Ashis Kumer Biswas
Director of Machine Learning Lab (ML Lab)
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Department of Computer Science and Engineering, |
I am an Assistant Professor of Computer Science and Engineering in the College of Engineering at the University of Colorado Denver. Prior to joining here, I was a lecturer at University of Dhaka from 2010 to 2011. I obtained B.Sc. and M.S. degrees in Computer Science and Engineering from University of Dhaka in 2008 and 2009. I received my Ph.D. degree in Computer Science from The University of Texas at Arlington, Texas in 2016 under the supervision of Professor Jean X. Gao.
I am looking for motivated students interested in my research. Please email me your CV if you are interested.
Research Interests
My main research areas are Machine learning, Data mining, Big Data analysis, and Bioinformatics. The goal of my research is to develop novel algorithms to solve problems from these areas while respecting the underlying problem structures and presenting with scalable platforms that can handle massive, heterogeneous data-sets. My current research projects are on the following fields:
Machine learning, Data Mining, Big data analysis
Ensemble learning, Matrix Completion algorithms, Multi-label learning, Recommender systems, Sparse learning, Graph mining
Cross-language text analysis
Bioinformatics
High Throughput Sequencing (HTS) (a.k.a Next Generation Sequencing (NGS)), eQTL epistasis, SNP, CNV, GWAS data analysis
Non-coding RNA – disease association study
Cancer data analysis to contribute in the advancement of precision medicine
Selected Publications ( complete list )
Effects of low dose ionizing radiation on DNA damage-caused pathways by reverse-phase protein array and Bayesian networks, JBCB’17.
PR2S2Clust: patched RNA-seq read segments’ structure-oriented clustering, JBCB’16.
Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders, BMC Medical Genomics’16.
Robust Inductive Matrix Completion Strategy to Explore Associations between LincRNAs and Human Disease Phenotypes, BIBM’16.
Multi-Block Bipartite Graph for Multimodal Genomic Data, TCBB’16.
Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization, NetMAHIB’15.
Improving Consensus Hierarchical Clustering Framework, CSPS’15.
A Multi-label Classification Framework to Predict Disease Associations of Long non-coding RNAs (lncRNAs), CSPS’14.
NMF-based LncRNA-Disease Association Inference and Bi-clustering, BIBE’14. Best paper award
CNCTDiscriminator: coding and noncoding transcript discriminator—an excursion through hypothesis learning and ensemble learning approaches, JBCB’13.
Infobox Suggestion for Wikipedia Entities, CIKM’12.
Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information, BMC Bioinformatics’10.