For this week’s ML practitioners series, Analytics India Magazine(AIM) got in touch with Valliappa Lakshmanan(Lak), Director for Data Analytics and AI Solutions at Google Cloud where he also founded Google’s Advanced Solutions Lab ML Immersion program. He currently leads a team that builds software solutions for business problems using Google Cloud’s data analytics and machine learning products. He is also the author of a handful of popular ML books. In this interview, Lak gives us a glimpse of how data gets leveraged from his vantage point.

AIM: Could you please tell us about your educational background?

Lak: I pursued my PhD in Electrical and Computer Engineering from the University of Oklahoma and my Master’s in Biomedical engineering from The Ohio State University in Columbus, Ohio. I did BTech in Electronics and Communications Engineering from the Indian Institute of Technology, Madras.

AIM: How did your journey in machine learning begin? Your fascination with algorithms. How did it all start?

Lak: My interest in Digital Signal Processing and Image Processing started in college when I got introduced to these subjects. My UG Project was to build a transducer to monitor heart rates. While applying for Masters I was keen to pursue my interest in this area and a lot of my coursework focused around image processing and computer vision. My MS project was on identifying mitral valves from ultrasound images.

My experience of building image processing for ultrasound helped me in getting my first job in weather forecasting. It turns out that much of the signal processing math is similar between ultrasound and ground-based weather radar. The research lab that I worked at – at University of Oklahoma and National Oceanic and Atmospheric Administration (NOAA) was a great opportunity and helped me enhance my skills and knowledge on weather detection algorithms.

As datasets grew over time, I continued my exploration of new technologies and started to apply machine learning methods like genetic algorithms and neural networks to those problems. In the 2010’s when Deep Learning was well on its way to becoming the backbone technology for all sorts of big data problems, it was a natural transition to research areas I was already involved in.

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