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Hello, I'm Kushal Borkar.

I am an MS by Research Student at IIIT Hyderabad.
"The more I learn, the more I learn how little I
know" - and that certainly makes me more curious
to learn more. Nice to meet you!

About Me

Allow me to introduce myself.

My name is Kushal Borkar. I am pursuing my Masters' by Research (MS) in Computer Vision at HAI, IIIT Hyderabad under the guidance of Prof. C V Jawahar (IIIT Hyderabad). Prior to this, I obtained my bachelor’s degrees in Electronics and Communication Engineering from IIIT, SriCity, Chittoor.
My primary research interest lies in the space of Computer Vision and AI, especially in understanding the interaction between vision, language, and applied Healthcare in AI.
I am actively looking for intuitive opportunities to work on challenging real-world problems where I can apply my acquired knowledge and learn, construct, develop, and evolve.
Every so often, while considered not occupied with academics or work, I set aside out some time for reading a novel or a blog post, watching anime, tuning in to podcasts, playing Evony or sharing moments with my family.
I am still figuring out what I am doing with my life. But hey, nice to meet you!

Education

International Institute of Information Technology, Hyderabad, India
January 2021 - Present
Master of Science (MS) by Research
Indian Institute of Information Technology, SriCity, Chittoor, India
August 2015 - May 2019
Bachelor of Technology(Hons.)

Experience

International Institute of Information Technology, Hyderabad, India
Research Fellow
July 2020 - June 2021

Research work involves developing new AI based solutions in Healthcare domain as part of the Intel Applied Center (INAI) initiative.

Varidus, Singapore
Data Science & Data Analyst Intern
January 2020 - July 2020

Designed and improved the accessibility of multi-sourced data for AR-VR product by automating the data scraping process from 5 various data sources & built the regression model.

Masho, Bangalore, India
Data Science Intern
September 2019 - December 2019

Designed and implemented the content-based filtering algorithm to generate personalised product recommendations by utilizing word2vec to analyze product title and various similarity metrics to form a hybrid model.

News

Latest News

2021

  • Selected for Master of Science by Research in Computer Science and Engineering under the guidance of Prof. C V Jawahar.

2020

  • Accepted as an research fellow under the Applied AI in Healthcare(HAI) at IIIT Hyderabad.

  • Interned at Varidus in Spring, 2020.

  • Paper on Image Dehazing by approximating and eliminating the additional airlight component accepted to Neurocomputing, 2020.

2018

  • Paper on Video Dehazing using temporal and spatial coherence of the hazy video accepted to ICVGIP, 2018.

Publications

Recent Research Papers

2023
PREHOST: Host prediction of coronaviridae family using machine learning

Anusha Chaturvedi, Kushal Borkar, U Deva Priyakumar, Vinod P.K.

2022
Ayu - Characterization of Healthy Ageing from Neuroimaging Data with Deep Learning and rsfMRI

Kushal Borkar, Anusha Chaturvedi, Vinod P.K., Raju Surampudi Bapi.

2020
Single image dehazing by approximating and eliminating the additional airlight component

Kushal Borkar and Snehasis Mukherjee, Neurocomputing, 2020.

2018
Video Dehazing Using LMNN with Respect to Augmented MRF

Kushal Borkar and Snehasis Mukherjee, Proc. of ICVGIP 2018, IIIT Hyderabad, ACM, pp. 42:1-42:9.

Single Image Dehazing Based on Generic Regularity

Kushal Borkar and Snehasis Mukherjee, arXiv:1808.08610, 2018

Portfolio

A selection of recent projects I have worked on.

Interactive Machine Learning

Current machine learning based systems mostly work in inference mode, which implies that one gives an input to the system, and gets an output. Thereafter, the only choice is to accept or reject the output. We have developed a new algorithm, which allows “human in the loop” design. Here an ML system produces an output (possibility highly erroneous), a doctor sees it and provide quick and limited corrections. The ML model takes the feedback into account, retrains itself in real time, and produces a new output. The doctor sees the new output and corrects again. The iterations continue until a doctor is mostly satisfied with the output.

Ayu - Characterization of Healthy Ageing from Neuroimaging Data with Deep Learning and rsfMRI

In this work, we propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection and an attention-based model for deep learning architecture for brain age assessment on a dataset (N = 638, age-range 20-88) comprising rsfMRI images from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository of a healthy population.

Covid Cough Detector

In this work, I worked on a Covid-19 Cough Detector using MFCCs and Chroma based features from 10 seconds cough audio recording of the person and applying a CNN based Architecture to classify COVID-19 positive patient or not.
This work was done on IISc ”Co-Swara” Dataset.