Optimization Theory
Developing efficient algorithms for convex and non-convex optimization problems with applications in machine learning, operations research, and control systems.
Research Domain
Algorithms
Optimization, learning theory, and graph flows that compress complexity into elegance. We design algorithms that solve hard problems with mathematical precision.
Research Focus Areas
Graph Algorithms
Novel approaches to network flow problems, shortest paths, and graph partitioning that scale to massive real-world datasets.
Learning Theory
Theoretical foundations of machine learning, including generalization bounds, sample complexity, and convergence analysis of learning algorithms.
Approximation Algorithms
Designing algorithms with provable performance guarantees for computationally hard problems, balancing accuracy with efficiency.
Research Methodology
- Theoretical Rigor.Every algorithm comes with formal complexity analysis and correctness proofs.
- Practical Implementation.Theory meets practice through carefully optimized, production-ready code.
- Empirical Validation.Extensive benchmarking on real-world datasets to validate theoretical predictions.
- Open Research.Publishing findings, sharing implementations, and contributing to the broader community.
Recent Breakthroughs
Adaptive Gradient Method
A novel optimization algorithm that adapts to problem structure, achieving faster convergence on non-convex landscapes.
Read paper →Distributed Graph Processing
Scalable framework for processing billion-edge graphs with near-linear time complexity.
Read paper →Robust Learning Framework
Algorithms that maintain performance under distribution shift and adversarial perturbations.
Read paper →Academic Partnerships
Collaborate on research
We partner with leading universities and research institutions worldwide on fundamental algorithmic research.