Mar 14, 2021
Meet Preksha Nema
Curiosity, questioning and understanding sentiments have long been considered human attributes. Preksha Nema, a young scientist’s research is all set to change this! Through her research in natural language processing or popularly called NLP, she is trying to shatter the human-machine boundary by giving computers the magical power to question. Born in Jabalpur city located in the heart of India, Madhya Pradesh, Preksha was fascinated by puzzles and computers from early on.

2 min read

Feb 10, 2021
Levelling up NLP for Indian Languages
Divyanshu Kakwani , Anoop Kunchukuttan , Satish Golla , Gokul N.C , Avik Bhattacharyya , Mitesh M Khapra , Pratyush Kumar
We are working towards building a better ecosystem for Indian languages while also keeping up with the recent advancements in NLP. To this end, we are releasing IndicNLPSuite, which is a collection of various resources and models for Indian languages

6 min read

Feb 9, 2021
Developing India-specific pregnancy dating model from Garbhini cohort
Ashley Xavier , Himanshu Sinha , Nikhita Damaraju
The duration of gestation of pregnancy is the period between the date of conception and date of delivery, which is about 40 weeks of gestation. Estimating gestational age is crucial for accurate prediction of the date of delivery and classifying it as term or preterm, a birth occurring before 37 weeks of gestation. Preterm birth is one of the leading causes of complications for the newborn that can lead to its death.

3 min read

Jan 28, 2021
Meet Dr. Gitakrishnan Ramadurai
Why is everyone heading in the same direction? Is there an accident ahead? When will I reach home? Why can’t cars fly? These are some of the thoughts that cross our minds while stuck in a traffic jam. As time slowly glides, we are nothing but full of frustration. For Dr. Gitakrishnan Ramadurai, however, this is an exciting opportunity! An opportunity to understand the complex, heterogeneous nature of Indian traffic and thinking of the ways artificial intelligence can solve the traffic congestion issues of India.

2 min read

Jan 21, 2021
Fireside chat: with RBCDSAI Distinguished Fellow Prof. Srinivasan Parthasarathy
Ethics in AI is one of the most discussed topics in recent times. Read about the fireside chat with Prof. Srinivasan Parthasarathy where he spoke about AI, Data Science, and Ethics. Get to know the various challenges and solutions embraced by different service providers to keep their clients safe and happy.

3 min read

Jan 20, 2021
Data Science and IoT for addressing ambient air sanctity
Sathish Swaminathan , Raghunathan Rengaswamy
Ambient air quality is a dynamic parameter that shows a high degree of spatiotemporal variation. It is known to impact human life and health. Obtaining meaningful estimates of human exposure to air pollutants is key to mitigating its ill effects. Current air quality monitoring regime consists of expensive, static monitoring stations that are sparsely distributed across a city which in turn results in poor spatial resolution. This particular work, accomplished under project Kaatru, demonstrates the efficacy of low-cost sensor based, vehicle mounted, mobile monitoring paradigm in hyperlocal air quality assessment.

7 min read

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
Ablation-CAM: Making AI trustworthy
Saurabh Desai , Harish G. Ramaswamy
As machine learning is set to change every aspect of our life, a key dilemma plagues the minds of researchers and its users- Can we trust machines to make key life decisions for us? Can we rely solely on the machine to drive us safely to the destination without knowing the basis of its function or shall we allow a machine to operate us instead of a doctor?

4 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

Nov 17, 2020
Deep learning in biomedical image analysis
Medical imaging, specifically radiologic imaging is the most commonly used diagnostic tool for disease diagnosis and treatment assessment for a wide variety of conditions. Over the last decades the image acquisition hardware has improved significantly and corresponding image reconstruction software has become more sophisticated. These provide increasingly complex data both in terms of size and content, making it a challenging task for radiologists to sift through and arrive at meaningful diagnosis and therapeutic assessment. The role of AI/ML techniques in this context is to act as a radiologist’s assistant to automate routine tasks and provide preliminary diagnosis. A radiologist can then use the outputs from these systems to speed up and improve accuracy of diagnosis.

3 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