Publications

Learning to Align Decentralized Agents with Global Goals via Co-Evolution of Code and Language

Preparing for submission, 2025

The combination of Large Language Models (LLMs) and Evolutionary Algorithms (EAs) offers a new paradigm for solving complex, real-world problems. We introduce Individual-Global Alignment via Evolution of Heuristics (IGA-EH), a framework that leverages LLMs as intelligent variation and interpretation engines within an evolutionary loop to align decentralized agent behavior with global system objectives. IGA-EH simultaneously evolves executable decision-making heuristics and natural language nudges, enabling adaptive mechanism design that integrates behavioral and programmatic reasoning. Applied to agricultural landscapes, the framework discovers heuristics that approximate optimal ecological connectivity while generating persuasive messages that steer diverse simulated agents toward collective outcomes. Crucially, IGA-EH extends beyond prior LLM-EA work focused on benchmark tasks by addressing real-world, mixed-integer optimization problems and co-evolving both behavioral rules and communication strategies. This approach establishes a generalizable method for influencing agent behavior in complex systems, with applications in environmental planning, resource governance, and aligned AI decision-making.

Assessing the climate benefits of afforestation in the Canadian Northern Boreal and Southern Arctic

Published in Nature Communications, 2025

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. (2025). Assessing the climate benefits of afforestation in the Canadian Northern Boreal and Southern Arctic. Nature Communications, 16(1), 1964. 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

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

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

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