About
Hi, I’m Deepa Tilwani; thanks for visiting my website!
As a doctoral student at the University of South Carolina's Artificial Intelligence Institute within the Department of Computer Science, my research focus lies at the dynamic intersection of Artificial Intelligence (AI) and Health Care. This convergence represents a promising frontier with profound implications across various domains, particularly in understanding complex cognitive processes and developing advanced technologies for neuroscience applications. At the core of my research agenda is the exploration of how AI techniques can be leveraged to enhance our understanding of brain function and behavior. One of the primary avenues of my inquiry involves investigating the dynamics of EEG data in individuals with autism spectrum disorder (ASD). Through the application of advanced Dynamical Causal Modeling techniques, I aim to unravel the intricate neural mechanisms underlying ASD, shedding light on the factors contributing to its etiology and symptomatology. In tandem with my investigation into EEG dynamics, I am also deeply engaged in the emerging field of Neurosymbolic AI. This interdisciplinary approach seeks to bridge the gap between symbolic reasoning and neural networks, thereby enabling AI systems to exhibit more human-like cognitive abilities, such as abstract reasoning and knowledge representation. By integrating symbolic knowledge with the statistical power of neural networks, I aspire to develop AI models that not only excel at pattern recognition tasks but also possess the capacity for logical inference and conceptual understanding—a crucial step towards building truly intelligent machines. By embracing the interdisciplinary nature of my research, I seek to contribute meaningfully to both the fields of AI and Neuroscience, ultimately striving to make a positive impact on society's well-being.
Research Interests
- Dynamical Casual Modelling
- Neurosymbolic AI
- Knowledge Infuse Learning
- Neruroscience Analysis