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Our long-term vision is to develop cross-paradigm techniques for designing algorithms that work across many models of computation, such as dynamic, distributed, streaming, parallel, and quantum algorithms. Our philosophy is to simultaneously study the same question from the perspectives of many computing paradigms to find new insights that may not emerge from the isolated viewpoint of a single model. We aim to achieve two goals simultaneously: (i) solutions for notorious long-standing open problems and (ii) efficient algorithms that can fully exploit the characteristics of modern computing devices and data.
Our current focus is on basic questions about graph data, such as connectivity, shortest paths, and matching. The aim is to develop provably fast algorithms and complexity theory, using tools such as sketching, spectral techniques, fast matrix multiplication, approximation algorithms, communication complexity and fine-grained complexity.
Although we focus on long-standing open problems and mathematical proofs, we are open to new problems arising from applications and to collaborations with experimentalists.
Keywords: Graph algorithms, Modern computational models, Distributed algorithms, Dynamic algorithms, Approximation algorithms, Sublinear algorithms, Lower bounds, Fine-grained Complexity, Hardness of approximation.
Our long-term vision is to develop cross-paradigm techniques for designing algorithms that work across many models of computation, such as dynamic, distributed, streaming, parallel, and quantum algorithms. Our philosophy is to simultaneously study the same question from the perspectives of many computing paradigms to find new insights that may not emerge from the isolated viewpoint of a single model. We aim to achieve two goals simultaneously: (i) solutions for notorious long-standing open problems and (ii) efficient algorithms that can fully exploit the characteristics of modern computing devices and data.
Our current focus is on basic questions about graph data, such as connectivity, shortest paths, and matching. The aim is to develop provably fast algorithms and complexity theory, using tools such as sketching, spectral techniques, fast matrix multiplication, approximation algorithms, communication complexity and fine-grained complexity.
Although we focus on long-standing open problems and mathematical proofs, we are open to new problems arising from applications and to collaborations with experimentalists.
Keywords: Graph algorithms, Modern computational models, Distributed algorithms, Dynamic algorithms, Approximation algorithms, Sublinear algorithms, Lower bounds, Fine-grained Complexity, Hardness of approximation.
研究兴趣
论文共 111 篇作者统计合作学者相似作者
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International Colloquium on Automata, Languages and Programmingpp.3:1-3:1, (2024)
SIAM Journal on Computingno. 5 (2023): 1132-1192
arXiv (Cornell University) (2023)
Distributed Computingno. 2 (2022): 165-183
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作者统计
#Papers: 110
#Citation: 3962
H-Index: 34
G-Index: 61
Sociability: 5
Diversity: 2
Activity: 57
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