Vision for PhD
AI for Science
My vision for a PhD revolves around advancing the integration of artificial intelligence with scientific discovery. By bridging mechanistic models and data-driven techniques, I aim to address critical scientific challenges across multiple domains. This interdisciplinary approach will enable the development of computational frameworks that can improve our understanding of complex systems. I aim to tackle problems which have an impact on sustainability and climate change, either directly or indirectly. One of the important areas I have looked at before is utilizing AI to revolutionize materials discovery and optimization. Traditional methods of materials development are often resource-intensive and time-consuming, requiring extensive experimentation and simulations. By integrating experimental datasets, simulations, and domain-specific insights, I have developed AI-driven algorithms capable of predicting material properties and suggesting optimal compositions for applications such as energy storage, catalysis, and semiconductor discovery. I would like to explore the space of automating processes like inverse design and fabrication optimization, aiming to bridge the gap between theoretical predictions and practical implementations. This research has the potential to significantly reduce the time-to-market for novel materials while constraining their impact on the environment and economy. Parallely, climate science presents numerous unresolved challenges, particularly in understanding the intricate interactions between oceanic and atmospheric processes. My research will focus on improving the representation of submesoscale and coastal dynamics in global climate models. These regions are critical for predicting climate variability and addressing the impacts of climate change, yet they remain poorly resolved in current models due to their complex multiscale nature. By leveraging high-resolution numerical simulations and state-of-the-art deep learning frameworks, I aim to develop parameterizations that can accurately capture these dynamics. This will involve studying phenomena such as turbulence, stratification, and their roles in vertical mixing and energy transfer. The outcomes of this research will contribute to more accurate climate forecasts and better mitigation strategies for climate-related risks.
Semiconductor Device Design
The semiconductor industry is at the forefront of technological innovation, and my vision includes harnessing AI to enhance its research and development processes. Semiconductor device design often involves complex trade-offs between performance, power efficiency, and manufacturability. Through the application of machine learning models, I aim to optimize device geometries and automate the inverse design process, leading to customized transistors and integrated circuits with enhanced properties. Additionally, my work will focus on extracting patterns from fabrication data to streamline manufacturing workflows and reduce variability. Beyond electronics, I see potential applications in medical diagnostics, where AI-enhanced sensors could enable real-time monitoring and personalized healthcare solutions. This line of research will bridge traditional device physics with cutting-edge computational tools, driving innovation in both technology and healthcare.
Environmental Science and Remote Sensing
Remote sensing technology provides a unique vantage point for monitoring environmental changes and understanding their underlying causes. However, challenges such as noise, artifacts, and domain shifts often limit the usability of satellite-derived data. My research will focus on developing AI frameworks to process and analyze remote sensing data with enhanced accuracy and robustness. These frameworks will be designed to quantify greenhouse gas emissions, identify their sources, and track their evolution across temporal and spatial scales. By addressing these challenges, I aim to deliver actionable insights that inform climate policy and support mitigation strategies. This research will play a crucial role in leveraging advanced computational methods to address global environmental challenges effectively.
Long-Term Vision
My ultimate goal is to establish myself as a leader in AI for Science, contributing to transformative solutions for global challenges such as climate change, sustainable energy, and technological innovation. A PhD will provide the training, resources, and collaborative opportunities necessary to achieve this vision. By engaging with diverse experts and leveraging interdisciplinary methodologies, I aim to create a lasting impact through research that bridges scientific understanding and practical applications. This journey will also enable me to mentor future researchers and foster a culture of innovation and curiosity in scientific exploration.