Publications
Machine Learning/Computational Statistics
18. 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, ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD 2024 [PDF]
17. WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer, Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh AP, Vaibhav Rajan, 41st International Conference on Machine Learning ICML 2024 [PDF]
16. Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning, Hansheng Xue, Vijini Mallawaarachchi, Lexing Xie, Vaibhav Rajan, Twelfth International Conference on Learning Representations ICLR 2024
15. Avoiding inferior clusterings with misspecified Gaussian mixture models, Siva Rajesh Kasa and Vaibhav Rajan, Nature Scientific Reports, 13, 19164 (2023) [PDF]
14. Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction, Hansheng Xue, Vaibhav Rajan, Yu Lin, 36th Conference on Neural Information Processing Systems NeurIPS 2022
13. RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning, Hansheng Xue, Vijini Mallawaarachchi, Yujia Zhang, Vaibhav Rajan, Yu Lin, 36th AAAI Conference on Artificial Intelligence AAAI 2022
12. Improved Inference of Gaussian Mixture Copula Model for Clustering and Reproducibility Analysis using Automatic Differentiation, Siva Rajesh Kasa and Vaibhav Rajan, Econometrics and Statistics, Volume 22, April 2022, Pages 67-97, https://doi.org/10.1016/j.ecosta.2021.08.010. [PDF]
11. Inferior Clusterings in Misspecified Gaussian Mixture Models, Siva Rajesh Kasa and Vaibhav Rajan, NeurIPS 2021 Workshop Your Model is Wrong: Robustness and misspecification in probabilistic modeling
10. Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks, Hansheng Xue, Luwei Yang, Vaibhav Rajan, Wen Jiang, Yi Wei, Yu Lin, The Web Conference (Formerly WWW) 2021
9. Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization, Debabrata Mahapatra, Vaibhav Rajan, Thirty-seventh International Conference on Machine Learning ICML 2020 [PDF]
8. Deep Collective Matrix Factorization for Augmented Multi-View Learning, Ragunathan Mariappan, Vaibhav Rajan, ECML-PKDD 2019 journal track Machine Learning (Springer), 2019, 1-26, DOI: 10.1007/s10994-019-05801-6 [PDF]
7. Context-Aware Sequential Recommendations with Stacked Recurrent Neural Networks, Rakkappan Lakshmanan, Vaibhav Rajan, The Web Conference (Formerly WWW) 2019 [PDF] [Code]
6. Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks, Aishwarya Jadhav, Vaibhav Rajan, 56th Annual Meeting of the Association for Computational Linguistics ACL 2018 [PDF] [Code]
5. Automatic Hierarchical Table of Contents Generation for Educational Videos, Debabrata Mahapatra, Ragunathan Mariappan, Vaibhav Rajan, The Web Conference (Formerly WWW) 2018 [PDF]
4. Vine Copulas for Mixed Data: Multi-view Clustering for Mixed Data Beyond Meta-Gaussian Dependencies, Lavanya Sita Tekumalla, Vaibhav Rajan, Chiranjib Bhattacharyya, ECML-PKDD 2017 journal track Machine Learning (Springer), 2017, 1-27 [PDF]
3. ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets, Sakyajit Bhattacharya, Vaibhav Rajan, Harsh Shrivastava, 31st AAAI Conference on Artificial Intelligence AAAI 2017 [PDF]
2. Dependency Clustering of Mixed Data with Gaussian Mixture Copulas, Vaibhav Rajan, Sakyajit Bhattacharya, 25th International Joint Conference on Artificial Intelligence IJCAI 2016 [PDF]
1. Unsupervised Learning using Gaussian Mixture Copula Model, Sakyajit Bhattacharya, Vaibhav Rajan, 21st International Conference on Computational Statistics COMPSTAT 2014 [PDF] (Won us the ERS IASC Young Researchers Award 2014 for best paper by researchers under 35 given by the European Regional Section (ERS) of the International Association for Statistical Computing (IASC)).
Healthcare and Precision Medicine
31. 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, ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD 2024 [PDF]
30. WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer, Kumar Shubham, Aishwarya Jayagopal, Syed Mohammed Danish, Prathosh AP, Vaibhav Rajan, 41st International Conference on Machine Learning ICML 2024 [PDF]
36. Cancer Drug Response in Patients via Semi-supervised Deep Co-training, Nicole Ren Jiahui, Aishwarya Jayagopal, Anand Jeyasekharan, Vaibhav Rajan, Abstract Presentation (TransMed COSI), 32nd Conference on Intelligent Systems for Molecular Biology ISMB 2024
35. Evaluating Explanations from AI Algorithms for Clinical Decision-Making: A Social Science-based Approach, Suparna Ghanvatkar and Vaibhav Rajan, IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), vol. 28, no. 7, pp. 4269-4280, July 2024, doi: 10.1109/JBHI.2024.3393719 [PDF]
34. The spatial organization of cells expressing MYC and BCL2 affects immune microenvironment composition and prognosis in DLBCL, Shridar, Shruti, Michal Marek Hoppe, Patrick Jaynes, Gayatri Kumar, Siddham Jasoria, Ziwei Meng, Vaibhav Rajan, Kasthuri Kannan, Claudio Tripodo, and Anand Devaprasath Jeyasekharan. Cancer Research 84, no. 6_Supplement (2024): 3648-3648
33. 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 Journal of Biomedical and Health Informatics (IEEE JBHI), vol. 28, no. 3, pp. 1785-1796, March 2024, doi: 10.1109/JBHI.2024.3354776 [PDF]
32. ExpertNet: A Deep Learning Approach to Combined Risk Modeling and Subtyping in Intensive Care Units, Shivin Srivastava, Vaibhav Rajan, IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), Vol. 27, No. 10, pp. 5076-5086, Oct. 2023 [PDF]
31. Classification aware clustering for Risk Prediction and Subtyping in Clinical Data, Shivin Srivastava, Vaibhav Rajan, Workshop on Information Technologies and Systems WITS 2023
30. Theory-driven Evaluation of Usefulness of Explanations in Clinical Decision Support Systems, Suparna Ghanvatkar and Vaibhav Rajan, Conference on Health IT and Analytics CHITA 2023
29. Unsupervised discovery of biomedical associations from clinical and auxiliary data, Aishwarya Jayagopal, Lin Jing, Folefac Aminkeng, Kee Yuan Ngiam and Vaibhav Rajan, Conference on Health IT and Analytics CHITA 2023
28. Towards a Theory-Based Evaluation of Explainable Predictions in Healthcare, Suparna Ghanvatkar, Vaibhav Rajan, International Conference on Information Systems ICIS 2022
27. Designing a Healthcare QA Assistant: A Knowledge Based Approach, Prakash Sukhwal, Atreyi Kankanhalli, Vaibhav Rajan, International Conference on Information Systems ICIS 2022
26. A Deep Learning Model for Risk Prediction and Subtyping with Temporal Clinical Data, Shivin Srivastava, Vaibhav Rajan, Workshop on Information Technologies and Systems WITS 2022
25. Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data, Ragunathan Mariappan, Aishwarya Jayagopal, Zong Sien Ho, Vaibhav Rajan, Bioinformatics, Volume 38, Issue 19, 1 October 2022, Pages 4554–4561 [PDF]
24. An Algorithm to Mine Therapeutic Motifs for Cancer from Networks of Genetic Interactions, Herty Liany, Yu Lin, Anand Jeyasekharan, Vaibhav Rajan, IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), Vol. 26, No. 6, pp. 2830-2838, June 2022, doi: 10.1109/JBHI.2022.3141076.
23. Patient Representation Learning from Heterogeneous Data Sources and Knowledge Graphs using Deep Collective Matrix Factorization: Evaluation Study, Sajit Kumar, Alicia Nanelia, Ragunathan Mariappan, Adithya Rajagopal, Vaibhav Rajan, JMIR Medical Informatics 2022;10(1):e28842 [PDF]
22. Adverse Drug Event Prediction using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Evaluation, Soham Dasgupta, Aishwarya Jayagopal, Abel Lim Jun Hong, Ragunathan Mariappan, Vaibhav Rajan, JMIR Medical Informatics 2021;9(10):e32730 [PDF]
21. Multi-Disease Predictive Analytics: A Clinical Knowledge-Aware Approach, Qiu Lin, Sruthi Gorantla, Vaibhav Rajan, Bernard Tan, ACM Transactions on Management Information Systems (ACM TMIS), Volume 12, Issue 3, June 2021, Article no. 19, Pages 1-34, https://doi.org/10.1145/3447942 [PDF]
20. Adverse Drug Event Prediction using Noisy Literature-Derived Knowledge Graphs, Abel Lim, Ragunathan Mariappan, Vaibhav Rajan, International Conference on Information Systems ICIS 2020 [PDF]
19. Mining Pathway Associations from Networks of Mutual Exclusivity Interactions, Herty Liany, Yu Lin, Anand Jeyasekharan, Vaibhav Rajan, 24th International Conference on Research in Computational Molecular Biology, Satellite: Computational Cancer Biology RECOMB-CCB 2020 [PDF]
18. Predicting Synthetic Lethal Interactions using Heterogeneous Data Sources, Herty Liany, Anand Jeyasekharan, Vaibhav Rajan, Bioinformatics, Volume 36, Issue 7, 1 April 2020, Pages 2209–2216 (Oxford University Press) [PDF]
17. Gaussian Mixture Copulas for High-Dimensional Clustering and Dependency-based Subtyping, Siva Rajesh Kasa, Sakyajit Bhattacharya, Vaibhav Rajan, Bioinformatics, Volume 36, Issue 2, 15 January 2020, Pages 621–628 (Oxford University Press) [PDF]
16. Deep Recurrent Neural Networks for Mortality Prediction in Intensive Care using Clinical Time Series at Multiple Resolutions, Suparna Ghanvatkar, Vaibhav Rajan, International Conference on Information Systems ICIS 2019 [PDF]
15. Battling Alzheimer’s Disease through Early Detection: A Deep Multimodal Learning Approach, Qiu Lin, Vaibhav Rajan, Bernard Tan, International Conference on Information Systems ICIS 2019 [PDF]
14. Joint Mixed Membership Clustering for Identifying Adverse Drug Events, Jiang Liu, Vaibhav Rajan, Workshop on Information Technologies and Systems WITS 2019
13. Deep Collective Matrix Factorization for Augmented Multi-View Learning, Ragunathan Mariappan, Vaibhav Rajan, Machine Learning (Springer), 2019, 1-26, DOI: 10.1007/s10994-019-05801-6 [PDF]
12. User Models for Personalized Physical Activity Interventions: Scoping Review, Suparna Ghanvatkar, Atreyi Kankanhalli, Vaibhav Rajan, JMIR Mhealth Uhealth 2019;7(1):e11098. DOI: 10.2196/11098
11. Multiple Disease Prediction: A Multi-label Learning Approach Using Deep Generative Learning and Biomedical Knowledge, Qiu Lin, Vaibhav Rajan, Bernard Tan, Workshop on Information Technologies and Systems WITS 2018
10. Detecting Temporal Pattern Profiles from Smartphones for User Activity Analysis, Suparna Ghanvatkar, Vaibhav Rajan, Atreyi Kankanahalli, International Conference on Information Systems ICIS 2018 [PDF]
9. A Dual Boundary Classifier for Predicting Acute Hypotensive Episodes in Critical Care, Sakyajit Bhattarcharya, Vijay Huddar, Vaibhav Rajan, Chandan K Reddy, PLoS ONE 13:2, e0193259, 2018 [PDF]
8. Prediction and Imputation in Irregularly Sampled Clinical Time Series Data using Hierarchical Linear Dynamical Models, Abhishek Sengupta, Prathosh AP, Satya Narayan Shukla, Vaibhav Rajan, Chandan K Reddy, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE EMBC 2017 [PDF]
7. ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets, Sakyajit Bhattacharya, Vaibhav Rajan, Harsh Shrivastava, 31st AAAI Conference on Artificial Intelligence AAAI 2017 [PDF]
6. Predicting Complications in Critical Care using Heterogeneous Clinical Data, Vijay Huddar, Bapu Koundinya Desiraju, Vaibhav Rajan, Sakyajit Bhattacharya, Shourya Roy, Chandan K Reddy, Special Section on Big Data Analytics for Smart and Connected Health, IEEE Access 4, 7988-8001, 2016 [PDF]
5. A Statistical Model for Stroke Outcome Prediction and Treatment Planning, Abhishek Sengupta, Vaibhav Rajan, Sakyajit Bhattacharya, GRK Sarma, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE EMBC 2016 [PDF]
4. Clinical Decision Support for Stroke using Multiview Learning based Models for NIHSS Scores, Vaibhav Rajan, Sakyajit Bhattacharya, Ranjan Shetty, Amith Sitaram, Vivek G Raman, PAKDD 2016 Workshop: Predictive Analytics in Critical Care (PACC) [PDF]
3. Classification with Imbalance: A Similarity based Method for Predicting Respiratory Failure, Harsh Shrivastava, Vijay Huddar, Sakyajit Bhattacharya, Vaibhav Rajan, IEEE International Conference on Bioinformatics and Biomedicine IEEE BIBM 2015 [PDF]
2. A Novel Classification Method for Predicting Acute Hypotensive Episodes in Critical Care, Sakyajit Bhattacharya, Vaibhav Rajan, Vijay Huddar, 5th ACM Conference on Bioinformatics, Computational Biology and Health Informatics ACM BCB 2014 [PDF]
1. Predicting Postoperative Acute Respiratory Failure in Critical Care using Nursing Notes and Physiological Signals, Vijay Huddar, Vaibhav Rajan, Sakyajit Bhattacharya, Shourya Roy, 36th Annual International Conference of IEEE Engineering in Medicine and Biology Society IEEE EMBC 2014 [PDF]
Bioinformatics
28. scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection, Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika Jain, Mirjana Efremova, Sarah Teichmann, Vaibhav Rajan and Xiuwei Zhang, RECOMB 2024 Highlights (Poster Presentation)
27. Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning, Hansheng Xue, Vijini Mallawaarachchi, Lexing Xie, Vaibhav Rajan, Twelfth International Conference on Learning Representations ICLR 2024
26. scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection, Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika Sarkin Jain, Mirjana Efremova, Sarah Teichmann, Vaibhav Rajan, and Xiuwei Zhang, Nature Communications, 14, 384 (2023) [PDF] Editor's Pick (Featured Content)!
25. Metagenomic Binning using Graph Neural Networks, Hansheng Xue, Vijini Mallawaarachchi, Yu Lin, Lexing Xie, Vaibhav Rajan, Abstract Presentation (Microbiome COSI), 31st Conference on Intelligent Systems for Molecular Biology ISMB 2023
24. Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data, Ragunathan Mariappan, Aishwarya Jayagopal, Zong Sien Ho, Vaibhav Rajan, Bioinformatics, Volume 38, Issue 19, 1 October 2022, Pages 4554–4561 [PDF]
23. Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction, Hansheng Xue, Vaibhav Rajan, Yu Lin, 36th Conference on Neural Information Processing Systems NeurIPS 2022
22. RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning, Hansheng Xue, Vijini Mallawaarachchi, Yujia Zhang, Vaibhav Rajan, Yu Lin, 36th AAAI Conference on Artificial Intelligence AAAI 2022
21. Integrating unpaired scRNA-seq and scATAC-seq with unequal cell type compositions, Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Mika Jain, Mirjana Efremova, Sarah Teichmann, Vaibhav Rajan, Xiuwei Zhang, ICML 2021 Workshop on Computational Biology
20. Maximum Likelihood Reconstruction of Ancestral Networks by Integer Linear Programming, Vaibhav Rajan, Ziqi Zhang, Carl Kingsford, Xiuwei Zhang, Bioinformatics, Volume 37, Issue 8, 15 April 2021, Pages 1083-1092 [PDF]
19. MetaBCC-LR: Metagenomics Binning by Coverage and Composition for Long Reads, Anuradha Wickramarachchi, Vijini Mallawaarachchi, Vaibhav Rajan and Yu Lin, Bioinformatics Volume 36, Issue Supplement_1, July 2020, Pages i3–i11 (Proceedings of the 26th Conference on Intelligent Systems for Molecular Biology ISMB 2020) [PDF]
18. Analysis of gene copy number changes in tumor phylogenetics, Jun Zhou, Yu Lin, Vaibhav Rajan, William Hoskins, Jijun Tang, BMC Algorithms in Molecular Biology, 11:26, 2016 [PDF]
17. Maximum parsimony analysis of gene copy number changes in tumor phylogenetics, Jun Zhou, Yu Lin, Vaibhav Rajan, William Hoskins, Jijun Tang, Proc. 15th Workshop on Algorithms in Bioinformatics WABI 2015 [PDF]
16. A method of alignment masking for refining the phylogenetic signal in multiple sequence alignments, Vaibhav Rajan, Molecular Biology and Evolution, Oxford University Press, 2013 Mar; 30(3): 689-712 [PDF] [Code]
15. TIBA: A tool for phylogeny inference from rearrangement data with bootstrap analysis, Yu Lin, Vaibhav Rajan, BME Moret, Bioinformatics, 2012 Dec 15;28(24):3324-5 [PDF] [Code]
14. Bootstrapping Phylogenies Inferred from Rearrangement Data, Yu Lin, Vaibhav Rajan, BME Moret, BMC Algorithms in Molecular Biology, 7:21,2012 (best papers from WABI '11) [PDF]
13. A metric for phylogenetic trees based on matching, Yu Lin, Vaibhav Rajan, BME Moret, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9, 4 (2012), 1014-1022 (best papers from ISBRA'11) [PDF]
12. Bootstrapping Phylogenies Inferred from Rearrangement Data. Yu Lin, Vaibhav Rajan, BME Moret, Proc. 11th Workshop on Algorithms in Bioinformatics WABI 2011, Lecture Notes in Computer Science 6833, 175-187, Springer Verlag (2011) [PDF]
11. A metric for phylogenetic trees based on matching. Yu Lin, Vaibhav Rajan, BME Moret, Proc. 7th Int'l Symp. of Bioinformatics Research and Applications ISBRA 2011 [PDF]
10. Fast and accurate phylogenetic reconstruction from high-resolution whole-genome data and a novel robustness estimator, Yu Lin, Vaibhav Rajan, BME Moret, Journal of Computational Biology 18, 9, 1131-1139 (2011) [PDF]
9. Fast and accurate phylogenetic reconstruction from high-resolution whole-genome data and a novel robustness estimator, Yu Lin, Vaibhav Rajan, BME Moret, Proc. 8th RECOMB Workshop on Comparative Genomics RECOMB-CG 2010; in Lecture Notes in Computer Science 6398, 137-148, Springer Verlag (2010) [PDF]
8. Estimating true evolutionary distances under rearrangements, duplications, and losses, Yu Lin, Vaibhav Rajan, Krister Swenson, BME Moret, BMC Bioinformatics 2010, 11(Suppl 1):S54 [PDF]
7. Estimating true evolutionary distances under rearrangements, duplications, and losses, Yu Lin, Vaibhav Rajan, Krister Swenson, BME Moret, Proc. 8th Asia-Pacific Bioinformatics Conference APBC 2010 [PDF]
6. Heuristics for the inversion median problem, Vaibhav Rajan, A.W. Xu, Yu Lin, Krister Swenson, BME Moret, BMC Bioinformatics 2010, 11(Suppl 1):S30 [PDF]
5. Heuristics for the inversion median problem, Vaibhav Rajan, A.W. Xu, Yu Lin, Krister Swenson, BME Moret, Proc. 8th Asia-Pacific Bioinformatics Conference APBC 2010 [PDF]
4. Sorting signed permutations in O(nlogn) time, Krister Swenson, Vaibhav Rajan, Yu Lin, and Bernard Moret, Journal of Computational Biology. March 2010, 17(3): 489-501 [PDF]
3. Hurdles and Sorting by Inversions: Combinatorial, Statistical and Experimental Results, Krister Swenson, Yu Lin, Vaibhav Rajan and Bernard Moret, Journal of Computational Biology. October 2009, 16(10): 1339-1351 [PDF]
2. Sorting signed permutations in O(nlogn) time, Krister Swenson, Vaibhav Rajan, Yu Lin and Bernard Moret, Proc. 13th International Conference on Research in Computational Molecular Biology RECOMB 2009; in Lecture Notes in Computer Science 5541, 386-399, Springer Verlag (2009). [PDF]
1. Hurdles Hardly Have to be Heeded, Krister Swenson, Yu Lin, Vaibhav Rajan and Bernard Moret, Proc. 6th RECOMB Workshop on Comparative Genomics RECOMB-CG 2008; in Lecture Notes in Computer Science 5267, 239-249, Springer Verlag (2008). [PDF]
Other Machine Learning Applications
12. Dependency Modeling with Copulas in Multi-Armed Bandits, Siva Rajesh Kasa and Vaibhav Rajan, International Conference on Information Systems ICIS 2021 [PDF]
11. Expect the Unexpected: Engaging Users via Serendipitous Recommendations, Cui Wei, Vaibhav Rajan, Zhenhui Jiang, International Conference on Information Systems ICIS 2021 [PDF] [Nominated for Best Short Paper Award in ICIS 2021]
10. An IoT-based DSS using Vehicle Movement Data for Nature Parks, Prakash Sukhwal, Atreyi Kankanhalli, Vaibhav Rajan, International Conference on Information Systems ICIS 2020
9. Inferring Concept Prerequisite Relations from Online Educational Resources, Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan, 31st Annual Conference on Innovative Applications of Artificial Intelligence IAAI-19 [PDF] [Code]
8. VideoKen: Automatic Video Summarization and Course Curation to Support Learning, Debabrata Mahapatra, Ragunathan Mariappan, Vaibhav Rajan, Kuldeep Yadav, Seby A, Sudeshna Roy, Kundan Shrivastava, Aishwarya Yadav, The Web Conference 2018 (27th Edition of Former WWW) [PDF]
7. 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, Human Computation (2015) 2:1:105-131 [PDF]
6. CrowdUtility: A Recommendation System for Crowdsourcing Platforms, Deepthi Chander, Sakyajit Bhattacharya, L. Elisa Celis, Koustuv Dasgupta, Saraschandra Karanam, Vaibhav Rajan, Avantika Gupta, Conference on Human Computation & Crowdsourcing HCOMP 2014 [PDF]
5. Adaptive Performance Optimization over Crowd Labor Channels, Saraschandra Karanam, L. Elisa Celis, Deepthi Chander, Koustuv Dasgupta, Vaibhav Rajan, Conference on Human Computation & Crowdsourcing HCOMP 2014 [PDF]
4. CrowdControl: Online learning for optimal task scheduling on crowd platforms, Vaibhav Rajan, Sakyajit Bhattacharya, L Elisa Celis, Deepthi Chander, Koustuv Dasgupta, Saraschandra Karanam, 30th International Conference on Machine Learning ICML 2013 Workshop: Machine Learning Meets Crowdsourcing [PDF]
3. Understanding Dynamic Performance Variability Across Multiple Crowdsourcing Platforms, Saraschandra Karanam, Vaibhav Rajan, Koustuv Dasgupta, Short Paper, ACM Web Science 2013 [PDF]
2. Adaptive Crowdsourcing for Temporal Crowds, Laura Elisa Celis, Koustuv Dasgupta and Vaibhav Rajan, 3rd Temporal Web Analytics Workshop TempWeb at WWW 2013 [PDF]
1. CrowdUtility: Know The Crowd That Works For You, Dasgupta, K., Rajan, V., Karanam, S., Balamurugan, C. and Piratla, N. Extended Abstracts, ACM SIGCHI Conference on Human Factors in Computing Systems CHI 2013 [PDF]