side Balaraman Ravindran

Balaraman Ravindran

Head, Robert Bosch Centre for Data Science and AI


Mindtree Faculty Fellow

Machine learning Reinforcement learning Social Network Analysis Data and text mining

Prof. Ravindran is the head of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) at IIT Madras and a professor in the Department of Computer Science and Engineering. He is also the co-director of the reconfigurable and intelligent systems engineering (RISE) group at IIT Madras, which has nearly 80 members associated with it currently. He received his PhD from the University of Massachusetts, Amherst He has nearly two decades of research experience in machine learning and specifically reinforcement learning. He has held visiting positions at the Indian Institute of Science, Bangalore, India and University of Technology, Sydney, Australia. Currently, his research interests are centred on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.He is one of the founding executive committee members of the India chapter of ACM SIGKDD and is currently serving as the president of the chapter. He has published nearly 100 papers in journals and conferences, including premier venues such as ICML, AAAI, IJCAI, ICDM, ICLR, NIPS, UAI, ISMB, and AAMAS. He has also co-authored the chapter on reinforcement learning in the Handbook of Neural Computation published by Oxford University Press. He has been on the program committees of several premier conferences as well as served as the program co-chair of PAKDD in 2010 and the General co-chair of the 2015 Big Data Summit at Sydney. He is currently serving on the editorial boards of PLOS One and Frontiers in Big Data. He has been closely collaborating with various industrial research labs, such as Ericsson Research and Development, KLA Tencor, Intel Labs, Applied Materials, Adobe Research, Bosch, IBM India Research Labs, Yahoo! Labs and General Motors, working on applications of data mining and machine learning techniques to hard real-world problems. He received Yahoo! Faculty research gifts in 2009 and 2014 to work on mining real-world text data and unrestricted research gifts from KLA Tencor in 2014, 2015 and 2017. He also serves on the advisory boards of several startups in the data analytics and AI space.


Jan 20, 2021
How COVID-19 impacts population movement: A data-driven analysis to study population behavior during a pandemic
Scientific evidence available on the transmission of SARS-CoV-2, the virus that has caused the global outbreak of COVID-19, shows that the disease spreads through droplets launched from an infected person via coughing, sneezing, talking that land on a healthy person in close proximity (less than 6 feet). Epidemiological researchers have found social distancing measures to be very effective in containing the spread of virus in the absence of a proven cure or vaccine.

5 min read

Jan 13, 2021
Predicting Essential Genes through Network Approach: Deciphering basis of Life
A classic challenge in biology is to study the function of proteins. Of various functions, essential functions are very interesting, as they map to important indispensable genes in an organism. Experimentally identifying these genes is rather expensive and challenging. Computational predictions can help point in the right direction, to prioritise experiments. To date, experimental data are available for <100 organisms! On the other hand, sequencing data are available for 1000s of organisms, as also interactome (networks of interactions) data.

4 min read

Sep 5, 2020
Finding Influencers in Social Networks: Reinforcement Learning Shows the Way
Social Network Analysis has given us many tools to effectively manage information dissemination in a social group, study growth and dynamics in such groups, etc. But one of the key challenges when studying social groups of underprivileged or socially marginalized groups is the recovery of the underlying social network itself. This study proposes a machine learning approach for learning to effectively allocate a limited budget to discover the network.

5 min read