The Centres will work in the areas of accurate and safe Data modelling, deployable AI, scientific research in sports data-driven disease and clinical research.
Computational Mathematics & Data Science
While navigating through a shared space with obstacles, pedestrians use different strategies to avoid collision with these obstacles and reach their goals within a limited time. In this paper, we try to understand how the speed and path changes are made by the pedestrians and obstacles, stationary or moving, for effectively maneuvering their shared physical space to reach their targets.
Principal Investigator: Neelesh S. Upadhye
Fair/ Social good in AI
Data, in the real world, often carries a lot of previously held racial, gender-specific, community-specific biases and these biases creep into the algorithms/models which train on the data. Furthermore, the biases in an Indian context might be very different from those in the western world. The goal of the project is to develop algorithmic fairness solutions that cater to the local needs keeping in mind local laws and regulations.
The recent tremendous success in ML/AI is attributed to the advent of deep learning algorithms. While these algorithms have performed extremely well in NLP, Vision, Speech and other domains, deploying them in several applications is often not a comfortable decision for industries. The fundamental reason for this is that deep neural models trade-off explainability with accuracy. The goal of the project is to contribute to the growing literature on explainable machine learning algorithms.
The performance of ML/AI algorithms depend crucially on the distributions from which data arrives. Real-world data often exhibit the phenomenon of ‘data drift’ and AI/ML algorithms must be equipped to handle these. The goal of the project is to develop deployable ML/AI algorithms that learn continuously in the field and adapt to changing data distribution/drifts.
Private and Safe AI
Every prediction of a machine learning algorithm can potentially leak information about the underlying model. Furthermore, even if the end-user is reliable, there may be several types of privacy attacks and breaches. It is thus important to develop safe and secure AI before they are deployed. The goal of the project is to develop private and safe AI algorithms
AI for Edge
Several user centric applications can benefit if deep learning algorithms can be run on the edge. However, at the moment thisseems a challenge because of hardware and software restrictions. To achieve a truly wide scale deployment of deep learning algorithms, one needs to make it work seamlessly in the edge as well. The goal of the project is to develop AI algorithms that work well in edge applications.
Principal Investigator: Arun Rajkumar
Sports Science and Analytics
This project establishes a Center of Excellence in Sports Science and Analytics at IIT Madras in collaboration with Northwestern University. The center envisions the application of physics-based and data-driven approaches to deliver deep insights into sports-related questions. This center would be the first of its kind at a university globally. This initiative is the result of eminent researchers with extensive credibility in sports analytics coming together internationally, spanning India, Europe and North America. The novelty and need for such a center has been recognized by our industry and government partners, who have willing expressed support. These include ESPN, the IPL team - Royal Challengers Bangalore (RCB), Tamil Nadu Olympic Association (TNOA) and Aspire Tennis Foundation.
Principal Investigator: Nandan Sudarsanam
Systems Biology and Medicine
Integrative Biology and Systems medicinE (IBSE) is a vibrant interdisciplinary centre engaged in pioneering innovative approaches and algorithms that integrate multidimensional biological data across scales to understand, predict and manipulate complex biological systems. IBSE has established itself as a centre for data analysis in biological & clinical sciences, which has led to it being a leading data science partner for some of India’s mega projects, viz. GenomeIndia, GARBH-Ini and INCENTIVE apart from major individual research grants. The central purpose of the CoE will be to promote and grow research in data-driven disease and clinical research at IITM.
Principal Investigator: Himanshu Sinha