Possible topics for discussion:
Public health authorities are always challenged with the socio-economic dynamics of health and preventing outbreaks of communicable diseases along with managing the prevalence of lifestyle disorders. Ubiquitous sensing has shown to have the potential for early detection of both infections as well as non-communicable diseases. With wearable devices getting more affordable each day, it is evident that in near future, every citizen will be carrying wearable devices to monitor their vitals, activity levels, etc. The question we ask is how public health can utilize this data alongside EMR/EHR data to provide more effective therapy outcomes to patients for improved quality of life.
Lifestyle interventions are a first line of treatment for many important public health issues in India and around the world. Lifestyle changes in addition to medication can help control blood pressure, diabetes, etc and result in health benefits in a cost-effective manner. To help people adopt healthier habits, personalization has the potential to be an impactful tool. This talk highlights some of the aspects related to designing such a platform that can offer personalized coaching tailored to each individual through digital means.
Abstract of Talk
It is imperative to bring equity in the access and benefits of AI systems. I will discuss our work on bridging these gaps by discussing ways in which we brought together NGOs, Academics and Google Researchers to advance AI in Social Good for underserved communities. I will specifically discuss work aimed towards improving the efficacy of public health programs through AI w/ ARMMAN and my recent work on examining public health data through the lens of valuation to improve data quality and accountability.
“Most attempts at building large digital public service applicaitons in national identity systems and national-scale health registries have often been questioned on privacy and fairness grounds and have been difficult to operationalise. There are few such successful systems anywhere in the world. Imprecise articulation of both the theory of public good and privacy threat models, and untenable assumptions regarding privacy safeguards, have made analysis of proportionality difficult and have often generated large-scale mistrust.
In this talk we will discuss a framework for analysing the privacy threat model in such large public service applications.”
State of the art AI and Deep Learning are very data hungry. This comes at significant cost including larger resource costs (multiple expensive GPUs and cloud costs), training times (often times multiple days), and human labeling costs and time. In this talk we present our an overview of our research efforts toward Data Efficient maChIne LEarning (DECILE) and our associated open source platform (http://www.decile.org) in which we attempt to address the following questions. Can we train state of the art deep models with only a sample (say 5 to 10%) of massive datasets, while having negligible impact in accuracy? Can we do this while reducing training time/cost by an order of magnitude, and/or significantly reducing the amount of labeled data required? I will also present an overview of the newly formed Koita Centre for Digital Health at IIT Bombay (https://www.kcdh.iitb.ac.in/).
Avik Ghose received his Master’s in the year 1997. He has more than 20 years of experience in embedded systems and has been with TCS Research since 2006. His area of research is human sensing, and he currently heads a research program called “Connected Digital Health” which aims at improving quality of life using wearables, AI, and vision-based technologies.
Sriram Lakshminarasimhan works with Google Research India on health and well-being related efforts. Prior to Google, he was a Researcher at IBM Research, Bangalore.
His expertise broadly spans the areas of storage and data management, NoSQL databases, query processing and high-performance computing. He received his PhD in Computer Science from North Carolina State University in 2013.
Abstract of Talk
Divy is a Program Manager Lead at Google Research where he focusses on enhancing academic collaborations with Google and leads strategy for the AI for Social Good research group. He is an HCI researcher examining human-AI interactions in low-resource and high-stakes domains such as Public Health, Future of Work, with a keen focus on marginalised communities. Divy has several publications at top-tier venues such as CHI.
Subhashis Banerjee is a professor of computer science at Ashoka University. He is on leave from the Department of Computer Science and Engineering at IIT Delhi, where he has held the Ministry of Urban Development, Microsoft and Naren Gupta chair professorships. He was the head of the department of computer science at IIT Delhi between 2004-2007 and head of the computer centre between 2009-2014. Subhashis is also associated with the School of Public Policy and the Centre for Transportation Research and Injury Prevention at IIT Delhi.
Subhashis’ primary areas of research are computer vision and machine learning, with a special emphasis on geometric algorithms. He has been on the editorial boards of the International Journal of Computer Vision and Computers and Graphics, and has published in leading journals and conferences. He has also worked extensively on design of computing and networking infrastructure and IT services and in developing the supercomputing infrastructure at IIT Delhi, which is the second largest in the country. He has been an academic visitor to several universities and research laboratories all over the world.
Recently he has also developed an interest in policy issues digitisation and society, including digital identity, electronic voting, data and privacy protection, and fairness and reliability of machine learning algorithms.
Subhashis graduated in electrical Engineering from Jadavpur University in 1982 and did his Master’s and PhD from the Indian Institute of Science in 1984 and 1989 respectively.
Ganesh Ramakrishnan (https://www.cse.iitb.ac.in/~ganesh/) is currently serving as an Institute Chair Professor at the Department of Computer Science and Engineering, IIT Bombay. His areas of research include human assisted AI/ML, AI/ML in resource constrained environments, learning with symbolic encoding of domain knowledge in ML and NLP, etc. More recently, he has been focusing his energy on organizing relevant machine learning modules for resource constrained environments into https://decile.org/. In the past, he has demonstrated the impact of such data efficient machine learning in applications such as Video Analytics (https://www.cse.iitb.ac.in/~vidsurv) and OCR (https://www.cse.iitb.ac.in/~ocr) and is seeking to make similar impacts in creating a machine translation eco-system (https://www.udaanproject.org/) and in multi-modal analytics (https://www.cse.iitb.ac.in/~malta/). In the past, he has received awards such as IBM Faculty Award, and awards from Qualcomm, Microsoft as well as IIT Bombay Impactful Research Award and most recently the Dr P.K. Patwardhan Award for technology Development. He also held the J.R. Isaac Chair at IIT Bombay. Ganesh is very passionate about boosting the AI research eco-system for India and toward that, the research by him and his students as well as collaborators has resulted in startups that he has either jointly founded, has transferred technology to, or is mentoring. Ganesh is also currently serving as the Professor-in-charge of the Koita Centre for Digital Health at IIT Bombay (https://www.kcdh.iitb.ac.in/).
