Hima Patel is an experienced Data Scientist and Researcher with over 13 years of experience in leading organizations like GE, Shell and now IBM. She currently works as a Research Manager in IBM Research India, leading a research group that is focused on developing novel technologies using Machine Learning and Natural Language Processing and pushing the state-of-art in this area/domain. She is passionate about developing technologies that solve real problems and impact businesses. 

Hima is also a Diversity advocate within IBM Research and outside. She has been part of the Grace Hopper India Conference organization committee for the last four years and has been leading the AI and Tech Expo tracks for the conference. She is charting a new program “Tech Pathways” that will help in reskilling or upskilling for women who are looking for change in careers or are returning to work from a break. She is also the Vice Chair of IEEE Computer Society, Bangalore Chapter and has been actively involved in organizing technical programs. This chapter won the global  - 2019 Outstanding Chapter award from IEEE Computer Society.

Hima is a Post Graduate in Information and Communication technology with a bachelor’s in Computer Engineering. 

Hima, tell us about your AI journey.

I got introduced to AI in my third year of graduation after a professor recommended I read a book on Neural networks.  So back then, even though AI was not really a “hot area” I decided to pursue a Master’s program where I learnt image processing, machine learning etc.  As I was very keen to become a Researcher, I worked with a few start-ups for a hands-on experience and also learn more as well as apply my skills. Soon I got an opportunity to work in GE Healthcare and GE Research, where I explored different problems on medical image analysis: from life science to CT/MRI and multimodal imaging problems. From there on, I worked with the Research group at Shell for a few years -  building machine learning techniques for predictive equipment maintenance and explored problems in that area. At IBM Research, I have been focused on the problems of building question answering systems from enterprise documents like product manuals and troubleshooting documents. And for the last two years, I have been leading the area of assessment of data readiness for machine learning tasks. 

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

I have been very fortunate to have a very supportive family and great set of friends who have always encouraged me to pursue my dreams. When I look back at my journey, I think of it more as a growth path, where the challenges and mistakes have made me a better professional. However, if I have to give advice to my younger self, I would tell her to speak up and communicate ideas proactively and not wait for someone to prompt me, also take out time to understand the larger picture, ecosystem and to not get lost in the details and day to day work.

What made you interested in AI?

As I mentioned earlier, in my third year of graduation, my professor asked me to read a book on neural networks and this was my first introduction to AI and I got immensely interested in the area and started studying more on the subject and even  designed my final year project on AI. Since then, I have enjoyed working in the field of AI and my passion to learn and explore still continues.

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

My expertise is in machine learning and currently I am focused on building tools and techniques that will help democratize this area and allow many people to use machine learning seamlessly. Right now there is an imbalance in the number of experts in this area against the business problems on hand – in such a scenario, there are innumerable possibilities where ML can impact business outcomes and this keeps me excited and motivated. My goal is to build techniques that can help automate mundane and time-consuming segments of the data science pipeline, so that data scientists can utilize their time effectively for other tasks. Please see my blog on Medium on this topic. 

 What's the one thing that you see AI transforming completely? (Eg. cars becoming self driving)

AI has been making great strides in every direction. I would personally like to see more advances in AI that allow a non AI expert to easily modify an AI system for their preference or as they switch use cases, while still not having the need to understand the inner workings of the AI system. 

 Your biggest AI nightmare?

AI is a technology like any other. The technology by itself cannot cause any nightmare. How we put it to use, is a different story. 

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

I would advise that everyone must pursue the area or field that excites them the most.  That will ensure that your job is enjoyable every day. If AI excites you, I recommend taking out the time to learn and understand the fundamentals, do hands- on projects, this will help you to clear your concepts. Once you are well-versed with the fundamentals, there are many libraries and tools available that can help you work in this area.