Saishruthi Swaminathan is an Advisory Data Scientist at IBM's AI Strategy and Innovation division. Previously, she was a technical lead and data scientist in the IBM Center for Open-Source Data and AI Technologies team, whose main focus is to democratize data and AI through open source technologies. She has a Masters's in Electrical Engineering that specializes in Data Science and a Bachelor's degree in Electronics and Instrumentation. Her passion is to dive deep into the ocean of data, extract insights, and use AI for social good. Previously, she worked as a Software Developer on a mission to spread the knowledge and experience she acquired in her learning process. She also leads an initiative to bring education to rural children and organizes meetups that focus on women's empowerment.
Datacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.
Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.
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Here are highlights from my conversation with Saishruthi:
I was born in a rural town in the south of India. My dad worked in the Indian posted service, while my mom managed the house and the office. We used to live in a 400-square feet house. Until high school, I literally had no Internet connection at home. I used to visit the local library to read books and newspapers. I used to pay 30 Indian rupees for 15 minutes of Internet connection, just to get a feel of using the computer.
The experience with less technology and more nature actually grew me into the person I am today. My parents made sure that I only heard things that elevated my thoughts and made me a better person. They gave me the freedom while providing a shell around me so that I did not experience too much social pressure. When I moved to the city for university, I could mold myself as a better person.
After university, I landed a job as a system engineer at Tata Consulting Services. On my first day at the job, I got spec and asked to debug 10,000 lines of production COBOL code. My hands were literally trembling. I didn’t even understand how to get started.
The next day, I was sitting next to my tech lead. He was typing intensely at his keyboard, and I actually wanted to get the same sound. I was so addicted to it that I wanted to get the same sound (even today). Thus, I started enjoying programming. That was the moment that turned my fear into something I enjoyed. I started typing and learning fast, making me more comfortable with programming. I was at Tata for 2 years. At the end of my tenure there, I was the top programmer of my unit, which handled 10 high-priority codebases with over 15,000 lines of COBOL code.
I encountered data science during an internship in Seattle. I love that I was able to make data speak to me in which I understand the language. My interest in programming and my passion for innovation intersects. From there on, I was fortunate to take courses like data mining, statistical ML, neural networks, and probability under the finest professors in San Jose State. I also participated in various research projects and other small projects, from smart city to material strength prediction. In the 1.5 years of my Master’s, the amount of data science learning I had gone exponentially. I used to sleep 2 to 3 hours per day. That’s because I enjoyed learning, so I didn’t feel tired at all.
Your language or fluency does not matter. Instead, try to:
My team is called the Center for Open Source Data and AI Technologies. We are a group of 30+ developers and data scientists around the world. The common goal is to democratize AI, making this technology accessible to everyone.
This is Saishruthi’s personal view, given in her talk titled “Digital Discrimination: Cognitive Bias in Machine Learning.”
Concerns about privacy and responsible AI will be a major topic in the upcoming years, so businesses will be ready to adopt them. As I mentioned before, it’s not just about the tools. It’s also about the cultural change at the organizational level, like having an ethical board. Ethical experts will be involved in data science projects to inform about the governance of data privacy rules. If you use PII based on certain countries, you need to be aware of data regulation rules. Overall, it will be about building, evaluating, and monitoring model performance, such that these models are ethical and responsible.
Furthermore, there will be an increasing amount of research on fairness, robustness, value alignment, transparency, privacy, explainability, and accountability of the ML system.
There are a lot of online courses out there. As an instructor, I needed to show something unique. People enrolled in my courses should be able to get hands-on practice.
Additionally, I tend to give too much information. So I need to be mindful of the difficulty level for the course notebooks. Each learner has their own expectations from the course. As a result, I need to streamline these expectations appropriately.