Hi, I’m Kaivalya Dabhadkar! 👋
I’m passionate about the elegant interplay between Mathematics and Artificial Intelligence. My journey spans from pure mathematical theory to practical AI applications, always seeking to understand the deep connections between abstract concepts and real-world problems.
My Interests
🎓 Mathematics
- Linear Algebra & Matrix Theory: The foundation of modern machine learning
- Calculus & Analysis: Understanding optimization and continuous systems
- Probability & Statistics: The language of uncertainty and inference
🤖 Artificial Intelligence
- Deep Learning: Neural architectures and representation learning
- Reinforcement Learning: Decision-making and optimal control
- Generative Models: Generating data and understanding latent spaces
My Background
Currently, I’m a Senior Software Engineer at NVIDIA, where I help build fault-tolerant platforms for LLM/AI training and inference.
I have a deep fascination with how mathematical abstractions can be transformed into powerful computational tools. Whether it’s understanding the geometry of high-dimensional spaces in machine learning or neural network architectures, I believe that mathematics provides the key insights needed to advance AI.
Let’s Connect!
I love discussing ideas and collaborating on interesting problems. Feel free to reach out through any of the social links above if you’d like to chat about mathematics, AI, or anything in between!
Recent Reading
- An Introduction to Variational Autoencoders by Diederik P. Kingma and Max Welling
- “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto