Hurdles Hardly Have to Be Heeded. - Krister M. Swenson; Yu Lin 001; Vaibhav Rajan; Bernard M. E. Moret (RECOMB-CG, 2008)

Sorting Signed Permutations by Inversions in O(nlogn) Time. - Krister M. Swenson; Vaibhav Rajan; Yu Lin 001; Bernard M. E. Moret (RECOMB, 2009)

Hurdles and Sorting by Inversions: Combinatorial, Statistical, and Experimental Results. - Krister M. Swenson; Yu Lin 001; Vaibhav Rajan; Bernard M. E. Moret (J. Comput. Biol., 2009)

Estimating true evolutionary distances under rearrangements, duplications, and losses. - Yu Lin 001; Vaibhav Rajan; Krister M. Swenson; Bernard M. E. Moret (BMC Bioinform., 2010)

Heuristics for the inversion median problem. - Vaibhav Rajan; Andrew Wei Xu; Yu Lin 001; Krister M. Swenson; Bernard M. E. Moret (BMC Bioinform., 2010)

Sorting Signed Permutations by Inversions in O(nlogn) Time. - Krister M. Swenson; Vaibhav Rajan; Yu Lin 001; Bernard M. E. Moret (J. Comput. Biol., 2010)

Fast and Accurate Phylogenetic Reconstruction from High-Resolution Whole-Genome Data and a Novel Robustness Estimator. - Yu Lin 001; Vaibhav Rajan; Bernard M. E. Moret (J. Comput. Biol., 2011)

Bootstrapping phylogenies inferred from rearrangement data. - Yu Lin 001; Vaibhav Rajan; Bernard M. E. Moret (Algorithms Mol. Biol., 2012)

TIBA: a tool for phylogeny inference from rearrangement data with bootstrap analysis. - Yu Lin 001; Vaibhav Rajan; Bernard M. E. Moret (Bioinform., 2012)

A Metric for Phylogenetic Trees Based on Matching. - Yu Lin 001; Vaibhav Rajan; Bernard M. E. Moret (IEEE ACM Trans. Comput. Biol. Bioinform., 2012)

Reconstruction of Large-Scale Phylogenies - Advances in Algorithms and Statistical Methods. - Vaibhav Rajan (2012)

CrowdUtility: know the crowd that works for you. - Koustuv Dasgupta; Vaibhav Rajan; Saraschandra Karanam; Kovendhan Ponnavaikko; Chithralekha Balamurugan; Nischal M. Piratla (CHI Extended Abstracts, 2013)

Adaptive crowdsourcing for temporal crowds. - L. Elisa Celis; Koustuv Dasgupta; Vaibhav Rajan (WWW (Companion Volume), 2013)

A novel classification method for predicting acute hypotensive episodes in critical care. - Sakyajit Bhattacharya; Vaibhav Rajan; Vijay Huddar (BCB, 2014)

Predicting Postoperative Acute Respiratory Failure in critical care using nursing notes and physiological signals. - Vijay Huddar; Vaibhav Rajan; Sakyajit Bhattacharya; Shourya Roy (EMBC, 2014)

CrowdUtility: A Recommendation System for Crowdsourcing Platforms. - Deepthi Chander; Sakyajit Bhattacharya; L. Elisa Celis; Koustuv Dasgupta; Saraschandra Karanam; Vaibhav Rajan; Avantika Gupta (HCOMP, 2014)

Adaptive Performance Optimization over Crowd Labor Channels. - Saraschandra Karanam; Deepthi Chander; L. Elisa Celis; Koustuv Dasgupta; Vaibhav Rajan (HCOMP, 2014)

Classification with imbalance: A similarity-based method for predicting respiratory failure. - Harsh Shrivastava 001; Vijay Huddar; Sakyajit Bhattacharya; Vaibhav Rajan (BIBM, 2015)

Maximum Parsimony Analysis of Gene Copy Number Changes. - Jun Zhou; Yu Lin 001; Vaibhav Rajan; William Hoskins; Jijun Tang (WABI, 2015)

Crowds of Crowds: Performance based Modeling and Optimization over Multiple Crowdsourcing Platforms. - Sakyajit Bhattacharya; Laura Elisa Celis; Deepthi Chander; Koustuv Dasgupta; Saraschandra Karanam; Vaibhav Rajan (Hum. Comput., 2015)

A statistical model for stroke outcome prediction and treatment planning. - Abhishek Sengupta; Vaibhav Rajan; Sakyajit Bhattacharya; G. R. K. Sarma (EMBC, 2016)

Dependency Clustering of Mixed Data with Gaussian Mixture Copulas. - Vaibhav Rajan; Sakyajit Bhattacharya (IJCAI, 2016)

Clinical Decision Support for Stroke Using Multi-view Learning Based Models for NIHSS Scores. - Vaibhav Rajan; Sakyajit Bhattacharya; Ranjan Shetty; Amith Sitaram; G. Vivek (PAKDD Workshops, 2016)

Predicting Complications in Critical Care Using Heterogeneous Clinical Data. - Vijay Huddar; Bapu Koundinya Desiraju; Vaibhav Rajan; Sakyajit Bhattacharya; Shourya Roy; Chandan K. Reddy (IEEE Access, 2016)

Analysis of gene copy number changes in tumor phylogenetics. - Jun Zhou; Yu Lin 001; Vaibhav Rajan; William Hoskins; Bing Feng; Jijun Tang (Algorithms Mol. Biol., 2016)

ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets. - Sakyajit Bhattacharya; Vaibhav Rajan; Harsh Shrivastava 001 (AAAI, 2017)

Prediction and imputation in irregularly sampled clinical time series data using hierarchical linear dynamical models. - Abhishek Sengupta; A. P. Prathosh; Satya Narayan Shukla; Vaibhav Rajan; Chandan K. Reddy (EMBC, 2017)

Vine copulas for mixed data : multi-view clustering for mixed data beyond meta-Gaussian dependencies. - Lavanya Sita Tekumalla; Vaibhav Rajan; Chiranjib Bhattacharyya (Mach. Learn., 2017)

Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks. - Aishwarya Jadhav; Vaibhav Rajan (ACL (1), 2018)

Detecting Temporal Pattern Profiles from Smartphones for User Activity Analysis. - Suparna Ghanvatkar; Vaibhav Rajan; Atreyi Kankanhalli (ICIS, 2018)

Automatic Hierarchical Table of Contents Generation for Educational Videos. - Debabrata Mahapatra; Ragunathan Mariappan; Vaibhav Rajan (WWW (Companion Volume), 2018)

VideoKen: Automatic Video Summarization and Course Curation to Support Learning. - Debabrata Mahapatra; Ragunathan Mariappan; Vaibhav Rajan; Kuldeep Yadav; Seby A.; Sudeshna Roy 001 (WWW (Companion Volume), 2018)

Inferring Concept Prerequisite Relations from Online Educational Resources. - Sudeshna Roy 001; Meghana Madhyastha; Sheril Lawrence; Vaibhav Rajan (AAAI, 2019)

Deep Recurrent Neural Networks for Mortality Prediction in Intensive Care using Clinical Time Series at Multiple Resolutions. - Suparna Ghanvatkar; Vaibhav Rajan (ICIS, 2019)

Battling Alzheimer's Disease through Early Detection: A Deep Multimodal Learning Approach. - Lin Qiu; Vaibhav Rajan; Bernard C. Y. Tan (ICIS, 2019)

Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks. - Lakshmanan Rakkappan; Vaibhav Rajan (WWW, 2019)

Deep collective matrix factorization for augmented multi-view learning. - Ragunathan Mariappan; Vaibhav Rajan (Mach. Learn., 2019)

Adverse Drug Event Prediction using Noisy Literature-Derived Knowledge Graphs. - Abel Lim Jun Hong; Ragunathan Mariappan; Vaibhav Rajan (ICIS, 2020)

An IoT-based DSS using Vehicle Movement Data for Nature Parks. - Prakash Chandra Sukhwal; Atreyi Kankanhalli; Vaibhav Rajan (ICIS, 2020)

Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization. - Debabrata Mahapatra; Vaibhav Rajan (ICML, 2020)

Gaussian mixture copulas for high-dimensional clustering and dependency-based subtyping. - Siva Rajesh Kasa; Sakyajit Bhattacharya; Vaibhav Rajan (Bioinform., 2020)

Predicting synthetic lethal interactions using heterogeneous data sources. - Herty Liany; Anand Jeyasekharan; Vaibhav Rajan (Bioinform., 2020)

MetaBCC-LR: metagenomics binning by coverage and composition for long reads. - Anuradha Wickramarachchi; Vijini Mallawaarachchi; Vaibhav Rajan; Yu Lin 001 (Bioinform., 2020)

Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional Data. - Siva Rajesh Kasa; Vaibhav Rajan (CoRR, 2020)

Multi-way Spectral Clustering of Augmented Multi-view Data through Deep Collective Matrix Tri-factorization. - Ragunathan Mariappan; Vaibhav Rajan (CoRR, 2020)

Expect the Unexpected: Engaging Users via Serendipitous Recommendations. - Wei Cui; Vaibhav Rajan; Zhenhui Jiang (ICIS, 2021)

Dependency Modeling with Copulas in Multi-Armed Bandits. - Siva Rajesh Kasa; Vaibhav Rajan (ICIS, 2021)

Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks. - Hansheng Xue; Luwei Yang; Vaibhav Rajan; Wen Jiang; Yi Wei; Yu Lin 001 (WWW, 2021)

Maximum likelihood reconstruction of ancestral networks by integer linear programming. - Vaibhav Rajan; Ziqi Zhang; Carl Kingsford; Xiuwei Zhang 002 (Bioinform., 2021)

CAC: A Clustering Based Framework for Classification. - Shivin Srivastava; Siddharth Bhatia 001; Lingxiao Huang; Lim Jun Heng; Kenji Kawaguchi; Vaibhav Rajan (CoRR, 2021)

Exact Pareto Optimal Search for Multi-Task Learning: Touring the Pareto Front. - Debabrata Mahapatra; Vaibhav Rajan (CoRR, 2021)

Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization. - Ragunathan Mariappan; Vaibhav Rajan (CoRR, 2021)

Multi-disease Predictive Analytics: A Clinical Knowledge-aware Approach. - Lin Qiu; Sruthi Gorantla; Vaibhav Rajan; Bernard C. Y. Tan (ACM Trans. Manag. Inf. Syst., 2021)

RepBin: Constraint-Based Graph Representation Learning for Metagenomic Binning. - Hansheng Xue; Vijini Mallawaarachchi; Yujia Zhang; Vaibhav Rajan; Yu Lin 001 (AAAI, 2022)

Towards a Theory-Based Evaluation of Explainable Predictions in Healthcare. - Suparna Ghanvatkar; Vaibhav Rajan (ICIS, 2022)

Designing a Healthcare QA Assistant: A Knowledge Based Approach. - Prakash Chandra Sukhwal; Atreyi Kankanhalli; Vaibhav Rajan (ICIS, 2022)

Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction. - Hansheng Xue; Vaibhav Rajan; Yu Lin 001 (NeurIPS, 2022)

Neural Collective Matrix Factorization for integrated analysis of heterogeneous biomedical data. - Ragunathan Mariappan; Aishwarya Jayagopal; Ho Zong Sien; Vaibhav Rajan (Bioinform., 2022)

ExpertNet: A Symbiosis of Classification and Clustering. - Shivin Srivastava; Kenji Kawaguchi; Vaibhav Rajan (CoRR, 2022)

An Algorithm to Mine Therapeutic Motifs for Cancer From Networks of Genetic Interactions. - Herty Liany; Yu Lin 001; Anand Jeyasekharan; Vaibhav Rajan (IEEE J. Biomed. Health Informatics, 2022)

Three-Dimensional Modelling of Past and Present Shahjahanabad through Multi-Temporal Remotely Sensed Data. - Vaibhav Rajan; Mila Koeva; Monika Kuffer; Andre Da Silva Mano; Shubham Mishra (Remote. Sens., 2023)

ExpertNet: A Deep Learning Approach to Combined Risk Modeling and Subtyping in Intensive Care Units. - Shivin Srivastava; Vaibhav Rajan (IEEE J. Biomed. Health Informatics, 2023)

Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning. - Hansheng Xue; Vijini Mallawaarachchi; Lexing Xie; Vaibhav Rajan (ICLR, 2024)

WISER: Weak Supervision and Supervised Representation Learning to Improve Drug Response Prediction in Cancer. - Kumar Shubham; Aishwarya Jayagopal; Syed Mohammed Danish; Prathosh A. P.; Vaibhav Rajan (ICML, 2024)

Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information. - Aishwarya Jayagopal; Hansheng Xue; Ziyang He; Robert J. Walsh; Krishna Kumar Hariprasannan; David Shao Peng Tan; Tuan Zea Tan; Jason J. Pitt; Anand D. Jeyasekharan; Vaibhav Rajan (KDD, 2024)

4D Geo Modelling from Different Sources at Large Scale. - Sander Münster; Jonas Bruschke; Daniel Dworak; Dávid Komorowicz; Vaibhav Rajan; Dominik Ukolov (SUMAC@ ACM Multimedia, 2024)

A Joint-Reasoning based Disease Q&A System. - Prakash Chandra Sukhwal; Vaibhav Rajan; Atreyi Kankanhalli (CoRR, 2024)

Mixture-Models: a one-stop Python Library for Model-based Clustering using various Mixture Models. - Siva Rajesh Kasa; Yijie Hu; Santhosh Kumar Kasa; Vaibhav Rajan (CoRR, 2024)

ASTER: A Method to Predict Clinically Relevant Synthetic Lethal Genetic Interactions. - Herty Liany; Aishwarya Jayagopal; Dachuan Huang; Jing Quan Lim; Nur Izzah NBH; Anand Jeyasekharan; Choon Kiat Ong; Vaibhav Rajan (IEEE J. Biomed. Health Informatics, 2024)

Evaluating Explanations From AI Algorithms for Clinical Decision-Making: A Social Science-Based Approach. - Suparna Ghanvatkar; Vaibhav Rajan (IEEE J. Biomed. Health Informatics, 2024)

GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment. - Aishwarya Jayagopal; Yanrong Zhang; Robert John Walsh; Tuan Zea Tan; Anand D. Jeyasekharan; Vaibhav Rajan (ICLR, 2025)

A Joint LLM-KG System for Disease Q&A. - Prakash Chandra Sukhwal; Vaibhav Rajan; Atreyi Kankanhalli (IEEE J. Biomed. Health Informatics, 2025)

Farm-Level, In-Season Crop Identification for India. - Ishan Deshpande; Amandeep Kaur Reehal; Chandan Nath; Renu Singh; Aayush Patel; Aishwarya Jayagopal; Gaurav Singh; Gaurav Aggarwal; Amit Agarwal; Prathmesh Bele; Sridhar Reddy; Tanya Warrier; et al. (CoRR, 2025)