Dr Dipankar Sengupta

Dr Dipankar Sengupta


July 2010 - March 2014: Ph.D., Jaypee University of Information Technology (JUIT), India [Thesis: Translational and High-End Computing of Clinical data in India]

July 2003 - June 2007: Bachelor of Technology (Bioinformatics), Jaypee University of Information Technology (JUIT), India

Professional Experience:

Sep. 2021 onwards: Lecturer in Health Data Analytics, School of Life Sciences, University of Westminster, United Kingdom

Apr. 2020 – Sep. 2021: Teaching Associate (Bioinformatics and Computational Biology), PGJCCR, Queen’s University Belfast, United Kingdom

Jan. 2018 - Mar. 2020: Postdoctoral Research Fellow, PGJCCR, Queen’s University Belfast, United Kingdom

Aug. 2014 - Dec. 2017: Postdoctoral Researcher and Teaching Assistant, Artificial Intelligence Lab, Vrije Universiteit Brussels (VUB), Belgium

Apr. 2014 -July 2014: Assistant Professor, Jaypee University of Information Technology (JUIT), India

May 2010 -Mar. 2014: Associate Lecturer, Jaypee University of Information Technology (JUIT), India

Sep. 2007 - Apr. 2010: Associate Consultant (Technology)Publicis Sapient, India

With an undergraduate and postgraduate background in Bioinformatics, I have ~3 years of industrial and ~8 years of post-doctoral research experience in business intelligence, data science, machine learning, relational databases, and software development as applied to clinical and life sciences, with 28 peer-reviewed abstracts, book chapters, and papers in journals/conference proceedings, including 14 as first or senior author. My work has spanned both academic and industrial sectors. 

My research aspires to unveil the precision medicine intricacies like patient stratification, tools for diagnosis/prognosis (temporal computing), and challenges like availability of labeled data, etc. using data science, machine learning, and other computational approaches. 

I also have an interest in addressing challenges in precision nutrition, urban health, sustainable development, etc. via technological solutions like machine learning.

In brief

Research areas

Clinical Informatics, Health Data Analytics, Precision Medicine and Bioinformatics

Skills / expertise

Machine Learning, Relational Databases, Programming Languages - R, Python, Scripting Languages - HTML, PHP and Data Analytics

Supervision interests

Machine Learning in Precision Medicine - Diagnosis, Prognosis, and Other Clinical Applications;, Novel machine learning algorithms to address challenges in Clinical Bioinformatics/Precision Medicine; and Biological Database/Datawarehouse and Data Modelling