Vaibhav RAJAN

Assistant Professor

  • M.S. (Computer Science), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
  • Ph.D. (Computer Science), École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
  • B.E. (Computer Science), Birla Institute of Technology and Science (BITS), Pilani, India

Vaibhav Rajan is an Assistant Professor in the Department of Information Systems and Analytics at the School of Computing, National University of Singapore (NUS). Earlier, he was a Senior Research Scientist at Xerox Research where he led a project on Clinical Decision Support Systems for over four years. He has also worked as a consultant at Hewlett-Packard Labs and as Chief Data Scientist at Videoken (an education technology startup). Vaibhav Rajan received his PhD and Master’s degrees in Computer Science from the Swiss Federal Institute of Technology at Lausanne (EPFL), Switzerland in 2012 and 2008 respectively and his Bachelor’s degree in Computer Science from Birla Institute of Technology and Science (BITS), Pilani, India in 2004. His research interests include Machine Learning, Algorithm Design and their applications, primarily in Healthcare and Bioinformatics. He is a recipient of the ERS IASC Young Researchers Award 2014 given by European Regional Section (ERS) of the International Association for Statistical Computing (IASC). To know more about current projects and openings in his group, please visit:


  • Modeling Heterogeneous Clinical and Genomic Data

  • Combined Data-driven and Knowledge-based Models

  • Deep Multi-View Learning

  • Unsupervised Learning

  • Applications In Healthcare and Bioinformatics


Personalized Treatment Prediction for Therapy-resistant Cancers

Cancer treatment is becoming highly personalized, where a patient's genomic characteristics are used to determine appropriate therapy choices. In this project we address the algorithmic challenges of learning from genomic data and electronic medical records in light of continuously evolving biomedical knowledge, to support treatment decision making.


Explainable Risk Models for Heterogeneous Patient Populations in Critical Care

This project aims to improve the accuracy and explainability of clinical risk prediction (e.g., of unforeseen adverse events) in critical care by explicitly modeling underlying heterogeneous subpopulations and through the use of auxiliary knowledge.




  • "ExpertNet: A Deep Learning Approach to Combined Risk Modeling and Subtyping in Intensive Care Units," Shivin Srivastava and Vaibhav Rajan, IEEE Journal of Biomedical and Health Informatics (IEEE JBHI), 2023
  • "Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization", Debabrata Mahapatra, Vaibhav Rajan, 37th International Conference on Machine Learning ICML 2020
  • "Predicting Synthetic Lethal Interactions using Heterogeneous Data Sources", Herty Liany, Anand Jeyasekharan, Vaibhav Rajan, Bioinformatics, 2020
  • "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 2021
  • "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
  • "Neural Collective Matrix Factorization for Integrated Analysis of Heterogeneous Biomedical Data", Ragunathan Mariappan, Aishwarya Jayagopal, Zong Sien Ho, Vaibhav Rajan, Bioinformatics 2022
  • "Maximum Likelihood Reconstruction of Ancestral Networks by Integer Linear Programming", Vaibhav Rajan, Ziqi Zhang, Carl Kingsford, Xiuwei Zhang, Bioinformatics, 2021
  • "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), 2022
  • "Improved Inference of Gaussian Mixture Copula Model for Clustering and Reproducibility Analysis using Automatic Differentiation", Siva Rajesh Kasa and Vaibhav Rajan, Econometrics and Statistics 2022
  • "Gaussian Mixture Copulas for High-Dimensional Clustering and Dependency-based Subtyping", Siva Rajesh Kasa, Sakyajit Bhattacharya, Vaibhav Rajan, Bioinformatics, 2020
  • "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


  • NUS SoC Faculty Teaching Excellence Award (FTEA) 2019, 2020, 2021

  • ERS IASC Young Researchers Award 2014 given by European Regional Section (ERS) of the International Association for Statistical Computing (IASC)


Advanced Analytics and Machine Learning
Topics in Machine Learning and Optimization


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