Work with thought leaders and academic experts from Indian Institute of Technology Ropar

• An experienced researcher proficient in renewable energy R&D, focusing on nanomaterials, Electrochemistry, Solar, Hydrogen production, Fuel cell, Energy storage & Conversion. • Possesses expertise in various synthesis methods, alongside adeptness in laboratory testing of materials, utilizing electrochemical workstation techniques and conducting fuel cell testing. • Demonstrated success in driving forward research objectives within the realm of cleaner energy and environmental sustainability. • Skilled leader known for project management, mentorship, and promoting innovation

Currently, I am a Postdoctoral Research Associate at the Indian Institute of Technology (IIT) Ropar, focusing my research on advanced machine learning techniques applied to non-linear time series data, particularly in the context of healthcare predictions. I applied ML and DNN models to analyze non-linear time series data from cardiac surgical patients, predicting outcomes such as ICU stay, hospital stay, acute kidney injury, and survival. Additionally, I developed an AI-based forecasting model for real-time monitoring, integrating adaptive learning to address concept drift. As a researcher with a passion for machine learning and a strong background in artificial intelligence, deep learning, and data analysis, I have over 3 years of experience creating and applying innovative deep-learning techniques for time series problems. I also have a record of producing high-quality research papers published in well-respected journals and conferences such as IEEE Sensors, Big Data Journal, Wireless Network, and IET Communication. With a Google Scholar citation count of 400, I am well-versed in contributing to the academic community. In addition, my commitment to staying updated with emerging technologies is reflected in my certifications and awards, including the NVIDIA, Upwork Rising Talent certificate for outstanding work in 2023.