Dr. Yugal Sharma was Senior Director of Solutions at CAS, with over 15 years of experience in applying and managing data science and machine learning approaches to solve complex problems in the healthcare space. Yugal was a scientist for the National Institutes of Health (NIH), focusing on developing early disease detection algorithms. Since NIH, Yugal helped found a technology startup, followed by a business consulting startup. More recently, he applied his background as a government consultant focusing on analytics strategy for NIH and FDA clients. He has published several scientific articles, as well co-authoring a book chapter on the mining of Electronic Health Records to detect disease signals. Yugal received his PhD in Biophysics from University of Cincinnati, where he graduated with honors.
Latest Content from Yugal Sharma, Ph.D.
May 28, 2021
Find a better route: Optimizing AI for more novel synthetic predictions
AI and machine learning models aid in retrosynthetic planning, but are limited by the training data they have seen. Read on to learn about ways to generate novel predictions by ensuring your data has the necessary diversity and quality to optimize key synthetic planning initiatives.
Case Study: Data quality impact on algorithmic prediction of biological activity
AI and machine learning are showing great promise for enhancing research productivity in many disciplines. However, there are still many well-documented challenges for implementing these technologies in R&D applications and many opportunities to build on the efforts to date for greater success. In fact, one Gartner analyst estimated that as many as 85% of AI-driven projects are not meeting their objectives. As I had posted about previously, I, and many others, believe that one of the primary gaps often impacting these success rates is data quality.
Data quality: The not-so secret sauce for AI and machine learning
The promise of artificial intelligence has always felt more like a future state, but the reality is that many companies are already adopting AI initiatives. This is especially true in the scientific R&D realms. Over the last few years, there has been a huge increase of machine learning and AI initiatives in everything from QSAR models to genomics. According to a 2018 survey, AI adoption grew drastically from 38% in 2017 to 61% in 2018. This occurred across a variety of industries, including healthcare, manufacturing and financial services. However, most early adopters noted one of the biggest challenges to successful implementation involved data, specifically, accessing, protecting, integrating and preparing data for AI initiatives.