Projects

Representation Learning Strategies for the Epigenome and Chromatin Structure using Recurrent Neural Models

Thesis, Thesis, 2023

In this Ph.D. thesis, we propose frameworks for designing informative position-specific representations from epigenomic and structural genomic signals. We use recurrent priors in our analysis owing to the fact that the genome is heavily correlated with nearby positions, and implement them using recurrent neural models. We demonstrate that the representations we learn are helpful for various tasks, including, locating known genomic elements, identifying conserved sites, correlating with established genomic measures, enabling accurate decoding, finding elements that drive 3D conformation, attributing relative positional importance, and performing in-silico modifications. In the process of designing these representations, we study two classes of strategies that differ in their underlying philosophy, namely, autoencoding and categorical encoding. We show that the usefulness of these representations depends on the underlying strategies used while designing them.

Private machine learning: Extension to the bayesian setting and higher dimensional querying

Private Machine Learning, Bayesian, High Dimensional, 2018

We extended the private machine learning framework to include higher dimensional querying arriving at upper and lower bounds on sample complexity. Provided a primer for querying under the bayesian setting with a prior on the underlying distribution of keys.

Character Based Language Models Through Variational Sentence and Word Embeddings

NLP, Language Model, 2018

Language models have come of age recently with the introduction of Long-Short-Term-Memory based encoders, decoders and the advent of the attention mechanism. These models however work by generating one word at a time and cannot account for character level similarities and differences. In this project we propose a novel character based hierarchical variational autoencoder framework that can learn the word and sentence embeddings at the same time. We couple this with an attention mechanism over the latent word embeddings to realize the end-to-end autoencoder framework.

Hybrid Precoding for mmWave Massive MIMO OFDM

Hybrid Precoding, Partially Connected Structure, 2018

Hybrid precoding, a combination of digital and analog precoding, is an alternative to traditional precoding methods in massive MIMO systems with a large number of antenna elements and has shown promising results recently. In this paper, we implement a parallel framework to make hybrid precoding competitive in fast-fading environments. A low-complexity algorithm which exploits the block diagonal phase-only nature of the analog precoder in a partially connected structure is proposed to arrive at a hybrid precoding solution for a multi-carrier single-user system using orthogonal frequency division multiplexing (OFDM). The original problem is broken down into two subproblems of finding the magnitude and the phase components which are solved independently. A per-RF chain power constraint is introduced instead of the sum power constraint over all antennas which are much more practical in real systems. An alternating version of the same algorithm is proposed for increased spectral-efficiency gains. Complexity and run-time analysis demonstrate the advantage of the proposed algorithm over existing hybrid precoding schemes for partially connected structure in an OFDM setting. The simulation results reveal certain insights about the partially connected structure and the tradeoffs that have to be made to make it workable in a real wideband system.