Hi!
I’m Gunnika, a 2024 MS CS grad from UC San Diego. I’ve been exploring different aspects of AI, some of which include the intersection of Neuroscience and AI; Audio/ Music; Computer Vision; and Generative AI.
I previously worked as a Data Scientist - Technology Consulting at Ernst & Young. I’m a former research intern at the INMAS lab of Defence Research and Development Organisation (DRDO) under Dr. Sushil Chandra, where my work involved dynamically allocating the aircraft control by detecting the level of pilot’s cognitive workload. Physiological sensors like electroencephalogram (EEG) were employed for this purpose.
I am also a Founding Team Member at DPhi, a thoughtful initiative to build data culture and democratize Data Science learning. We regularly conducted bootcamps, datathons and released intuitive courses in this endeavor. I personally formulated 12 courses and piloted 4 AI bootcamps. We also empowered Software Development firms with Assessment PaaS & facilitated AI hiring by open-innovation challenges.
Apart from this, you will find me getting involved in communities, writing blogs, and participating in hackathons.
Masters of Science in Computer Science, 2022-2024
University of California, San Diego
B.Tech in Information Technology, 2017-2021
Guru Gobind Singh Indraprastha University
A Flask web application to predict flair (tag) of any post on India Subreddit using Machine Learning Algorithms.
A PyTorch implementation of Dog Breed Classification.
Dynamically allocating the aircraft control by detecting the level of pilot’s cognitive workload.
Multi-modal ML and DL architectures to forecast overnight sleep quality from the first 60 minutes of sleep data. We’re testing out various predictors of sleep quality such as duration, number of durations, and number of slow waves.
Summer internship project to identify the source of a user’s profile picture on the SHEROES platform.
Answering questions posed in natural language about the contents of an image - in the form of short text.
Recognition of hand gestures in 3D space using a single low resolution camera for converting American Sign Language into any spoken language.