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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.

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.

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.

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.

A Downscaled Faster-RCNN Framework for Signal Detection and Time-Frequency Localization in Wideband RF Systems

Published in IEEE Transactions on Wireless Communications, 2020

We propose a wideband spectrum sensing technique to detect and localize wireless radio frequency (RF) signals of interest in time and frequency when uninteresting signals cause RF interference (RFI). Specifically, we adopt and downscale the existing Faster-RCNN (FRCNN) framework to achieve better signal detection and localization than the state-of-the-art. For experimental evaluation, we present a data generation framework for Wi-Fi as the signals of interest and the Bluetooth and microwave oven signals as the RFI. Experiments reveal that (i) the downscaled FRCNN model can achieve up to a mean average precision (mAP) of 0.8, significantly outperforming the state-of-the-art, (ii) feature extraction with the VGG-13 architecture gives the best mAP with pretrained weights and configured as trainable, (iii) for signal detection in real RF traces, when compared to training purely with synthetic RF data, a better mAP can be achieved by training with a mixture of synthetic and real RF traces or by fine tuning the synthetically-trained weights with an additional round of training on a small amount of real RF traces, and (iv) the mAP performance decreases as the signal to noise ratio (SNR) is lowered.

Recommended citation: Prasad, K. S. V., D’souza, K. B., & Bhargava, V. K. (2020). A downscaled faster-RCNN framework for signal detection and time-frequency localization in wideband RF systems. IEEE Transactions on Wireless Communications, 19(7), 4847-4862. Full Document

Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021

The availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were cell type-specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-cell type representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions, and evolutionary conservation. These representations outperform existing methods in a majority of cell types, while yielding smoother representations along the genomic axis due to their sequential nature.

Recommended citation: Dsouza, K. B., Li, A. Y., Bhargava, V., & Libbrecht, M. W. (2021). Latent representation of the human pan-celltype epigenome through a deep recurrent neural network. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Full Document

Wireless threat detection device, system, and methods to detect signals in wideband RF systems and localize related time and frequency information based on deep learning

Published in US Patent, 2021

The present invention comprises a novel system and method to detect and estimate the time-frequency span of wireless signals present in a wideband RF spectrum. In preferred embodiments, the Faster RCNN deep learning architecture is used to detect the presence of wireless transmitters from the spectrogram images plotted by searching for rectangular shapes of any size, then localize the time and frequency information from the output of the FRCNN deep learning architecture.

Recommended citation: Koppisetti, N. R. S. V. P., Dsouza, K. B., Boostanimehr, H., & Mallick, S. (2022). U.S. Patent Application No. 17/825,304. Full Document

Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation

Published in Nature Communications, 2021

Despite the availability of chromatin conformation capture experiments, discerning the relationship between the 1D genome and 3D conformation remains a challenge, which limits our understanding of their affect on gene expression and disease. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory neural network model. We find that these representations contain all the information needed to recreate the observed Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.

Recommended citation: Dsouza, K. B., Maslova, A., Al-Jibury, E., Merkenschlager, M., Bhargava, V. K., & Libbrecht, M. W. (2022). Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation. Nature Communications, 13(1), 1-19. Full Document

Assessing the climate benefits of afforestation: processes, methods, and frameworks

Published: July 23, 2024

Afforestation greatly influences several earth system processes, making it essential to understand these effects to accurately assess its potential for climate change mitigation. Although our understanding of forest-climate system interactions has improved, significant knowledge gaps remain, preventing definitive assessments of afforestation’s net climate benefits. In this review, focusing on the Canadian northern boreal and southern arctic, we identify these gaps and synthesize existing knowledge. The review highlights regional realities, Earth’s climatic history, uncertainties in biogeochemical (BGC) and biogeophysical (BGP) changes following afforestation, and limitations in current assessment methodologies, emphasizing the need to reconcile these uncertainties before drawing firm conclusions about the climate benefits of afforestation. Finally, we propose an assessment framework which considers multiple forcing components, temporal analysis, future climatic contexts, and implementation details. We hope that the research gaps and assessment framework discussed in this review inform afforestation policy in Canada and other circumpolar nations.

Recommended citation: Dsouza, K. B., Ofosu, E., Salkeld, J., Boudreault, R., Moreno-Cruz, J., & Leonenko, Y. (2024). Assessing the climate benefits of afforestation: processes, methods, and frameworks. arXiv preprint arXiv:2407.14617. Full Document

Representation learning for biology 1

Published: December 13, 2019

The talks were about Epi-LSTM: A recurrent neural network model that builds cell type-agnostic representations of the human epigenome.

Representation learning for biology 2

Published: December 10, 2021

The talks were about Hi-C-LSTM: a contact generation framework that forms Hi-C intrachromosomal representations.

ELEC 433: Error control coding for communications and computers

Course, The University of British Columbia, Department of ECE, 2018

The course included code design techniques, including Hamming, BCH, Reed-Solomon, LDPC and convolutional codes, ARQ techniques, and LFSR implementation of encoding-decoding algorithms. Created and graded assignments.

CPEN 491: ECE final year undergraduate Capstone design project

Projects, The University of British Columbia, Department of ECE, 2019

This course involved mentoring final year undergraduate capstone students. I provided design inputs at various stages and helped them to drive the projects to completion. For Data and ML related projects I provided substantial guidance each week (2019:1,2,3; 2020:1,2; 2021:1,2,3). All Coding was done by the students.

CPEN 481: Economic Analysis of Engineering Projects

Course, The University of British Columbia, Department of ECE, 2020

The course included Time-money relationships; economic analysis of alternatives including the effects of interest rates, inflation, depreciation, taxation and uncertainty; cost estimation and budgeting; financial analysis of engineering operations. Created and graded assignments.