NeurIPS 2024202445
My research focuses on developing efficient optimization algorithms and machine learning methods that scale to real-world problems. I'm particularly interested in the intersection of theoretical foundations and practical applications.
Exploring the intersection of theory and practice across multiple domains
Selected papers highlighting key contributions to the field
Browse and search through the complete list of research publications
Research implementations and tools available on GitHub
PyTorch implementations of adaptive learning rate optimizers from our research papers
Efficient transformer architectures for time series forecasting
Reproducible experiments for neural network scaling laws
Production-ready ML pipeline template with CI/CD, monitoring, and deployment
Find my work across academic platforms and databases
I'm always open to discussing research ideas, potential collaborations, or speaking opportunities. Whether you're working on related problems or have interesting applications, let's connect.
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