Data Scientist Software Engineer Postgraduate Researcher
I am Shivani Khandelwal, a Data Scientist currently pursuing a Master's by Research in Computational Science and Artificial Intelligence at Coventry University, UK. I am focused on using AI to solve real-world problems, especially related to climate change. My research focuses on using Reinforcement Learning as a control method to improve the efficiency of smart heating and cooling systems, reducing energy consumption while maintaining thermal comfort. Additionally, it involves creating a data-driven simulation environment for buildings that is computationally faster and adaptable to various house types and designs. My journey started with a bachelor's degree in computer science from ITM University, Gwalior, India and a dream of studying master's in AI abroad. Starting from scratch, I am proud to have earned the prestigious fully-funded British Council Women in STEM Scholarship for my master's in the UK, which has helped me advance my knowledge and skills in this field. Throughout my journey, I gained valuable experience working in various industries and collaborating on multiple projects. I love creating and sharing content on technical topics, personal experiences, and career journeys on social media. I also enjoy engaging with new people through networking events. Feel free to connect with me for any collaborations or discussions on how AI can improve lives and address global challenges.
+44 7733586879
shivanikhandelwal487@gmail.com
Research Focus: Curbing Poverty through Sustainable Energy.
Director: Prof. James Brusey
Relevant Subjects: Python, Data Science, Machine Learning.
Libraries: Numpy, Pandas, Scikit-learn
Techniques: Regression, Classification, Clustering
ANN, CNN, RNN, LSTM
Frameworks: TensorFlow, PyTorch
Languages: HTML, CSS, JavaScript
Frameworks: Flask, Django
AWS, Google Cloud, Azure
Git, GitHub, GitLab
This project aims to build an effective control system for smart heating and cooling systems using Reinforcement Learning to reduce energy consumption and cost.
This system predicts user preferences and recommends songs using various machine-learning techniques, including collaborative filtering and content-based filtering.
Implemented various operations such as age and gender classification, face blurring, makeup, and facial emotion detection on images and videos.
Developed a prototype for a human-device interaction project, creating an AI-based security layer for mobile devices using unique user handling patterns.
This hardware-based project integrates Distance Sensing, RFID, and IR Remote features, along with emergency unlock functionality for added safety.
Designed an image caption generator using an encoder-decoder architecture (CNN and RNN).
Conducted exploratory data analysis on the PUBG dataset with hypothesis testing.
Created a system to summarize videos based on human activity using a Deep Learning model and OpenCV.