Nikil Nambiar

- Published work in Frontiers in Medicine: "AI-based Screening Tool for Early Diagnosis of Burkitt Lymphoma":

https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1345611/full

Volunteer at Burkitt’s Lymphoma Fund for Africa (BLFA)

Led the development of a machine learning-based application to accelerate early diagnosis of Burkitt’s Lymphoma, a prevalent childhood cancer in Uganda.

- Collaborated with medical professionals at PhenoPath and mentors from the University of Washington and Microsoft.

- Built ML based tool for analyzing biopsy/pathology images, delivering high-accuracy results at low cost.

- Prioritized cases for pathologist review, optimizing diagnostic processes and resource allocation.

Work Volunteer at Seattle Humane Society

Collaborated with Animal Pathway Coordinator to analyze adoption process data spanning several decades.

- Developed visualizations to identify key pet characteristics affecting adoption timelines (breed, age, color, gender, medical and behavioral history).

- Built a machine learning application to predict adoption times based on pet attributes, enabling the prioritization of care for pets with longer stay predictions.

- Analyzed data for thousands of pets to optimize re-homing strategies.

Golf Ball Collection Optimization Project - Microsoft Imagine Cup competition

Designed a machine learning model to detect high-density zones of golf balls on driving ranges, optimizing ball collection efficiency.

-Identified inefficiencies in current golf ball collection methods, which consume excessive energy, generate noise pollution, and increase CO2 emissions.

-Developed an AI-based solution to pinpoint high ball density areas, improving the accuracy of ball picking machines and reducing energy consumption, noise, and emissions.

-Researched the advantages of automatic ball pickers over traditional machines and proposed AI enhancements for both methods, driving cost savings and environmental benefits.