Radhika Dua

Hi, I am a Predoctoral Researcher at Google Research India, where I am working under the guidance of Dr. Gaurav Aggarwal. Before joining Google, I was a Master's student in Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where I was fortunate to have Prof. Edward Choi as my advisor. My research interest lies at the intersection of Computer Vision, Machine Learning, Healthcare, and NLP. I currently work on enhancing the reliability of deep neural networks against natural and synthetic distribution shifts.

Prior to my time at KAIST, I was a Summer Intern at Brown University, where I was fortunate to be advised by Prof. Srinath Sridhar. I also spent some time as a Visiting Researcher at IIT Hyderabad, where I was blessed to be supervised by Prof. Vineeth N Balasubramanian.

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We define a new task, natural attributebased shift (NAS) detection, to detect the samples shifted from the training distribution by some natural attribute such as age of subjects or brightness of images. We also introduce benchmark datasets in vision, language, and medical for NAS detection.

We introduce a new task: ViQAR (Visual Question Answering and Reasoning), wherein a model must generate the complete answer and a rationale that seeks to justify the generated answer.

We present VayuAnukulani system, a novel end-to-end solution to forecast fine-grained ambient air quality information based on the historical and realtime ambient air quality and meteorological data.

Conducting research in 3D computer vision and machine learning.

Conducting research in Vision and Language applications and introduced a new task, ViQAR: Visual on Answering and Reasoning, which focuses on automatic generation of the answer, and of a rationale, given a visual query.

Developed a temporal forecasting solution based on the historical data reported by Central Pollution Control board to predict the real-time and fine-grained air quality information in five locations of Delhi.

We introduced a new task, ViQAR, in which the model generates the complete answer and rationale. We also proposed an end-to-end, attention-based encoder-decoder architecture to solve this task, and showed that our model generates strong answers and rationales through qualitative and quantitative evaluation, as well as human Turing Test.

As a part of the Celestini Project, sponsored by Google and Marconi Society and mentored by Dr. Aakanksha Chowdhery, we developed Clair: Air pollution forecasting in Delhi, a temporal forecasting solution based on the historical data reported by Central Pollution Control board to predict the real-time and fine-grained air quality information in five locations of Delhi. We were awarded Second Prize for our novel solution and also had the opportunity of presenting our work at GlobalSIP 2019.
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