Pratik Katte

[Github] [Website]

Lorax is a tool for interactive exploration of biobank-scale Ancestral Recombination Graphs (ARGs). It lets users scroll across the genome and watch the local genealogy change as recombination reshuffles ancestry. Alongside the trees, Lorax links what you’re seeing to coalescent time (branch length), variants, and sample metadata such as population, cohort, and custom labels. The goal is to make genetic ancestry more intuitive and help researchers quickly spot hidden patterns that are hard to see in static plots.

[Github] [Website]

WEPP (Wastewater-Based Epidemiology using Phylogenetic Placements) is a tool for analyzing wastewater sequencing data for many different pathogens. It uses a large evolutionary “family tree” of the pathogen, built from lots of clinical genomes, to figure out which variants (haplotypes) are likely present in a wastewater sample and about how much of each one is there. WEPP places sequencing reads onto this tree, narrows down a set of likely haplotypes, and then estimates their proportions (and the proportions of broader lineages). It also keeps the results easy to understand by linking reads back to the haplotypes they support. One especially useful feature is “unaccounted alleles”: mutations that show up in the wastewater data but don’t match the haplotypes WEPP inferred, which can be an early hint of a new or emerging variant. On top of that, WEPP includes an interactive dashboard so you can explore the tree, the haplotypes, and the read-level evidence behind the results.

StructHunt won the first prize in the QBI Hackathon orgaizied at University of California San Fransisco.

We created a tool designed to track the publication of new research papers detailing integrative biomolecular structures in bioRxiv and medRxiv. This tool enables us to swiftly capture and incorporate this valuable new data into the RCSB Protein Data Bank. Within a remarkably short timeframe of 36 hours, our team swiftly gained insights into integrative biomolecular structures, various LLM techniques (special thanks to Lantern for their assistance in optimizing storage and retrieval of embeddings), and successfully launched a fully operational prototype. This prototype has been seamlessly integrated with GoogleDocs and email notifications, hosted in the AWS cloud environment.

XraySetu is a joint collaboration between Niramai Health Analytix, Indian Institute of Science and ARTPARK.

Covid-19 delta variant had a disastrous imapct on not only matropolitan cities but also even in rural parts of India. RT-PCR test which was heavily relied upon to diagnose covid-19 was resulting in misdiagnosis for the delta variant of Covid-19. Therfore, doctors started using chest x-ray scans to diagnose covid-19. In India, there is less than 1 radiologist for every million people. With the limited number of radiologists in the country, it was impossible for doctors in 2nd tier cities and rural areas to diagnose covid-19 using chest x-rays. Xraysetu played a huge role in allowing doctors to plan early intervention for their patients by simply taking a picture of their chest x-ray and sending it over via whatsapp.

The free Whatsapp based XraySetu service responds with a detailed report generated using our state of the art deep learning model.

The state of the art deep learning model is trained using multi-task learning on a combination of dataset from multiple sources which includes NIH and RSNA, etc. The model generates a report containing predictions for COVID-19 and 14 other lung abnormalities with interpretable semantic markings on chest x-ray. This helps doctors understand the severity of illness of their patients.

Xraysetu is widely covered in print and online media by NDTV, CNBC-TV18 News, Gadgets Now, Mint, Hindustan Times, Business Standard, Economic Times, ET Healthworld, KnockSense, Bangalore Mirror, Jagran, Zee News Hindi, and Prasar Bharti (video).

Niramai Fever Test project received research funding by CDC-Group.

The outbreak of covid-19 brought a tremendous impact on the livelihood on the population. Community screening is the most important and primary aspect to reduce the spread of the virus in the cummunity. In order to detect people with covid-19 symptoms, we at Niramai, developed an AI based solution integrated with a thermal camera to measure temperature of a person and also detect if the person is wearing mask or not. We trained a light weight deep learning using thermal images for the task of face detection and mask detection.

Niramai Fever Test has been deployed in more than 100 locations which includes railyway stations, corporate tech parks, banks, etc. It has screened more than 10 lakhs of people in 2 years scince the deployment.

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Niramai Thermal Capture Tool is a desktop app built for hospital technicians to capture high-precision thermal breast images and upload them directly to the Thermalytix platform. Because thermal imaging is extremely sensitive—small errors in positioning, timing, or protocol can reduce image quality—the tool acts like a built-in quality coach. We trained deep-learning models (paired with image-processing checks) to automatically spot common capture issues early—like inconsistent framing, suboptimal alignment, or protocol deviations—so technicians can correct them on the spot instead of discovering problems later. The result is much more consistent, accurate, and protocol-compliant image capture across technicians and sites, which improves the reliability of downstream AI screening and supports stronger early breast cancer detection.