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Umesh Nandal PhD

Umesh Nandal PhD

Principal NLP Scientist

Connect with Umesh Nandal PhD on LinkedIn

About the author: Umesh is the Principal Machine Learning & NLP scientist in the Content Transformation (CT) department at Elsevier. The CT team is focused on the content production pipelines that enable Elsevier to turn content into answers. Specifically, they combine natural language processing (NLP) and machine learning (ML) methods with domain expertise in order to enrich content into data structures, that drive the analytics that Elsevier’s products require with the quality that Elsevier’s customers expect. With a background in Chemistry and computational biology, Umesh is applying state-of-the-art methods in ML and NLP to improve or build new life science products of Elsevier that can help researchers in getting correct answers to their questions quickly. Umesh has several years of experience in data analytics. Prior to joining Elsevier, he used various ML and computational approaches to analyze molecular data generated from high-throughput technologies to understand biological processes in healthy and diseased organisms. During his PhD, he intensively worked on the comparison of mouse models with humans by building a network based integration method that can compare their biological networks.

Posts by Umesh Nandal PhD

Mathematical modeling the emergence and spread of new pathogens: Insight for SARS-CoV-2 and other similar viruses

Posted on April 24th, 2020 in COVID-19

Knowing if and how rapidly an emerging pathogen will spread through a population enables public health officials to make well-informed decisions to protect the public. Mathematical modeling can provide them this means to predict pathogen spread, but modeling previously unheard of pathogens, like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is challenging.


Webinar: Using machine learning to identify adverse events from scientific literature

Posted on February 5th, 2020 in Pharmacovigilance

The “vigilance” aspect of the pharmacovigilance process can be very challenging. Always being on guard and knowing all of the places to look can be difficult. In a sea of information, it can even seem like a nearly impossible task to maintain awareness of all adverse events (AE). That is why there has been a lot of buzz around technologies that can help automate parts of the pharmacovigilance process.


Life Sciences trends that make me excited for 2020 (and beyond)

Posted on January 20th, 2020 in Pharma R&D

Beginning a new year offers an important opportunity to reflect on the past one. I’ve been thinking a lot about what I learned in 2019, and where I believe my industry is going as we continue further into 2020.