Feb 2024 – Feb 2024 · 0 mo · On-site

• Selected to participate in Google Research’s exclusive 3-day AI summit (Feb 1-3), where I engaged in insightful workshops, explored cutting-edge advancements in AI, and gained deeper insights into Google’s work in healthcare and large language models (LLMs) through collaboration with top professionals and researchers.

May 2023 – Jul 2023 · 2 mos · Grenoble, Auvergne-Rhône-Alpes, France · On-site

• I completed an enriching internship at Team Morpheo, INRIA Grenoble, where I worked on an exciting project focused on predicting the General Movement Assessment (GMA) rating of infant motion video sequences. The goal of this project was to leverage SMIL Body pose parameters to develop a predictive model that aids in evaluating the neurological development of infants.
• During my internship, I was responsible for data preprocessing and extracting relevant features from the SMIL Body pose parameters. I implemented and fine-tuned various machine learning algorithms and deep learning architectures, using state-of-the-art techniques in computer vision and natural language processing to capture the temporal dynamics of infant motion sequences effectively.
• Collaborating with experienced researchers and professionals at Team Morpheo was incredibly rewarding. I had the opportunity to exchange ideas with experts in computer vision and machine learning, whose mentorship greatly contributed to my growth as a researcher.
• This internship was a perfect alignment with my passion for computer vision, machine learning, and their applications in healthcare. By contributing to the development of a robust prediction model, I aimed to make a tangible impact in assessing neurological development in infants, potentially aiding in early diagnosis and intervention.

Dec 2022 – Feb 2023 · 2 mos · Changwon, South Gyeongsang, South Korea · Remote

• During my internship at CNU (Changwon National University) in South Korea from December 2022 to February 2023, I had the opportunity to work on an exciting project focused on 3D face modelling and eXplainable Artificial Intelligence (XAI). My main contributions involved implementing the innovative NextFace Model for accurate 3D face reconstruction and conducting a survey of research papers related to XAI.
• One of the key aspects of my internship was the implementation of the NextFace Model. This state-of-the-art model utilizes advanced computer vision techniques to reconstruct highly detailed 3D models of human faces from 2D images. I was responsible for implementing NextFace Model using appropriate deep-learning frameworks.
• Additionally, I conducted a comprehensive survey of research papers focusing on eXplainable Artificial Intelligence (XAI). Through the survey, I explored various XAI techniques, methodologies, and frameworks proposed by researchers in the field. I analyzed and summarized the key findings, strengths, and limitations of these papers, gaining valuable insights into the current state of XAI research and its potential applications.
• By working on the NextFace Model and conducting the survey on XAI, I was able to deepen my technical skills in computer vision, deep learning, and research methodology. I gained hands-on experience with implementing complex models and analyzing academic literature, enhancing my ability to tackle challenging problems and contribute to cutting-edge research.
• This experience has fueled my passion for computer vision, 3D face modelling, and XAI. I am excited to continue pursuing research and innovation in these fields, as I believe they hold immense potential for revolutionizing various industries and improving human-computer interaction.
• Gagan