Satish Gupta currently works as Director of AI & Analytics at Cognizant. He provides global support to the company’s pharmaceutical, life sciences and healthcare customers for all R&D, discovery and analytical work.
He supported the clinical and crop science applications of the Bayer Crop Sciences account as a Life Science Domain Consultant at TCS, Delhi. In addition, he was a team member that validated the NGS panels used in oncology to meet CAP/CLIA/NABL testing site compliance requirements.
INDIAai interviewed Satish Gupta to get his take on AI.
It’s great to see someone with a life sciences degree in data science. How did it all start?
Science is an evolutionary subject that is constantly evolving through the implementation of new methodologies and technologies emerging from research. Bioinformatics is a subject that introduces life science students to algorithms, databases, statistics, and programming. All the striving for learning new subjects and the demand for the application of bioinformatics in current scientific research has gradually pushed many of us into data science. There are many good universities and institutes that offer bioinformatics courses and meet the needs of the scientific and pharmaceutical sectors. The application of third/fourth generation technologies in scientific research has poured vast amounts of data into our bucket to inspire us to learn more and make meaningful interpretations of it. It’s been called the data era, and the life science, healthcare and pharmaceutical industries have capitalized on it beautifully.
Who motivated you to pursue an AI career? What was the driving force?
I would say it was a gradual step and ‘bioinformatics’ was a buzzword during our master’s degree and it touched us. I was interested in starting my career in industry after completing my master’s degree in biotechnology, but I wasn’t satisfied for many reasons. The hunt for entry into the industry has made us aware of the upcoming demand for bioinformatics. The bioinformatics course at JNU, New Delhi gave me a good exposure to databases, statistics and programming, which motivated me to continue my work later in research institutes and continue my career in industry in different roles. There is an enormous need for resources for the modern way of looking at data. It’s called “Explainable AI” where these mixes of expertise fit well. As soon as big data becomes part of the journey, AI must follow suit.
What were the first obstacles you encountered? how did you defeat them
As mentioned, my current goal was a career in industry, but I needed help to take a break even after studying bioinformatics. So I started working at the leading research institutes in India to gain experience and break into the industry as they always prefer an experienced candidate to a fresh one. I also came into contact with people from science and industry through various conferences, workshops and meetings. Proactive networking always works best for me. It also enables learning and awareness of new aspects in the scientific field. After working in a research institute for a few years, I got a breakthrough in the industry, but I soon recognized the need for higher education for personal development.
What are your responsibilities as Director of AI and Analytics for Bioinformatics and Life Sciences at Cognizant?
It’s quite a challenging job where I have to keep up to date with the latest trends in the life sciences, healthcare and pharmaceutical industries. Cognizant is a service provider and as a business unit we are focused on the impact of AI and analytics for our business partners based on the required objectives. Therefore, I need to understand the exact requirements from an R&D, discovery and analytics perspective and provide a solution strategy. At the same time, I also try to understand their broader theme of working and collaborating to collect pain points where we can support, offer a solution and have a long-term business relationship.
Tell me about your doctoral thesis. What were your research contributions?
Research focused on examining genetic and environmental modifiers of cancer risk. I was mainly involved in the analysis of the modifying effects of blood plasma/serum selenium and polymorphism in selenium (Se) metabolizing genes on cancer risk in CHEK2- and unselected patients with lung, larynx and colorectal cancer. I also examined the role of methylation in cancer-related and selenoprotein genes in breast cancer. Some of the conclusions were as follows:
- A higher Se concentration is significantly associated with a lower probability of cancer occurrence.
- The Se concentration can be a valuable marker for early detection of cancer in the studied group.
- The effect of blood serum selenium levels on cancer incidence may depend on genotypes in selenoprotein genes.
- Methylation of the BRCA1 promoter in peripheral blood is associated with a risk of breast cancer in patients with BRCA1-negative germline mutations.
I have also collaborated with several research groups and published more than 10 publications during my PhD.
Is coding skills essential for life sciences graduates who want to work in the field of artificial intelligence?
I highly recommend studying programming language if you decide to pursue a career in data science. Again, it depends on the requirement of the role and the responsibilities. For example, the data scientist would require more statistical knowledge with a reasonable understanding and programming experience, and a data engineer would also require an advanced level of algorithm development, experimental design, and programming experience in addition. Understanding cloud technologies is essential as everything is delivered through the cloud. Thanks to many online learning platforms, you can learn and improve your skills.
What advice would you give to someone who wants to work in artificial intelligence research? What should they focus on to move forward?
AI is an application that we can implement in various fields, from healthcare, banking, finance, market research, agriculture, climatology, etc. Understanding any field of interest and finding out the challenges in that particular field can be leveraged using AI. The following approach would be to search for available data and define a problem to be solved using data science methods. Here I assume previous experience with programming. Beginners can start by learning Python or R fundamentals and data science modules. The flow I think is appropriate is a good understanding of the area of interest, knowledge of at least one programming language, knowledge of statistics and cloud-based approaches, a good feel for data, and implementing data science into the problem statement. There are many materials and courses on the internet to get certified.
Which scientific articles and publications have had the greatest impact on your life?
Throughout my career I have always been involved with genetics, genomics and bioinformatics. I admire articles, blogs, and research papers on implementing AI/ML-based approaches to solve drug discovery and precision medicine problems. It is interesting to read about the multi-omics process of analyzing and interpreting OMICS data, integrating data from different sources and how we can implement FAIR guidelines. The post-COVID era has expanded the application of AI/ML approaches in clinical sciences. It is interesting to learn about decentralized trials and the extensive effort to use Real World Data (RWD) for decision making in patient recruitment, patient stratification and adverse drug reactions. AI plays a significant role in the pharmaceutical industry and it would be interesting to see FDA and EMEA AI regulations in medical device development, which would shorten drug development time.