Dr Priyank Shukla
Lecturer in Stratified Medicine (Bioinformatics)
Dr Priyank Shukla was appointed to his current post as a Lecturer in Stratified Medicine (Bioinformatics) at Ulster University in July 2016 and is a member of the Northern Ireland Centre for Stratified Medicine research group.
Dr Shukla earned his BSc in Biotechnology (2001-2004) from Bareilly College (MJPR University, Bareilly, India) and MSc in Bioinformatics (2004-2006) from University Institute of Engineering & Technology (CSJM University, Kanpur, India). He then joined Laboratory of Genomics at Department of Histology Embryology and Applied Biology, University of Bologna for three months as a Visiting Researcher to co-work on a bioinformatics project aimed to full parsing of Genbank database.
He completed a PhD in Computer Science (area of research: Machine Learning and Bioinformatics) at Bologna Biocomputing Group under the supervision of Professor Rita Casadio, where he developed Machine Learning based methods for prediction of disulphide bonding states of cysteine residues in proteins.
Dr Shukla undertook a Postdoctoral Scientist (Bioinformatics) position at Department of Biomedical Sciences, University of Veterinary Medicine, Vienna, Austria (2010-2016), where he was responsible for consulting, data analysis and data management of Next Generation Sequencing (NGS) based projects of Jak-Stat Signalling Consortium. His research focused on applying NGS approaches to understand Epithelial to Mesenchymal Transition (EMT) and Jak-Stat Signalling – linking infection, inflammation and cancer.
Scholarships and Awards
- DIEBA Prize for a Young Researcher working in Bioinformatics applied to Functional Genomics.
- ‘Brains-in PhD residential scholarship’ awarded by the Institute of Advanced Studies, University of Bologna (2007-2009)
- 100-Young Indian Researcher Scholarship’ awarded by Ministry of Education, University & Research (MIUR) (2009-2010)
Dr Shukla aims in developing Machine Learning – specifically Artificial Neural Networks (ANN), Hidden Markov Models (HMM) and Support Vector Machines (SVM) based computational methods for insilico biomarker discovery and patients’ stratification in inflammatory diseases and cancer via exploiting high-throughput omics data (genomics, transcriptomics, proteomics & metabolomics).
- BIO337: Mathematical and Computational Methods – 2
- BIO535: Insilico Genomics Proteomics & Metabolomics Analysis Methods
- BIO540: Clinical Research Project
- BIO541: Biomedical Informatics (Module Coordinator)
- BIO831: Biomedical Informatics (Module Coordinator)
- BIO833: Research Project