modeling, interpretability, and optimization. Some of my representative projects are listed below.

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 present empirical evidence showing that the local complexity of DNNs -- measured in terms of linear region density -- undergro an epoch-wise double descent phenomenon during training. Starting with an initial descend phase, after a number of epochs the network accumulates its non-linearities around training points, resulting in a ascent of local complexity. Finally through another descent phase, all the non-linearities of the networks accumulate around the decision boundary.

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.

We extend existing methods that rely on the use of kernels to variable-length sequences by using the Recurrent Neural Tangent Kernel (RNTK).

We show that novel Convolutional Neural Network (CNN) layers that emulate different classes of Finite Impulse Response (FIR) filters can perform domain invariant heart sound abnormality detection.

Very Deep Convolutional Residual Network achieve state-of-the-art results in sleep staging, using only raw single channel EEG.

Convolutional Recurrent Neural Networks outperform SOTA RNN based compression methods as well as JPEG 2000 for X-ray image compression.

State-of-the-art heart abnormality classification using an ensemble of Representation Learning methods.

We propose novel linear phase and zero phase convolutional neural networks that can be used as learnable filterbank front-ends.

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)

Jointly largest open-sourced Bengali ASR dataset as well as first Bengali Out-of-Distribution Speech Recognition benchmarking dataset.

First Multi-Domain Bengali Document Layout Analysis Dataset, with 700K polygon annotations from image captured documents in the wild.

We have crowdsourced the first public 500 hr Bengali Speech Dataset on the Mozilla Common Voice platform, with speech contributed by over 20K people from Bangladesh and India.

A benchmark datset for multi-target classification of handwritten Bengali Graphemes, with novel implications for all alpha-syllabary languages, e.g., Hindi, Gujrati, and Thai.

The first large scale Multi-Domain Bengali Handwritten Digit Recognition Dataset

Yet another steal of Jon Barron's amazing website.