Debarka Sengupta

Principal Investigator
Google Scholar

Debarka Sengupta received his Ph.D. in Computer Sc. and Engineering from Jadavpur University. Afterward, he spent about three years as a postdoctoral research fellow at the prestigious Genome Institute of Singapore. Before joining IIIT-D he worked as an INSPIRE Faculty at the Machine Intelligence Unit of Indian Statistical Institute. Debarka started his professional career as a software engineer working for Infosys and then IBM. He consulted and advised a number of technology and service-based firms including IPsoft, Datanomers, CoreCompete and Applied Research Works on various data science and business analytics projects. He received the 2007 spot award by Infosys while working there as a software engineer. His U.S. patent on applied social choice theory was awarded by the patent monetization giant Intellectual Ventures. He has twice been nominated for the prestigious INSPIRE Faculty award - in 2014 and 2016. Debarka currently mentors the data science team at Circle of Life healthcare Pvt. Ltd., a prominent, Delhi-based health tech startup.


Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC

Stable Feature Selection using Copula based Mutual Information

Challenges and possible solutions for decoding extranasal olfactory receptors

Analysis of single-cell transcriptomes links enrichment of olfactory receptors with cancer cell differentiation status and prognosis

The Cellular basis of loss of smell in 2019-nCoV-infected individuals

SelfE: Gene Selection via Self-Expression for Single-Cell Data

Integrative analysis and machine learning based characterization of single circulating tumor cells

Improved dropClust R package with integrative analysis support for scRNA-seq data

deepMc: Deep Matrix Completion for Imputation of Single-Cell RNA-seq Data

MenstruLoss: Sensor For Menstrual Blood Loss Monitoring

Staging System to Predict the Risk of Relapse in Multiple Myeloma Patients Undergoing Autologous Stem Cell Transplantation

McImpute : Matrix Completion Based Imputation for Single Cell RNA-seq Data

Texture Classification Using Deep Convolutional Neural Network with Ensemble Learning

Discovery of rare cells from voluminous single cell expression data

AutoImpute : Autoencoder based imputation of single-cell RNA-seq data

Structure aware Principal Component Analysis for single cell RNA-seq data

CellAtlasSearch: a scalable search engine for single cells

dropClust: Efficient clustering of ultra-large scRNA-seq data

Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

ParSel: Parallel Selection of Micro‐RNAs for Survival Classification in Cancers

A scoring scheme for online feature selection: simulating model performance without retraining

FORKS: Finding Orderings Robustly using k-means and Steiner trees

Tumor-derived circulating endothelial cell clusters in colorectal cancer

Fast, scalable and accurate differential expression analysis for single cells

Pathway Informatics

FOCS: Fast Overlapped Community Search

Feature selection using feature dissimilarity measure and density-based clustering: Application to biological data

Weighted Markov Chain Based Aggregation of Biomolecule Orderings

Strong Nash Stability based Approach to Minimum Quasi Clique Partitioning

Determining a relative importance among ordered lists

XRDS Mobilizes

GRF: a greedy rank fusion algorithm for combining microRNA target orderings

Reformulated Kemeny Optimal Aggregation with Application in Consensus Ranking of microRNA Targets

Topological patterns in microRNA–gene regulatory network: studies in colorectal and breast cancer

Score based aggregation of microRNA target orderings

Participation of microRNAs in human interactome: extraction of microRNA–microRNA regulations

Entropy steered Kendall's tau measure for a fair Rank Aggregation

A novel measure for evaluating an ordered list: application in microRNA target prediction