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Vaibhav Rajan

7 accepted papers

2026

MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models

IJCAI 2026

Agricultural landscape segmentation in the Global South is challenging as it is characterized by fragmented plots, high intra-class variance, and a scarcity of labeled training data. Recent advances in segmentation have been made by Multimodal Large Language Models (MLLMs). However, current approach

Cited by 0Scholar
2025

GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment

ICLR 2025poster

Effective treatment of cancer is a major challenge faced by healthcare providers, due to the highly individualized nature of patient responses to treatment. This is caused by the heterogeneity seen in cancer-causing alterations (mutations) across patient genomes. Limited availability of response dat…

Cited by 0SourcePDFScholar
2024

Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning

ICLR 2024poster

Metagenomics studies genomic material derived from mixed microbial communities in diverse environments, holding considerable significance for both human health and environmental sustainability. Metagenomic binning refers to the clustering of genomic subsequences obtained from high-throughput DNA seq…

Cited by 2SourcePDFScholar
2024

WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer

ICML 2024poster

Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers ('cell lines') is experimentally determined in laboratory…

2022

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction

NeurIPS 2022accept

Understanding genetic variation, e.g., through mutations, in organisms is crucial to unravel their effects on the environment and human health. A fundamental characterization can be obtained by solving the haplotype assembly problem, which yields the variation across multiple copies of chromosomes.…

2022

RepBin: Constraint-Based Graph Representation Learning for Metagenomic Binning

AAAI 2022technical

Mixed communities of organisms are found in many environments -- from the human gut to marine ecosystems -- and can have profound impact on human health and the environment. Metagenomics studies the genomic material of such communities through high-throughput sequencing that yields DNA subsequences…

2020

Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization

ICML 2020poster

Multi-Task Learning (MTL) is a well established paradigm for jointly learning models for multiple correlated tasks. Often the tasks conflict, requiring trade-offs between them during optimization. In such cases, multi-objective optimization based MTL methods can be used to find one or more Pareto op…