Interview with Venkat Padmanabhan, Deputy Managing Director, Microsoft Research India

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Interview with Venkat Padmanabhan, Deputy Managing Director, Microsoft Research India

Anubha Gupta

Venkat Padmanabhan is Deputy Managing Director at Microsoft Research India in Bengaluru. He was previously with Microsoft Research Redmond, USA for nearly 9 years. Venkat’s research interests are broadly in networked and mobile systems, and his work over the years has led to highly-cited papers and paper awards, technology transfers within Microsoft, and also industry impact. He received the Shanti Swarup Bhatnagar Prize in 2016, the ACM SIGMOBILE Test-of-Time paper award in 2016 and 2019, and the ACM SenSys Test-of-Time paper award in 2019.

He was also among those recognized with the SIGCOMM Networking Systems Award 2020, for contributions to the ns family of network simulators. Venkat holds a B.Tech. from IIT Delhi (from where he received the Distinguished Alumnus award in 2018) and an M.S. and a Ph.D. from UC Berkeley, all in Computer Science, and has been elected a Fellow of the INAE, the IEEE, and the ACM. He is an adjunct professor at the Indian Institute of Science and was previously an affiliate faculty member at the University of Washington. He can be reached online at

Q. Please share some of your recent exciting research works in signal processing/AI/ML/vision, etc. that may have significant societal impact.

Together with colleagues, I have been working on a project called HAMS (Harnessing AutoMobiles for Safety,, where we have developed a smartphone-based system for monitoring drivers and their driving, with a view to improving road safety. Modern smartphones are a rich package of sensing & computation. We use the sensors --- front and rear camera, inertial sensors, and GPS --- in tandem to monitor such safety-related issues such as driver distraction and tailgating. The key technical challenges we have addressed in HAMS include (a) adaptive switching between highly accurate deep neural network based models and much less expensive traditional computer vision techniques, and (b) autocalibration to cater to the diversity of deployment contexts (e.g., camera orientation, vehicle geometry) without requiring the ML models to be trained afresh.

Since HAMS is a smartphone-based system, it can be retrofitted onto existing vehicles, which makes it a practical system, especially in a setting as in India where there is a large installed base of legacy vehicles. An application of HAMS that we are particularly excited about is in automating the driver’s license test, which we first launched in Dehradun in 2019 ( and has since led to engagements with other states and interest from outside the country too. A windshield-mounted smartphone allows HAMS to automatically evaluate and score the driver, and produce a report within minutes. The automated process also means that the testing is compliant with the physical distancing norms, which is welcome in these COVID times!

Q. During these COVID times, companies are working with interns and/or staff remotely. How is your team coping up with the pressure? Please share some innovative measures being taken by your team for carrying out quality research work.

Working remotely for an extended period has certainly been a new and challenging experience for everyone. This has been especially so for the new research fellows and interns who have joined us over the past few months, often straight from college, and whom we have never met in person! While the challenge is daunting, the good news is that there are excellent tools such as Microsoft Teams to enable remote collaboration. To provide an anchor of normalcy in these times, we at Microsoft Research India, and more broadly at Microsoft, have kept up with our routine just as we used to in the pre-COVID times, with weekly lab meetings, regular project meetings, technical talks, and even a virtual offsite to brainstorm on new research ideas. A silver lining of remote work is that it is a great leveler --- no person or group feels disadvantaged because of their location since everyone is equally close or distant from others, so it is just as easy to collaborate with someone in Delhi or Seattle as it is to work with a colleague in Bengaluru.

Q.In your opinion, what are some of the most exciting technical areas or problems, particularly, for researchers working in machine learning, that may play as game changers for people in the coming years?

I can speak as a user of ML, since I am not an ML researcher myself. A problem area that I am particularly excited about, and one where my colleagues and I are working, is data-driven systems and networking. With the rise of the cloud, and the concentration of computing that it represents, there is the opportunity to instrument and gather large volumes of telemetry about the working of the system. Such telemetry enables the application of ML to simulate, debug, optimize, and improve systems. For example, we are using data to reduce cost in the context of large first-party cloud services (i.e., where the service and the underlying infrastructure are part of the same entity), and separately to simulate networks to enable a realistic evaluation of networked systems.

Q. At times, we hear about knowledge gaps of the training received in academics and that required by the industry. With the increased impetus on joint collaborations between the two, do you still see some gaps and have suggestions to overcome the same? 

I think the academic training of students should focus, most importantly, on teaching them how to identify, define, and solve problems, including new ones that require the students to learn on their own and figure things out. While a foundation of standardized knowledge and skills is no doubt important, the fact is that the world, especially the technology world, is changing rapidly and will likely continue to do so. So, the ability to acquire and apply new knowledge is just as important, if not more so, than the foundation.

More broadly, collaboration is important and works best when it is seen as mutually beneficial. For instance, industry could leverage the deep expertise of academic researchers, who in turn could benefit from learning about real-world problems from the industry experts and getting access to data. I have been fortunate to have had productive collaborations on many of my projects, e.g., in HAMS, we collaborated with and co-authored research papers with IIIT Hyderabad and IIT Mumbai, and we also partnered with the Institute of Driving and Traffic Research (IDTR) run by Maruti-Suzuki, the largest car manufacturer in India.

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