Ranjini Guruprasad is a Research Scientist at IBM Research Labs (IRL), India. She joined IBM Research India in 2015 as an intern and as a Research Staff Member in 2017. She currently works with the Impact Sciences group and her current work spans different aspects of Agribusiness including building innovative solutions using satellite, weather, and IoT data and in the domains of precision agriculture, water pollution monitoring and carbon accounting for food supply chains. Her research interests are broadly in the areas of data analytics, machine learning, and optimization.

Ranjini holds a PhD degree in Green Communications from the University of San Diego, California, where her research work was focused on energy optimization techniques for base stations and mobile devices and she has publications in leading peer-reviewed journals and conferences. 

Ranjini, can you describe your AI journey?

My AI journey began in late 2016 when IBM Research India started a special initiative on digital farming/precision agriculture to improve farm productivity by increasing the visibility of agronomic states such as soil moisture, crop health, pest and disease risk, weather, etc. of farms. The project required using diverse data sources such as satellites, drones, on-field sensors and historical crop data as well as leveraging digitization, mobile, IoT and cognitive technologies. This motivated me to learn AI concepts and build AI-based systems to predict crop parameters and derive actionable insights early in the crop growing season. The AI tools that we developed ( incorporated in Watson Decision Platform for Agriculture), have been very successfully applied in multiple client projects spanning diverse geographic locations, climatic conditions and crops, enabling farmers to make more informed decisions. I continue to apply AI skills in the domains of water pollution monitoring of rivers and lakes and estimating greenhouse gas emissions from food supply chains. 

What are the major challenges you faced as a woman in reaching where you are right now?

In my journey, the constant conflict between the biological and the career clocks is a continuous challenge that I face like many other women. I think this challenge also presents itself in the dwindling number of women you work with as your career progresses. The reality is, there are very few women at the top, whom you can look up to and derive inspiration. The challenges that women face makes it that much harder to pursue their careers. 

What made you interested in AI?

AI attempts to mimic the human brain and this opens up the immense potential to solve diverse problems. This fascinating aspect of AI interests me the most. Furthermore, my experience with AI in the context of agriculture, and pollution monitoring has been extremely gratifying as these have deep impacts on day to day lives of people. This aspect of AI as a technology to address pressing problems of society is what motivates me to further my AI journey. 

What's your area of expertise in AI and why chose that one? 

I am very interested in the area of learning with less data and transfer learning. In my experiences with real-world scenarios; be it estimating yield as soon as a crop is sown, estimating the pollution levels of huge rivers such as the Ganges, estimating the greenhouse gas emissions of food supply chains, data is not available at the scale required. In order to deliver AI solutions to solve such problems, the ability to learn with less data and transfer the learning is critical. 

What's the one thing that you see AI transforming completely?

I believe AI has the potential to transform drug and sustainable material discovery thereby immensely benefitting mankind and planet earth. 

Your biggest AI nightmare?

AI-based systems can be black boxes, that is, there is a lack of explainability of the decisions or predictions that are output by the systems. Furthermore, AI-based systems can be plagued by biases that are present in the training data. This lack of understanding of why a particular output was produced by the biased systems is a nightmare in scenarios wherein AI-based systems are critical decision-making systems. 

What's your advice for other women who wants to pursue a similar journey?

My advice to women embarking on a similar journey would be to have passion and perseverance to learn AI skills, apply them to solve real-world problems, teach/mentor others and do the above three continually.