Podcast: Evaluating and validating research that aspires to societal impact in real world scenarios. With Tanuja Ganu
Episode 006 | October 20, 2020
At Microsoft Research India, research focused on societal impact is typically a very interdisciplinary exercise that pulls together social scientists, technology experts and designers. But how does one evaluate or validate the actual impact of research in the real world? Today, we talk to Tanuja Ganu who manages the Societal Impact through Cloud and AI (or SCAI) group in MSR India. SCAI focuses on deploying research findings at scale in the real world to validate them, often working with a wide variety of collaborators including academia, social enterprises and startups.
Tanuja is a Research SDE Manager at Microsoft Research, India. She is currently part of MSR’s new center for Societal impact through Cloud and Artificial Intelligence (SCAI).
Prior to joining MSR, she was a Co-Founder and CTO of DataGlen Technologies, a B2B startup that focuses on AI for renewable energy and sustainability technologies. Prior to this, she has worked as Research Engineer at IBM Research, India.
Tanuja has completed MS in Computer Science (Machine Learning) from Indian Institute of Science (IISc, Bangalore). She has been recognized as MIT Technology Review’s Innovator Under 35 (MIT TR 35) in 2014 and IEEE Bangalore Woman Technologist of the Year in 2018. Her work was covered by top technical media (IEEE Spectrum, MIT Technology Review, CISCO Women Rock IT TV series, IBM Research blog and Innovation 26X26: 26 innovations by 26 IBM women).
Click here (opens in new tab) to go to the SCAI website.
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Transcript
Tanuja Ganu: As the name suggests, SCAI, that is Societal Impact through Cloud and Artificial Intelligence, it is an incubation platform within MSR for us to ideate on such research ideas, work with our collaborators like academia, NGOs, social enterprises, startups, and to test or validate our hypothesis through very well defined real world deployments. At SCAI, it’s an interdisciplinary team of social scientists, computer scientists, software engineers, designers, and program managers from the lab who come together for creating, nurturing and evaluating our research ideas through real world deployments and validations.
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Sridhar: Welcome to the Microsoft Research India podcast, where we explore cutting-edge research that’s impacting technology and society. I’m your host, Sridhar Vedantham.
[Music]
At Microsoft Research India, research focused on societal impact is typically a very interdisciplinary exercise that pulls together social scientists, technology experts and designers. But how does one evaluate or validate the actual impact of research in the real world? Today, we talk to Tanuja Ganu who manages the Societal Impact through Cloud and AI (or SCAI) group in MSR India. SCAI focuses on deploying research findings at scale in the real world to validate them, often working with a wide variety of collaborators including academia, social enterprises and startups.
Tanuja has been recognized as one of MIT Technology Review’s Innovators Under 35 (MIT TR 35) in 2014 and by IEEE Bangalore as a Woman Technologist of the Year in 2018, and her work has been covered by top technical media.
[Music]
Sridhar Vedantham: Tanuja, welcome to the podcast. I’m really looking forward to this particular edition of what we do here. Because, I know that you manage SCAI and it’s quite an intriguing part of the lab. Now before we get into that, tell us a little bit about yourself.
Tanuja Ganu: First of all, thanks Sridhar for having me on the podcast today. And uh, yes, uh, I’m not a full-time researcher, but I’m engineer by training and I have done my Master’s in Computer Science. Over the last decade or so, my work is primarily at the intersection of research and engineering, and it’s on the applied research side. So throughout my experience and journey, working at research labs and start up, I’m very much interested in taking a research idea through the entire incubation phase to validate its applicability in real world problem settings.
Sridhar Vedantham: So, Tanuja, I know you manage this thing called SCAI within the lab and I think it’s a very interesting part of the lab. Talk to us a little bit about that, and especially expand upon what SCAI- the term SCAI- itself stands for, because I myself keep tripping up on it whenever I try to explain it.
Tanuja Ganu: Yes, Sridhar. So since the inception of our lab, the lab has been doing very interesting work in the societal impact space. Additionally, with the advances in artificial intelligence and cloud-based technologies in recent years there are increased opportunities to address some of these societal problems through technology and amplify its positive effect. So as the name suggests, SCAI, that is Societal Impact through Cloud and Artificial Intelligence, it is an incubation platform within MSR for us to ideate on such research ideas, work with our collaborators like academia, NGOs, social enterprises, startups, and to test or validate our hypothesis through very well defined real world deployments. Also our location in India allows us to witness and carefully analyze various socio-economic challenges. So the solutions that we ideate are inspired by Indian settings and in many cases equally applicable to different parts of the world.
Sridhar Vedantham: Interesting, so it sounds like there’s a fair amount of difference between the kind of work that SCAI does and between what the rest of the lab actually does in terms of research.
Tanuja Ganu: So at MSR India, where research work is mainly along three different axes, firstly advancing the state of the art in science and technology, second is inspiring the direction for technology advances, and the third important axis is building the technology for driving societal impact. So SCAI is primarily focused on social impact access and many of our projects also do have very strong academic and technological impact. At SCAI, it’s an interdisciplinary team of social scientists, computer scientists, software engineers, designers, and program managers from the lab who come together for creating, nurturing and evaluating our research ideas through real world deployments and validations. So that’s really the difference in terms of the other type of research that we do at lab and what we do at SCAI.
Sridhar Vedantham: So when you decide to take up a project or accept it under the SCAI umbrella, what do you actually look for?
Tanuja Ganu: Yeah, we look for a few things for defining a SCAI project. So firstly, it should address a significant real-world problem and should have a potential to scale. The second thing is the problem should offer interesting research challenges for our team. The next thing is whether we have credible partners or collaborators with domain expertise to deploy, evaluate and validate of our research. We also look for how we can define rigorous impact evaluation plan for a project. And lastly, we look for what are the feasible graduation paths for the project within two to three years of time horizon.
Sridhar Vedantham: What do you mean by graduation?
Tanuja Ganu: So, um, there are different ways in which a particular project can complete its successful execution at SCAI center, and that’s what we’re really terming it as a graduation. And there could be really different types of graduation path depending upon each type of project.
Sridhar Vedantham: OK, let’s talk a little bit about some of the projects that you are currently doing under the SCAI umbrella. Because to me from what you’ve said so far, it sounds like there’s probably going to be a fairly wide spread of types of projects, and quite a large variety in the type of things that you’re doing there.
Tanuja Ganu: So yes, Sridhar, that’s very true. We are working on a very diverse set of projects right now. And, um, so to give a flavor of our work, I would discuss about two or three projects briefly. The first project is called HAMS that is Harnessing Automobiles for Safety. We all know that road safety is very critical issue and according to World Bank Report globally there are 1.25 million road traffic deaths every year. In India there is one death every 4 minutes. That happens due to road accidents. So, to understand and address this very critical issue of road safety, HAMS project was initiated by our team at MSR, including Venkat Padmanabhan, Akshay Nambi and Satish Sangameswaran. HAMS provides a low cost solution which is being evaluated for automated driver license testing. HAMS includes a smartphone with its associated sensors like camera, accelerometer, etc that is fitted inside a car. It monitors a driver and the driving environment and using AI and edge intelligence, it provides effective feedback on the safe driving practices. So at present, HAMS has been deployed at regional transport office in Dehradun, India for conducting dozens of driver license tests a day, and the feedback from this deployment is very encouraging, since it provides transparency and objectivity to the overall license testing and evaluation process. The second project is in the domain of natural language processing, called Interactive Neural Machine Translation, which was initiated by Kalika Bali and Monojit Choudhury in our NLP team. So, when we look at this problem, there are 7000 plus spoken languages worldwide, and for many many use cases, we often need to translate content from one language to another. Though there are many commercial machine translation tools available today, those are applicable to a very small subset of languages, say 100, which have sufficiently large digital datasets available to train machine learning models. So to aid human translation process as well as for creating digital data set for many low resource or underserved languages, we combine innovations from deep learning and human computer interactions and bring human in the loop. So when we talk about INMT, the initial translation model is bootstrapped using small data set that is available for these languages. And then INMT provides quick suggestions for human translators while they are performing translations. And over time it also helps in creating larger digital datasets which would help in increasing accuracy of translation for such underserved languages. So in INMT we’re currently working with three external collaborators called Pratham Books, Translators Without Borders and CGNet Swara to evaluate and enhance INMT. So just to give few examples, Pratham Books is a nonprofit publisher who would like to translate children story books in as many languages as possible. Translators Without Borders is a nonprofit who is working in the areas of crisis relief, health and education, and they would like to evaluate IN&MT for an Ethiopian language called Tigrinya. Our other collaborator CGNet Swara is working with INMT for collecting Hindi Gondi data set. And just to give you one last flavor of one more project…
Sridhar Vedantham: So I’m sorry, sorry to interrupt, but I was curious, how do you actually go around selecting or identifying partners and collaborators for these projects?
Tanuja Ganu: So when we started thinking about SCAI projects last year, we had initiated a call for proposals where we invited external partners and collaborators to submit various ideas that they do have and the process that they have in addressing some of the societal impact projects and we Interestingly received a huge pool of applications through this call for proposals we received more than 150 applications through that. And through careful evaluation process, as we discussed earlier, we finally selected a few projects to start under SCAI umbrella.
Sridhar Vedantham: OK, so I’m sorry I interrupted. You wanted to…you were speaking about another project.
Tanuja Ganu: Yeah, so just to give one more flavor of the project that we are currently doing which is addressing another important issue of air pollution. So air pollution is another major concern worldwide, with an estimated 7 million deaths every year, and when we look in India, it’s even more serious problem since 13 out of 20 most polluted cities in the world are in India. So to solve the air pollution problem, it is important to get correct monitoring of pollution levels, their timely and seasonal patterns in more granular manner, that is, from multiple locations inside the city. So apart from sophisticated and expensive air pollution monitoring stations feature already available, there are low-cost air pollution sensors which are being deployed for this purpose. But the local sensors tend to drift or develop fault overtime and the entire monitoring and analytical insights are dependent on reliability and correctness of this IoT data. So taking these things into account, we are now evaluating our research project called Dependable IoT for these low-cost air pollution sensors. Dependable IoT helps in automatically identifying and validating the drift or malfunction in the sensors and notifies for recalibration or replacement. So currently we are working with a few startups in this space to evaluate dependable IoT Technology and as the project name such as this is not only limited to air pollution sensing, but this technology is applicable for many other use cases for IoT sensing- in agriculture, food technology or in healthcare. So I guess this gives you a view on some of the diverse projects that now we are doing and working on at present in SCAI.
Sridhar Vedantham: Yeah, so this Dependable IoT thing sounds quite interesting. So correct me if I’m wrong, but essentially, what we’re saying is that we’re trying to figure out ways in which we can ensure that the data that we’re receiving in order to extract information from it and make decisions– we’re actually trying to figure out our trying to make sure that the data itself is solid.
Tanuja Ganu: Absolutely. That’s correct, Sridhar, and it’s like monitoring the monitor, right? So while we’re doing the IoT monitoring and sensing, we need to make sure that the technology that we’re putting in place is being monitored and it’s giving us reliable and correct data.
Sridhar Vedantham: Great. Now what’s also coming across to me throughout this conversation is that given the variety of projects and the variety of collaborators that you’re looking at in SCAI– would I be right in saying that the kind of people that you have in SCAI in addition to the researchers, obviously who are your internal collaborators, the people who are part of SCAI, are they a very diverse and varied set of people?
Tanuja Ganu: Yes, absolutely true, Sridhar. As we discussed earlier, SCAI’s an interdisciplinary team that consists of social scientists, CS researchers, solid software engineers and designers. And we also have a program called SCAI Fellows where fresh under graduates or the candidates who are already working in the industry can join on the specific SCAI project for a fixed time period and contribute towards the development of SCAI project. So particularly in SCAI, in addition to all these technical or academic skills, we’re also looking for people who have passion for societal impact and willingness to do the field work and deployment to scale a research idea.
Sridhar Vedantham: OK, and you know, you might at any point of time be working on say, four, five or six projects. Uh, what happens to these projects once they are completed?
Tanuja Ganu: Yeah, so I would say each project would have a different graduation plan. So whenever a project is complete from the SCAI perspective, we call it as a graduation plan where we would define how this project would then sustainably grow further internally or externally. And this graduation plan would be different for different projects depending upon the nature of the project. So for some of the projects, the graduation plan could be an independent entity that is spun off to take the journey of the project forward by scaling the initial idea to more people, more geographies, or for more use cases. A very good example of this type of graduation plan is a MSR project called 99 DOTS, where researchers like Bill Thies and others at Microsoft Research started this project to address medical adherence for tuberculosis. Over the years, this work has significantly grown and there is an independent entity spun off called Everwell to take the 99 DOTS journey forward. The other type of graduation plan can be putting up a work and technology in the open source wherein the external social enterprises, NGOs or our collaborators can build on top of it and take the solution forward at larger scale. The example of this is our work on interactive machine translation, where we have open sourced our initial work and various collaborators are now using, validating and building on top of this technology.
Sridhar Vedantham: OK, and does the work that you do in SCAI or say the validation that you’re looking for from research projects or the validation you’re looking at of research projects through SCAI– does that feed back further into the research itself, or does it kind of just stay at SCAI?
Tanuja Ganu: So, it has two or I would say it would have multiple pathways, but primarily the first thing is certainly the work that we’re doing is validating certain research hypothesis that we do have. So some of the output or outcome of these SCAI projects is feeding back into the research areas and validating or invalidating the hypothesis to say how the technology is helping to solve a particular research problem or not. But also if the intervention is successful, it would be useful for external collaborators internally, externally for them to take up this idea forward and utilize the technology that we have built at SCAI to taking it to larger scale.
Sridhar Vedantham: OK, so once again coming back to the fact that the projects that you do are of such different nature, how do you actually even define success metrics for SCAI projects?
Tanuja Ganu: Yeah, this is a very interesting question, Sridhar. So, the whole purpose of SCAI, as the name suggests, is about bringing social impact through technology innovations. So there is no one fixed set of metrics that would be applicable for each and every project at SCAI. But our success metrics for these projects are geared towards validating whether technological interventions can support the people and ecosystem and actually help address a specific problem or not. And if it does help solve the problem, then how can we amplify the positive effect using technology? So those are really the metrics that we’re defining on each of the project depending upon nature of the project.
Sridhar Vedantham: So Tanuja, thank you so much for your time. This has been a great conversation and all the best for going forward in SCAI.
Tanuja Ganu: Thank you, Sridhar, for having me here and I really enjoyed discussing these projects and ideas with you. Thank you.
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