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The Machine Learning Journey Of Aishwarya Srinivasan

The Machine Learning Journey Of Aishwarya Srinivasan

  • I have never restricted myself to a specific domain or kind of modelling technique. I have always liked to explore new domains and learn new algorithms.

Analytics India Magazine got in touch with Aishwarya Srinivasan, AI/ML Innovation Leader, Business Development, IBM, to understand her machine learning journey. An advocate for open-source technologies, Aishwarya holds a postgraduate in Data Science from Columbia University. An ardent reader, she has also founded Illuminate AI – a platform to build and connect people from the artificial intelligence community.

AIM: What drew you to machine learning? Do share your machine learning journey so far.

Aishwarya: The mere excitement behind building solutions that are able to solve real-world challenges. Since my first project in data science, which was to predict stock prices based on macroeconomic factors, I have been allured to working on more and more problems.

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My journey started with researching cloud computing and how data science services sit in a multi-cloud environment. Soon, I was working on predictive and prescriptive modelling in finance, auditing, and social media use-cases. While I was pursuing my undergraduate studies, I was exploring personalisation applications like chatbots and recommendation systems, which was very interesting. I have never restricted myself to a specific domain or kind of modelling technique. I have always liked to explore new domains and learn new algorithms. 

During my Master’s at Columbia University, I explored working on an open-source machine learning package – Scikit Learn. I also worked on a research project with Dr Minjae Kim to predict acute kidney injury based on patient health records and inter-operative patient data. It was an extraordinary learning experience as I could think out of the computational perspective and had to wear a subject matter expert hat. After I joined IBM, I have worked on many industrial use-cases, and one of the major learnings is that data quality issues are a real challenge. I also learned that, unlike college assignments, measuring the effectiveness of your solutions isn’t dependent on any model metric but is more about what business value it creates.

AIM: Tell us about your role at IBM.

Aishwarya: I am currently leading AI and ML initiatives at IBM, where I work cross-functionally with the data science, product, and sales organisation to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. 

AIM: Could you tell us some of the exciting data science works you were part of?

Aishwarya: I worked on a project with the United Nations Environment Program on addressing the sustainable development goal of life below water. The project’s goal was to estimate the total marine litter worldwide and use this information to better facilitate clean-up initiatives at beaches. We used statistical methods to establish a baseline of the estimates and applied bayesian estimation algorithms to extrapolate. 

We addressed crucial questions like the amount of plastic pollution advancing over the next few years, the concentration of plastic waste across different countries, and the effectiveness of clean-up activities. With this information, we can build better policies and initiatives to effectively reduce marine litter. As a result, this project received much appreciation and was highlighted by Forbes in a news article.

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AIM: What is the idea and vision behind your platform Illuminate AI?

Aishwarya: Illuminate AI is very close to my heart, and I value mentorship as a crucial aspect of life. While I try to help as many people as possible who used to reach out to me on LinkedIn, I had limited bandwidth. Hence, I got the idea of establishing a platform through which people can connect with industry experts and engage in a mentor-mentee relationship. Illuminate AI has now grown in a few other directions, having collaborated with organisations like DeepLearning.ai, AI for Good Foundation, The AI Journal, OReilly Media, etc. In addition, we run a weekly technical series called AI Guild which features experts talking about real-world AI case studies they worked on.

AIM: What does your machine learning toolkit look like? What is your advice for ML aspirants?

Aishwarya: I work with a mix of open-source technology and IBM’s Data & AI services in Cloud Pak for Data.

It is important for ML aspirants to establish a focus area. This field is very vast, not just spanning across multiple industries but also across numerous job personas. Hence, one needs to understand which industry they would like to work in and what use-cases they would like to explore and build their skillsets accordingly. Moreover, Data Science and ML demand a solid foundation around SQL, algorithms and databases. 

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